legacy effects of extreme flood events on soil quality and

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Legacy Effects of Extreme Flood Events on Soil Quality and Ecosystem Functioning (Project LM0316). Prof Dave Chadwick, Prof Davey Jones, Dr. Rachel Kingham, Dr. Antonio Rodriguez, Dr. Paul Cross and Dr.

Helen Taft. Environment Centre Wales, Bangor University, Bangor, LL57 2UW.

Executive Summary

This study explored the effects of prolonged flooding on soil microbial, chemical and physical properties

through regular sampling of fields on five commercial farms for 9 months following the 2013/2014 winter

floods, and through controlled laboratory experiments. The effect of using equipment to alleviate

compaction and poor soil condition of flooded soils was also determined on two commercial farms. Finally,

a survey was conducted with the aim of determining farmers’ perceptions of the relative threat of flooding

to their businesses compared with other potential threats, and to assess what adaptation strategies have

been used on farms that have been flooded, and what management strategies have been tried to alleviate

flood affected soils. The online survey had to be adapted to a face-to-face survey at a large agricultural

show, due to farmer survey fatigue in the flooded area.

Monthly sampling of flooded fields on the commercial farms provided evidence that: earthworm numbers

were reduced by flooding but number recovered within 3-9 months of the flood water receding; the

electrical conductivity of soil remained higher in flooded fields for the majority of the monitoring period;

agricultural crop biomass was severely affected by flooding, with crops such as swedes and spring onions

being completely destroyed, resulting in farmers re-cultivating and planting new crops. During the sampling

visits we noted that recently sown pastures were not as flood resilient as permanent grassland swards.

The laboratory studies demonstrated that during flooding organic N is mineralised to ammonium which

accumulates in the soil until flood water recedes. However, in one experiment we measured increased

ammonia emissions during the flood period under warmer temperatures. Soil nitrate concentrations

reduce during the flooding period. However, once the flood water has receded then ammonium is nitrified

to nitrate, and nitrous oxide is emitted. In contrast, methane is emitted from flooded soil when organic

matter is present particularly under warmer temperatures, but following flood water removal the soil

becomes more aerated and methane emissions cease, as greater CO2 fluxes are observed. These findings

suggest that flooding is likely to increase the emissions of greenhouse gases and ammonia from agricultural

land, but this needs to be verified in the field, e.g. via exploration of atmospheric concentration data for

GHG emissions and ammonia from tall towers during recent flood events.

There was no consistent effect of slot seeding or use of a sward lifter or soil aerator on soil properties or

pasture yields on the commercial farm large-plot experiments.

The farmer survey resulted in 50 completed questionnaires. Respondents considered flooding to pose the

lowest threat to farm businesses compared to other threats, e.g. severe frost and snow, and a reduction in

farm subsidies (which also scored high in terms of a threat that farmers felt they had least control over).

However, those farmers who had experienced extreme flooding gave significantly higher scores for the

degree of threat that flooding poses to their businesses, and significantly lower scores for degree of

control.

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1. Introduction The UK has experienced amounts of flooding which are unprecedented in recent times. This flooding is arising particularly in the winter months due to exceptional amounts of winter rainfall in parts of England and Wales. For example in 2013/2014, amounts of rainfall were measured, which had not been recorded for over 248 years (MetOffice, 2014). It is estimated that the 2013/2014 flooding cost insurance companies in excess of £1 billion (RIBA, 2014). Other adverse impacts include loss of water quality and biodiversity, whilst emissions of nitrous oxide (N2O) and methane (CH4) may also be increased from flooded soils. The presence of long-term floodwaters is evident in some areas, such as The Somerset Levels where 65 million cubic metres of floodwater lay on farmland during the 2013/2014 floods. Further, The Environment Agency indicated that groundwater flow meant that some areas such as the Somerset Levels remained susceptible to flooding well into the Spring of 2014. With predictions of increased frequency of extreme weather as a result of Climate Change, events such as flooding will increase in the future. Indeed, the recent flooding caused by Storms ‘Desmond’ and ‘Frank’, are already thought to have resulted in a cost of >£20M to flood affected farmers in Cumbria (http://www.fwi.co.uk/news/cumbrian-farm-flood-claims-to-cost-at-least-20m.htm). There is a need for Policy to, i) understand and map risk, and ii) influence a shift in farm practices to promote adaptation to flooding by farmers. Whilst short term flooding (<2-4 weeks) has little long-term consequence in terms of soil quality, the continued inundation of agricultural land for many months is likely to have profound effects on ecosystem functioning, particularly for the soils of agricultural grasslands and croplands where the potential to maintain agricultural production may be significantly compromised. Anecdotally, there is an assumption within the farming community that if soil and crops are inundated for >21-28 days (‘ the 28-day rule’) then many crops will not survive. This is probably the result of anaerobic conditions that quickly prevail both in the soil profile and the overlying water column, while light may also be eliminated by the turbid floodwaters. This will induce the release of soluble carbon and N into the rhizosphere (Jones et al., 2009) ultimately eliminating the vegetation component of the ecosystem. Similarly, we can hypothesise that it will also eradicate the meso- and macro-faunal community (including earthworms) as well as inducing very large shifts in both microbial activity, community structure and functioning, with subsequent impacts on birds which rely on these food sources. Other negative aspects can include changes in soil physical structure (due to loss of aggregate stability and deposition of suspended particles) with subsequent effects on reduced infiltration (thus exacerbating the risk of future flooding if left unchecked). Prolonged flooding also results in physical erosion from floodwater and meso- and macro-faunal/root death) as well as the introduction of potentially pathogenic organisms from sewage contamination of floodwaters (SSDC, 2014). Lastly, in terms of chemical shifts, long-term inundation is likely to induce major shifts in soil nutrient cycling through the anaerobic turnover of dead plant material and components of the microbial community. Based on the lack of oxygen (O2), this is likely to lead to the accumulation of fermentation products (fatty acid, lactic/acetic acid, alcohols etc), changes in pH, loss of NO3

- (via denitrification and leaching), Mn2+/Fe2+ and NH4

+ in soil and the production of N2O/N2 and CH4. Although there has been work in this research area, previous studies have either focused on (1) the flood event itself, (2) an analysis of mitigation options to reduce flood risk, (3) reliance on remote sensed data to assess impact (i.e. vegetation only), (4) short-term laboratory studies using disturbed soils, no plants and unrepresentative conditions, (5) riparian areas that are flooded annually (i.e. non-extreme flooding). In addition, none of the studies have evaluated recovery rates and tipping points (see for example recent reviews by Eisenbies et al., 2007; Bodelier et al., 2011; Hapuarachchi et al., 2011; Brun and Baros, 2013; Natuhara, 2013; Shaw et al., 2013), which highlight the knowledge gaps and deficiencies of previous studies). We therefore designed this project to address a range of unique elements including: (1) carrying out some of the study under actual fields which have been recently flooded, (2) investigation at more than one site with contrasting soil types, (3) assessment of impact both during the flood event and its subsequent recovery, (4) calibration of the observed field results with additional laboratory studies, and (5) assessment of long-term flooding on a wide range of soil and water quality parameters directly linked to assessing soil natural capital and key ecosystem services (e.g. Provisioning services, Food and freshwater;

http://www.fwi.co.uk/news/cumbrian-farm-flood-claims-to-cost-at-least-20m.htm

http://www.fwi.co.uk/news/cumbrian-farm-flood-claims-to-cost-at-least-20m.htm

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Supporting Services, Physical stability and support for plants, Renewal, retention and delivery of nutrients for plants, Habitat and gene pool; Regulating services, Regulation of major elemental cycles, Buffering, filtering and moderation of the hydrological cycle, Disposal of wastes and dead organic matter). Without intervention, it is likely that some changes in soil quality induced by long term flooding may be irreversible for years afterward, whilst other properties may show a high degree of resilience. Until now, the magnitude of any hysteresis effects remains unquantified. Knowledge of soil ecosystem responses both during and following long-term flood events, is therefore required to make critically informed judgments on future land management activities (e.g. tillage, soil aeration, application of fertilisers, organic wastes, lime, and the timing such activities), potential risks to human health (survival of sewage and livestock derived viruses and bacteria), spread/loss of plant disease (e.g. nematodes), changes in net C sequestration and greenhouse emissions, plant yield and quality (food security), loss of biodiversity, risks for future surface runoff and freshwater pollution, and potential remediation/restoration activities (e.g. microbial inoculation via manures, meso- and macro-fauna inoculation, sowing of more flood tolerant varieties) etc. Whilst the recent flooding of the 2013/2104 winter was severe, affecting greater areas of land than ever before, and for longer periods, many farmers with fields in flood plains have acquired knowledge and experience in managing flooded land, and have perhaps adapted cropping and soil management in these areas. This knowledge should complement scientific understanding and contribute to the development of adaptation strategies for the wider farming community now being affected by flooding. The results from this project will assist future prioritization of land areas where government funding programmes could be best spent, through a greater understanding of which soil types and land management practices are most vulnerable, and what amelioration strategies (pre- and post-flooding) are most effective.

2. Aims and Objectives The aims of this project were to:

i) provide information on the negative effects of recent flood events on soil properties and quantify rates of recovery and any potentially irreversible effects (field sampling)

ii) identify potential tipping points in terms of inundation time (laboratory experiments) iii) identify potential intervention measures to promote recovery through pre- and post-flooding

management (literature review and survey) iv) record levels and methods of farmer adaptation to flood risk, and assess relative merits of

different strategies (survey) v) provide stakeholders (e.g. landowners, Defra, NFU, EA) with critical scientific information to be

able to make informed decisions (i.e. to minimize immediate impacts such as production losses) and respond and adapt to potential future flood events.

In order to achieve these aims, the project comprised the following activities;

sampling of flood affected soils across a range of farming systems in the Somerset Levels and Herefordshire during the recovery phase, post winter 2013/2014 flooding

quantifying the effects of prolonged flooding on soil properties in laboratory experiments simulating a range of scenarios (intact grassland, arable land with maize residues or manure application, different temperature and light regimes)

testing the effects of equipment to alleviate compaction and poor aeration status of flooded soils on commercial farms

a summary review of the literature on the impact of flooding on soil properties

a farmer survey; of perceptions of the relative threat of flooding to their businesses compared with other potential threats; to assess what adaptation strategies have been used on farms that are flooded; to assess what management strategies have been tried to alleviate flood affected soils.

The section below summarises the findings from each of these activities, in turn.

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3. Effects of flood events on soil quality 3.1 Field measurements of soil recovery post winter 2013/2014 flooding

Prolonged flooding is known to have serious impacts on soil chemical, biological and physical properties. Nutrients such as nitrogen and sulphate can leach out of the soil, and heavy metals can be deposited (Reddy and Rao, 1983; Burt and Arkell, 1987; Alloway, 1990). In anaerobic conditions, nitrogen mineralisation stops at the ammonium stage because of the lack of oxygen to carry the reaction through to nitrate. As a result ammonium builds up in flooded soils (Ponnamperuma, 1984; Unger et al., 2009). Soil pH usually rises immediately after flooding due to denitrification of nitrate to gaseous N, e.g. nitrous oxide (N2O). This depends on the amount of nitrates in the soil, but it can lead to a soil pH of 6.7 – 7.2 (Alloway, 1990). Also, soil nitrogen can be immobilised by both chemical and biological processes where there is a source of vegetative organic carbon; this can make it temporarily unavailable for plant uptake, even after the soil has begun to dry out as it will only be released over time as the microbes decompose (Ponnamperuma, 1984). Furthermore, several products are produced under waterlogged or anaerobic conditions, such as hydrogen sulphide, acetic acid and butyric acid, which can be toxic to plants for several weeks after flooding (Ponnamperuma, 1972; Koch and Mendelssohn, 1989; Lynch, 2006). As such, prolonged waterlogging can have lasting impacts on plant yield. Macrofaunal communities can all but disappear during prolonged flooding due to the lack of oxygen (Satchell, 1980; Plum 2005), and microbial communities change from a diverse aerobic assemblage to a much less diverse and less active anaerobic community that produces methane (CH4) rather than carbon dioxide (CO2) (Ponnamperuma, 1984; Freeman et al., 2001; Freeman et al., 2004). The changes in these communities can contribute to changes in soil structure, particularly the lack of channels and burrows (Zorn et al, 2005; Lavelle et al., 2006). This coupled with significant soil compaction from the weight of flood water, the creation of impermeable surface caps, or slumping of soil as it collapses downwards or sideways can seriously impact the physical properties of the soil (Horn et al., 1995). While it is known what happens to soils during and shortly after flooding, little work has been done on the long term impacts of extreme flooding on agricultural soils, or on recovery rates of soils and crop yields. This study addressed this question by means of a monthly study conducted over several sites that were heavily impacted by the winter 2013-14 floods. 3.1.1 Methods Site selection: A total of fifteen sites were selected across Somerset, Worcestershire, Herefordshire and North Wales. All of the selected sites had been under flood water for 8-12 weeks during the extreme winter flooding in 2013-14. Five of the fifteen sites were selected for more detailed monthly sampling; each of these sites had definite flooded and control areas (areas that had remained above the flood water), which could be directly compared throughout the study. The sites selected for the monthly sampling were:

Site 3 – an arable field in Worcestershire

Site 4 – an arable field adjacent to Site 3 in Worcestershire

Site 7 – a grassland field in Somerset that is regularly flooded and has little intervention, and thus has developed a flood tolerant sward

Site 14 – an arable field in Somerset

Site 15 – a grassland field adjacent to Site 12 in Somerset Time scale: All of the fifteen sites were sampled in April 2014 as an ‘Initial’ sample period, just after the last of the flood water receded. Sampling was then carried out on the five selected sites every five weeks from the end of May 2014 through to the middle of December 2014 – a total of seven sampling periods. The remaining ten sites of the original fifteen were also re-sampled in December 2014 as an ‘End’ sample period. Data collection: Samples were taken from three plots from within each control and flooded area. Sites 3 and 13 also had ‘Medium’ areas which had been flooded for only a short period compared to the flooded areas (4-5 weeks). All of the measurements detailed below were sampled at the five selected sites every

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five weeks for the monthly sampling period. However, only aboveground biomass, bulk density, soil pH, soil salinity and soil nutrients were measured at the initial and end sampling periods for the remaining ten sites. The following measurements were made:

Aboveground biomass (dry weight t ha-1) was measured to determine differences in crop yield or plant productivity between flooded and control areas over time. Plant aboveground biomass was cut down to the soil from within a 20 x 20 cm quadrat at each plot. These samples were then dried at 80°C for 16 hours and weighed to determine the dry weight.

Worm numbers (n) were quantified to determine the health of the worm population. Worm numbers were counted from within a 20 x 20 x 20 cm ‘sod’ for each plot.

Soil bulk density (g cm3) was measured as an estimate of soil compaction. A 100 cm3 bulk density ring was used to take three samples from each flooded and control area. These samples were weighed, then dried at 120°C for 16 hours before being weighed a second time. Bulk density and soil moisture content was then calculated.

Soil pH was measured to determine changes in soil conditions. Soil samples were taken from each plot and 10 g samples were extracted with 25 ml deionised water. The samples were then shaken thoroughly and left for approximately 4 hours to settle. The pH was then measured using a Hanna pH probe.

Soil electrical conductivity was measured as a proxy for soil salinity to determine changes in soil conditions. Soil samples were taken from each plot and 10 g samples were extracted with 25 ml deionised water. The samples were then shaken thoroughly and left for approximately 4 hours to settle. The electrical conductivity was then measured using a Jenway 4520 conductivity meter.

Soil phosphate (µg g-1) was measured to determine nutrient changes and uptake rates in the soil. Three soil samples were collected from each flooded and control area. Five grams of each sample was extracted with 25 ml of 0.5 molar Sodium Bicarbonate (NaHCO3) buffered to pH 8.5. Phosphate concentration was then determined using the methods from Murphy and Riley (1962): 180µl of Ames reagent and 30µl of 10% ascorbic acid was mixed with 80µl of each sample in a microplate. The colour was allowed to develop for 30 minutes before the absorbance of the samples was read at 820 nm using a Biotek Powerwave XS plate reader and Gen5. Standards were also run alongside the samples; these were used to create a standard curve from which the concentration of each sample was calculated.

Soil nitrate (µg g-1) was measured to determine nutrient changes and uptake rates in the soil. Three soil samples were collected from each flooded and control area. Five grams of each sample was extracted with 25 ml of 0.5 molar Potassium Sulphate (K2SO4). Nitrate concentration was then determined using the methods from Mulvaney (1996) and Miranda, Espey and Wink (2001): 100µl of vanadium chloride, 50µl of NEDD and 50µl of sulphonamide were mixed with 100µl of each sample. The colour was allowed to develop for 15 minutes and the absorbance of the samples was read at 540 nm using a Biotek Powerwave XS plate reader and Gen5. Standards were also run alongside the samples; these were used to create a standard curve from which the concentration of each sample was calculated.

Soil ammonium (µg g-1) was measured to determine nutrient changes and uptake rates in the soil. Three soil samples were collected from each flooded and control area. Five grams of each sample was extracted with 25 ml of 0.5 molar Potassium Sulphate (K2SO4). Ammonium concentration was determined using methods outlined in Mulvaney (1996): 15µl of EDTA, 60 µl of Na-salicylate-nitroprusside reagent and 30 µl of sodium hypochlorite reagent were mixed with 150 µl of each sample. The colour was allowed to develop for 15 minutes before the absorbance was read at 667 nm using a Biotek Powerwave XS plate reader and Gen5. Standards were also run alongside the samples; these were used to create a standard curve from which the concentration of each sample was calculated.

Infiltration rates (ml min-1) were measured to determine soil water saturation and infiltration changes over time after the flood water receded. Infiltration rates were measured at each plot using Decagon Devices mini disk infiltrtrometers. The rate of infiltration per minute was measured over 30 minutes and an average infiltration rate was calculated for each plot for each month.

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Soil respiration (g m-2 h-1) was measured as an indication of soil microbial activity in the soil. Microbial activity is a good indicator of soil conditions, but can also be affected by temperature and soil moisture (Amundson, 2001; Pendall et al., 2004) and there can therefore be considerable seasonal changes in soil respiration rates (Bardgett et al., 1997; Bardgett et al., 1999). Soil respiration was measured at each plot using an EGM-4 infra-red gas analyser (PP-Systems 2010).

Data analysis: Two separate analyses were run on the data: an analysis of the monthly data and an analysis of the initial and end data. The monthly data was analysed as an overall data set (all months, all sites), as grasslands vs. arable sites, and as individual sites, while the initial and end data was analysed only as an overall data set to compare between the time periods. The data was square root transformed before the analysis and Euclidean distance dissimilarity matrices were calculated for each analysis. Permutational multiple analyses of variances (PERMANOVAs) were then used to determine differences between treatments (flooded, control), months (n = 7), and sites (n = 5). Partial eta squared effect sized (η2

p) were calculated for PERMANOVA results, where a small effect ≥ 0.0099, a medium effect ≥ 0.0588, and a large effect ≥ 0.1379 (Cohen, 1988; Richardson, 2011). Pairwise tests were conducted where appropriate to determine where any statistical differences lay. Where significant differences between treatments were found, principle component analyses (PCAs) were used to determine which factor(s) contributed most to the differences. 3.1.2 Results Here we provide a summary of the 5 sites below. Appendix 1 provides a more complete description of the results. Each farmer was provided with a report of the results specific to their farm. At the start of the study (April 2014, just after the flood water had receded), there was a significant difference with a large effect size between flooded and non-flooded (control) areas (PERMANOVA Factor: Treatment – Psuedo F = 2.86, P(perm) = 0.023, η2

p = 0.706). By the end of the study (December 2014, at least 8 months after flood waters had receded), these differences were no longer apparent, although there were significant changes in both flooded and control areas between the two time periods (Flooded areas: PERMANOVA Factor: Time – Psuedo F = 88.62, P(perm) = 0.001, η2

p = 0.621; Control areas: PERMANOVA Factor: Time – Psuedo F = 151.73, P(perm) = 0.001, η2

p = 0.783). (Further detail of the statistical results can be found in Appendix 5). A principle component analysis (PCA) was conducted on the ‘Start’ data to determine what factors contributed to the difference between flooded and control areas. The PCA revealed four components with eigenvalues exceeding 1, and together these explained 97.6% of the variation in the data. Soil moisture loaded strongly on the first, second and third factorial component, soil EC (salinity) loaded strongly on the first and third factorial component, and soil nitrate loaded strongly on the second and third factorial component, while soil phosphate and ammonium loaded strongly on the fourth factorial component (Table 1). Table 1 shows how these factors related to each other at the start of the study.

Soil Moisture (% Dry Weight)

Soil Salinity (mS)

Soil Nitrate (µg g

-1)

Soil Phosphate (µg g

-1)

Soil Ammonium (µg g

-1)

�̅� SE �̅� SE �̅� SE �̅� SE �̅� SE

Flooded area 92.13 8.95 167.22 15.99 16.52 2.33 14.04 2.02 3.18 1.10 Control area 49.08 5.15 68.15 37.67 10.31 2.71 23.87 4.46 7.45 2.03

Table 1. Descriptive statistics of the main soil parameters (loadings on the factorial components for the PCA

analysis) on the ‘Start’ data by ‘Flooded’ and ‘Control’ areas.

A second PCA analysis was conducted on the on the data from flooded and control areas (over both time periods) to determine which factors contributed to the differences in both areas over time. The PCA revealed five and four components with eigenvalues exceeding 1, respectively. The five components identified in the ‘Flooded’ analysis explained 100% of the variance in the flooded area data across both time periods, while the four components identified in the ‘Control’ analysis explained 98.8% of the variance

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in the control area data across both time periods. Both analyses showed that soil moisture content loaded strongly on the first factorial component, soil EC loaded strongly on the second factorial component, and soil nutrients loaded strongly on the third fourth fifth components (Tables 2 & 3). Table 2 shows how these factors related to each other at the start of the study.

Soil Moisture (% Dry Weight)

Soil EC (mS cm

-1)

Soil Nitrate (µg g

-1)


-1)


-1)

�̅� SE �̅� SE �̅� SE �̅� SE �̅� SE

Flooded area; Start 92.13 8.95 167.22 15.99 16.52 2.33 14.04 2.02 3.18 1.10 Flooded area; End 307.42 37.46 188.17 18.89 5.09 1.02 31.69 5.38 4.20 1.53

Control area; Start 49.08 5.15 68.15 37.67 10.31 2.71 23.87 4.46 7.45 2.03 Control area; End 260.49 46.49 155.92 14.44 11.03 2.75 40.30 8.06 1.95 0.25

Table 2. Descriptive statistics of the main loadings on the factorial components for the PCA analysis on the

‘Start’ and ‘End’ data by ‘Flooded’ and ‘Control’ areas.

Worm numbers were not included in this analysis as data was not collected for all of the sites in the start-end data set. Instead, data was collected monthly for the five selected sites. Worm numbers are being mentioned here because, although there were no significant differences in worm numbers between flooded and control areas or between sites in the more detailed analysis, they did change over time (Figure 1). There is evidence of lower worm numbers in flooded soil for periods of the year, although no significant differences were detected across all five sites. There were no strong monthly trends beyond a dip in June (week 9) but there was a steady increase in worm numbers over time. A t-test showed a significant difference in worm numbers between May and December (p = 0.001).

Figure 1. Overall trend of worm numbers from the ‘Overall’ data set (all months, all sites) over time with

standard error bars.

When looking at the detailed data from the selected five sites, there were no significant differences between flooded and control areas overall (all sites pooled, over all months), but there were significant differences with large effect sizes between months (PERMANOVA Factor: Month – Psuedo F = 5.17, P(perm) = 0.001, η2

p = 0.621) and sites (PERMANOVA Factor: Site – F = 45.80, P(perm) = 0.001, η2p = 0.704).

Similarly, overall analyses for each month (all sites pooled) showed no significant difference between flooded and control areas, but significant differences between sites each month. To determine what factors were contributing to the differences between sites and months, a PCA was run on the overall data set. The PCA revealed three components with eigenvalues exceeding 1, and together these explained 87.1% of the variance in the data. Soil salinity loaded strongly on the first factorial component, plant biomass and soil phosphate loaded strongly on the second factorial component, and soil nitrate loaded strongly on the third factorial component (Table 4). Figure 2 shows general trends of these factors over time with data pooled for all sites; there were strong seasonal patterns in each of the factors.

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Figure 2. Trends of soil salinity, vegetation biomass, soil phosphate and soil nitrate on the ‘Overall’ data set

(all months, all sites) over time with standard error bars on both flooded and control data sets.

There were overall differences between flooded and control sites for two specific factors: soil salinity and plant biomass. Individual t-tests on the data showed that soil salinity was significantly different between flooded and control areas overall in May (p<0.001), June (p = 0.007), July (p = 0.013) and August (p = 0.011). Figure 2 shows that soil salinity remained consistently higher in flooded areas than in control areas throughout the sampling period, but the difference between the areas did reduce over time. Plant biomass differed significantly between flooded and control areas in June (p = 0.004), July (p = 0.004) and August (p = 0.005), but not when initially sampled in May. Figure 2 shows that plant biomass remained higher in the control areas during these months, before dropping to similar levels to that of the flooded areas. Due to the large differences between sites, the data was divided into grassland sites (n=2) and arable sites (n=3), and analysed to determine how flooding impacted each land use type. Again, data for all grassland/arable sites was pooled over all time periods to determine any overall patterns, and separate analyses were done for each month for both grassland and arable data. There were no significant differences between flooded and control areas, or between months on grasslands. However, there was a significant difference between the two grassland sites (PERMANOVA Factor Site: Pseudo F = 84.51, P(perm) = 0.001, η2

p = 0.751). Similarly, an analysis of the arable fields showed no significant differences between flooded and control areas, but there were significant differences between sites (PERMANOVA Factor Site: Pseudo F = 21.89, P(perm) = 0.001, η2

p = 0.472) and between months (PERMANOVA Factor Month: Pseudo F =6.74, P(perm)= 0.001, η2

p = 0.796). Further to this analysis, each site was analysed separately. However, the data for each month at each site was pooled rather than analysing each month separately due to low sample sizes and therefore low statistical power (Cohen 1988). The results of each analysis by site are shown in Table 3.

Site

Treatment (flooded vs. control) Month

Pseudo F P(perm) η2

p Pseudo F P(perm) η2

p

Site 3 (arable) 2.32 0.053 0.100 49.56 0.001 0.876 Site 4 (arable) 15.01 0.001 0.349 34.47 0.001 0.881 Site 7 (grassland) 15.75 0.001 0.360 8.15 0.001 0.636 Site 14 (arable) 34.73 0.001 0.554 12.26 0.001 0.730 Site 15 (grassland) 86.52 0.001 0.756 3.30 0.001 0.414

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Table 3. Results of several permutational analyses of variance, one for each site, showing Pseudo F, P(perm) and partial eta squared effect size (η2

p) for each factor by site. The factors analysed were Treatment (n = 2: Flooded and Control), and Month (n = 7). Statistically significant results are highlighted by emboldened text. Principle component analyses were run for the sites that showed significant differences between flooded and control areas to determine what factors were contributing to these differences. A PCA for Site 4 revealed four components with eigenvalues exceeding 1 and together these explained 95.6% of the variance in the data. Soil salinity and soil nitrate loaded strongly on the first and fourth factorial components, and plant biomass and soil phosphate loaded strongly on the second and third factorial components. Table 4 shows how these factors related to each other on Site 4.

Soil Salinity (mS cm

-1)

Soil Nitrate (µg g

-1)


-1)

Plant Biomass (t ha

-1 DM)

�̅� SE �̅� SE �̅� SE �̅� SE

Flooded 145.80 12.79 21.41 2.60 25.79 3.15 1.46 0.49 Control 138.36 16.11 22.47 3.18 36.82 4.02 12.15 2.60


overall data for Site 4.

A PCA for Site 7 revealed three components with eigenvalues exceeding 1 and together these explained 85.8% of the variance in the data. Soil salinity loaded strongly on the first factorial component, soil nitrate loaded strongly on the second factorial component, and soil nitrate and soil phosphate loaded strongly on the third factorial component. Table 5 shows how these factors related to each other on Site 7.


-1)


-1)

Soil Nitrate (µg g

-1)

�̅� SE �̅� SE �̅� SE

Flooded 123.67 20.06 4.81 1.90 11.73 1.53 Control 140.39 13.25 8.50 3.22 11.26 2.13



A PCA for Site 14 revealed four components with eigenvalues exceeding 1 and together these explained 95.5% of the variance in the data. Soil salinity and plant biomass loaded strongly on the first and third factorial components, soil phosphate loaded strongly on the second factorial components, and soil nitrate and worm numbers loaded strongly on the fourth factorial component. Table 6 shows how these factors related to each other on Site 14.


-1)

Plant Biomass (t ha

-1 DM)


-1)

Soil Nitrate (µg g

-1)

Worm Numbers (No. 8000cm-

3)

�̅� SE �̅� SE �̅� SE �̅� SE �̅� SE

Flooded 251.52 17.85 3.06 0.83 24.05 8.86 15.45 1.65 0.95 0.45 Control 160.17 15.91 22.47 4.58 25.24 4.80 12.81 1.86 1.71 0.55



A PCA for Site 15 revealed four components with eigenvalues exceeding 1 and together these explained 96.5% of the variance in the data. Soil salinity loaded strongly on the first factorial component, soil phosphate loaded strongly on the second factorial component, soil phosphate and soil nitrate and worm numbers loaded strongly on the third factorial component, and soil ammonium loaded strongly on the fourth factorial component. Table 7 shows how these factors related to each other on Site 15.

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-1)


-1)

Soil Nitrate (µg g

-1)

Worm Numbers (No. 8000cm-

3)


-1)

�̅� SE �̅� SE �̅� SE �̅� SE �̅� SE

Flooded 415.42 36.84 9.11 2.52 18.15 2.08 2.43 0.47 3.45 0.57 Control 134.23 18.38 7.55 1.51 19.57 2.46 2.57 0.57 7.38 2.28



There were few fluctuations over time, or differences between sites and treatments in the other measured parameters: CO2 flux, infiltration rates, soil pH, and soil bulk density. There were only minor seasonal fluctuations in a general decrease in CO2 flux, likely due to seasonal changes in soil moisture and temperatures. The general decrease in CO2 flux was likely due to decreasing temperatures throughout the sampling period, but warmer periods in June and August may have increased CO2 production in the soils. These seasonal changes were also reflected in soil infiltration rates: there was a general decrease in soil infiltration rates as surface soil moisture increased due to increasing rainfall, with small fluctuations during slightly drier periods. Soil bulk density showed few fluctuations until several of the sites were re-seeded in September, and there were no differences between flooded and control areas throughout the study. Soil pH also showed little fluctuation, ranging between 6 and 7.5. There were still some seasonal fluctuations, such as an increase in May and June, shortly after the flood water receded. 3.1.3 Discussion There were significant differences between flooded and control areas at the start of the study in April 2014, but by the end of the study in December 2014, these differences were no longer statistically significant. The differences at the start of the study were due to soil moisture, soil salinity and soil nutrient levels. Both flooded and control areas changed over time with significant differences between the start and end of the study. These differences were due to changes in soil moisture, soil salinity and soil nutrient levels. Soil moisture showed the same pattern for both flooded and control areas: a higher soil moisture content in December compared to April. This was most likely due to the weather; in April the flood water had receded and there was very little rain, thus the surface soil had dried out. By December, there had been a considerable amount of rain before and during the sampling period, so the surface soil was more saturated. Neither an analysis of the monthly data as an overall data set, or one by site type (arable vs. grassland) yielded any clear effect of flooding on agricultural soils. Instead, only when the sites were treated separately could some of the effects of the flooding be seen, suggesting that flooding impacts are site-specific. All but Site 3 showed significant effects of the flooding, with differences in soil salinity, soil nutrients, plant biomass and worm numbers explaining most of the variation in the data. Soil salinity in the flooded areas did not differ much from April to December, but in the control areas, salinity showed a big increase from April to December. The December levels of soil salinity in the control areas were roughly equal to those of the flooded areas in both April and December. Looking at the more detailed monthly data, soil salinity was higher in flooded areas than in control areas, but this difference diminished over time as soil salinity fluctuates over the year. It’s likely that the general increase in soil salinity is due to the addition of fertilisers – this is particularly evident in May and September when fertilisers were added to many of the sites before re-seeding. The elevated soil salinity in the flooded areas was likely due to a combination of deposits by the flood water and the lack of live plant biomass that could process added soil nutrients. Soil nutrient levels fluctuated over time. Soil nitrate levels were similar in both control and flooded areas at the beginning of the study, but at the end of the study nitrate levels were lower in the flooded area, while control areas showed no difference in soil nitrate between April and December. The detailed monthly data showed monthly fluctuations in soil nitrate levels, in particular after the arable fields were harvested and re-fertilised in August. Soil phosphate levels were higher at the end of the study than at the start. During the monthly sampling period soil phosphate levels followed re-seeding patterns: there were peaks in soil phosphate levels in May and November just after the sites had been fertilised and/or re-seeded. Soil ammonium levels were higher at the start of the study in the control areas than at the end; there was no

11

change between beginning and end in the flooded areas. The higher levels at the start of the study could have been due to a build-up of ammonium in following nitrogen mineralisation in the flooded soil (Ponnamperuma, 1984; Unger et al., 2009). Again, soil ammonium levels appeared to change according to farming practices rather than any other process: soil ammonium remained relatively low except in May and September after the sites were fertilised and re-seeded. Earthworm numbers generally remained highest in the control areas, suggesting that flooding did reduce the worm populations. However, worm numbers in flooded areas did slowly increase over time, suggesting that populations recovered. There was a clear impact of flooding on plant biomass during the spring and early summer months following the flooding. For some crops (e.g. spring onions and swedes) the severity of impact depended on the period of inundation – with destruction of the whole crop where flood waters remained for ca. 3 months. In such cases, farmers resorted to re-cultivation and planting alternative crops. Observations of the larger number of field sites visited in the site selection phase suggested that growth of permanent grassland was less affected by prolonged flooding than recently reseeded grasslands. On the whole, the agricultural soils that were sampled in this study have shown considerable recovery after nine months. A great deal of this has been due to interventions by the farmers, such as the addition of fertilisers and re-seeding, but one of the grassland sites (Site 7) had very little intervention. Instead, this field was left to develop flood-resistant species over several years of semi-regular flooding, yet it has shown similar recovery rates to that of the other sites. However, this study only focused on surface soils. There may have been more lasting impacts on deeper soil layers, particularly those already heavily impacted by farm machinery.

12

3.2 Identification of potential tipping points in terms of inundation time (laboratory experiments) A series of laboratory experiments were conducted to study the changes in soil chemistry and soil processes that occur during prolonged flooding, as well as during the period after flood water has drained away (the recovery period). Where appropriate, effects of flooding on above-ground plant biomass were quantified. The field-based soil and vegetation monitoring reported earlier was limited to the recovery period only, i.e. the period after the floodwater had receded. Hence, these controlled experiments facilitated the study of changes in soil water and flood water chemistry both during the flooding period itself, and during the post-flood recovery period. 3.2.1 General Materials and Methods Experimental design All of the laboratory experiments used a similar design. Soil, which was not normally exposed to flooding, was collected from a depth of 0-10 cm, sieved to 1 cm to remove stones and roots, and sealed in polythene bags and refrigerated at 4oC prior use. In experiments 1 and 5, intact grassland soil was used. Rectangular plastic boxes (2 L) were filled with ca. 850 g of sieved soil, or intact soil blocks were cut to size. For some treatments flood water was added to achieve a depth of 9-10 cm above the soil surface. Non-flooded treatments were also established, where the soil was maintained at field capacity. Flood boxes were stored at a constant temperature in controlled growth cabinets with lighting (depending on the experiment) and loosely covered with polythene bags to prevent excessive water loss. Temperature was measured during each experiment. The depth of flood water was maintained for 9-12 weeks until it was drained off. After flood water was removed, sampling continued for at least 4 weeks (the recovery phase).

Photo 1. Experimental flood boxes with and without flooding treatments. Flood water and soil solution sampling Rhizon samplers were used to extract soil solution on a regular basis. Samples of flood water above the soil were also taken for analysis. pH and EC were determined with Hanna pH209 pH and Hanna EC214 conductivity meters, respectively. Soil solution and flood water samples were analysed colorimetrically for NO3

-, NH4+ and PO4

3- using a BioTek Epoch spectrophotometer and the methods of Miranda et al. (2001), Mulvaney (1996) and Murphy and Riley (1962), respectively. (Samples containing sea water were first diluted (x10) with distilled water before being analysed for NH4

+ due to the solution turning cloudy once reagents had been added). GHG emission measurements Nitrous oxide, CH4 and CO2 emissions were measured in some experiments. The headspace above the floodwater was sampled after closure of the flood box. 20 ml samples were removed at T0, T30 and T60 minutes and placed in pre-evacuated vials prior to GC analysis. Emissions were calculated based on the assumption of a linear increase in headspace gas concentration during this period.

13

Ammonia volatilisation estimates Potential ammonia emissions were measured in a number of experiments by trapping the gas in sulphuric acid in a small petri dish within the closed headspace over a 1 hour period. The NH4 content of the trap was quantified and potential ammonia volatilisation was estimated. Acid traps were changed regularly according to a strict temporal schedule. Treatments and measurements Experiments were designed to include a number of different (but typical) scenarios to study the interaction between flooding and a number of typical management factors. Table 8 summarises the treatments and measurements made in each of the five experiments.

Exp. ID and monitoring period

Crop and soil

Amendment / light regime

Flood water

Temperature (oC)

Soil water chemistry

Flood water chemistry

GHG

1. Flood period and recovery

Intact grassland soil Clay loam

None Fresh 5, 15, and 25 pH, EC, NO3, NH4, PO3

pH, EC, NO3, NH4, PO3

N2O, CH4, CO2

NH3


No crop Clay loam

Poultry manure

Fresh or Saline

10 and 15 pH, EC, NO3, NH4, PO3


N2O, CH4, CO2

3. Recovery period only

No crop Loamy sand

Maize straw residue

Fresh 10 pH, EC, NO3, NH4, PO3


N2O, CH4, CO2


Intact grassland soil Clay loam

Light and dark

Fresh 15

pH, EC, NO3, NH4, PO3,

33P, Fe

pH, EC, NO3, NH4, PO3,

33P, Fe

N2O, CH4, CO2

*

Table 8. Summary of laboratory experimental treatments. *GHGs only measured in the recovery phase (after flood water was removed) Statistical analysis The effect of flooding on ammonium, nitrate and phosphate release and changes in floodwater pH in the freshwater and saline treatments was investigated using a one-way ANOVA (P<0.05). Correlations and linear regressions were used to identify relationships. 3.2.2 Results 3.2.2.1 Experiment 1 – Effect of temperature on soil and flood water chemistry, during and post flooding Brief summary of the experimental approach: 24 intact soil cores from a permanent grassland were cut to size and placed in flood boxes (described earlier) and the sample number split evenly into 5oC, 15oC and 25oC treatments. The 5oC treatment was maintained in the dark (to simulate a greater depth of flood water), whilst the 15oC and 25oC treatments were kept in temperature controlled growth cabinets. Half of the intact soil cores at each temperature were flooded with water taken from the Rhaeadr-Fawr River, and the depth of the water was maintained at a depth of 10 cm (above the soil surface) for 12 weeks. After 12 weeks the flood water was removed from all treatments. The other half of the intact soil cores received sufficient water to reach field capacity. This water status was maintained for the duration of the flooded period through regularly weighing flood boxes and adding flood water. Weekly soil water and floodwater sampling took place during the 12 week flood period and during the 4 weeks after the flood water had been carefully removed. The experimental treatments were:

Flooded 5oC

Flooded 15oC

14

Flooded 25oC

Non-flooded 5oC

Non-flooded 15oC

Non-flooded 25oC Results: Soil water and flood water chemistry Soil water pH increased gradually during the flood period, and remained relatively constant following flood water removal (Figure 3). However there was a significant increase in electrical conductivity (EC) in soil water during the flood period (Figure 4), as a result of an increase in ions, e.g. via organic N mineralisation to NH4

+ (Figure 5).

Figure 3. Effect of flooding on soil and flood water pH.

Figure 4. The EC of soil and flood water, during and after prolonged flooding.

The flood water EC level reflected the soil EC levels. There was evidence of a reduction in soil water EC levels in the 15oC and 25oC treatments after the flood water had been removed, presumably as NH4

+ was nitrified and subsequently denitrified and lost as N2O (see Figure 7) and N2. There was no plant uptake of nutrients after flood removal in the 15oC and 25oC treatments, as the grass in these treatments had died. Figure 5 shows the dynamics of NH4

+ formation in the flooded soil treatments, with faster production of NH4

+ and greater rates of mineralisation with increasing temperature. The NH4+ concentration of the flood

water also increased, and resulted in measureable NH3 emissions (Figure 9).

15

Figure 5. NH4+ in soil and flood water, during and after prolonged flooding.

In contrast to NH4+, there was little NO3

- in the soil at the start of the flood period. Significant soil water NO3

- concentrations were not observed until after the flood water had been removed, especially at the highest temperature treatment (Figure 6). The increase in soil water NO3

- concentration coincided with the increase in N2O emissions (Figure 7).

Figure 6. NO3- in soil and flood water, during and after prolonged flooding.

GHG and ammonia emissions N2O emissions were negligible from the unflooded soils, although some fluxes were measured from the flooded soil at the highest temperature during the flood period (Figure 7). However, the N2O emissions after the flood water was removed were very high for the 25oC flood treatment, but also marked from the 15oC flood treatment. These N2O emissions coincided with the formation of soil NO3 once the flood water was removed and the large soil NH4 pool (that had accumulated during the flood period) was nitrified.

16

Figure 7. N2O emissions during and after flooding.

Methane emissions increased from the flooded 25oC treatment (Figure 8). These CH4 fluxes remained high for > 2 months before decreasing. Once the flood water was removed, fluxes rapidly reduced back to zero.

Figure 8. CH4 emissions during and after flooding. Potential NH3 emissions were also measured, using acid traps in the headspace of the sealed chambers. Figure 9 shows the dynamics of potential NH3 emission during the flooding period. Potential emissions are shown as concentration of NH3 in the acid traps. Emissions were significantly greater at 25oC, but emissions were also detected from the 15oC flooded treatment.

Figure 9. The effect of flooding on potential ammonia emissions. Iron release Finally, there was significant release of Fe as a result of flooding, with greater concentrations in soil water at the 15oC and 25oC treatments. Following removal of the flood water, the soil solution Fe concentrations in these higher temperature treatments reduced (Figure 10). There was no clear effect of flooding or temperature on PO4

3- release into soil water (data not shown).

17

Figure 10. Iron concentrations in soil and flood water, during and after prolonged flooding.

18

3.2.2.2 Experiment 2 – Impact of freshwater and marine flooding on soil and flood water chemistry, and GHG emissions following broiler litter amendment Brief summary of the experimental approach: A clay loam soil was collected from permanent grassland from a depth of 0-10 cm, sieved through a 1 cm sieve and stored in plastic bags at 4oC prior to use. Broiler litter was collected from a commercial farm on Anglesey, and was sealed in a polythene bag and stored at 4oC prior to use. Sea water was collected from a section of the Menai Straits, and freshwater was collected from the Rhaeadr-Fawr River in Abergwyngregyn, North Wales. Flooded and non-flooded treatments, with and without broiler litter (applied at the equivalent rate of 10 t/ha) were established with the fresh and saline water, and soils maintained at either 5oC or 15oC. The flooded period lasted for 9 weeks, with a further 4 weeks when soils were retained at field capacity. Sampling continued for a further 4 weeks when the soils were allowed to dry out. The following treatments were established: 5oC Soil + freshwater (control) 5oC Soil + broiler litter + freshwater 5oC Soil + freshwater flood (control) 5oC Soil + broiler litter + freshwater flood 5oC Soil + saline water (control) 5oC Soil + broiler litter + saline water 5oC Soil + saline flood (control) 5oC Soil + broiler litter + saline water flood 15oC Soil + freshwater (control) 15oC Soil + broiler litter + freshwater 15oC Soil + freshwater flood (control) 15oC Soil + broiler litter + freshwater flood 15oC Soil + saline water (control) 15oC Soil + broiler litter + saline water 15oC Soil + saline flood (control) 15oC Soil + broiler litter + saline water flood The broiler litter had a high N and P content (Table 9), and was typical of this manure type (Defra, 2010) Property Value

Oven dry matter (%) Total N (%) Total C (%) C:N Nitrate-N (mg kg

-1)

Ammonium-N (mg kg-1

) Total P (%) Total K (%) pH [1:6] Uric acid-N (%)

48.37 ± 0.09 2.76 ± 0.00 21.17 ± 0.07 8:1 <10 4105.3 ± 63.25 0.82 ± 0.01 1.90 ± 0.01 5.54 ± 0.07 0.66 ± 0.02

Table 9. Broiler litter composition. Results Soil and flood water chemistry The addition of poultry litter significantly (P<0.05) increased the NH4

+ concentration in the soil water in all treatments (Figure 11) and the total content of PO4

3- in the majority of treatments. However, temperature had no significant (P<0.05) effect on mineralisation of organic N. Freshwater flooding significantly increased the release of NH4

+ into soil water and flood water. NO3- and PO4

3- concentrations were all greater in freshwater flooded soils. Saline flooding also increased the release of NH4

+ and NO3- at 8oC, with total content averaging 25.32 and 1.19 mg/microcosm compared to 13.10 and 0.40 mg/microcosm in the non-

19

flooded treatments. However, flooding the soil with saline water resulted in a smaller release of PO43-

compared to the soil which was flooded with freshwater, averaging 0.66 and 0.73 mg/microcosm (8oC) respectively. The results of this study suggest that overall, freshwater and saline flooding both have the potential to significantly increase the mineralisation and release of NH4

+ from soil organic matter and organic amendments. However, the release of NO3

- and PO43- was greater following freshwater flooding

compared to saline flooding which appeared to release less PO43- and to inhibit NO3

- formation via nitrification.

Figure 11. Average soil water and flood water (freshwater) NH4

+ and NO3- concentrations (8oC). Flood water

was removed on day 56. Greenhouse gas emissions During the flooding, CH4 emissions remained low from all treatments, except the freshwater 20oC treatment, where fluxes started after ca. 3 weeks, and continued to increase for the duration of the flooded period (Figure 12). Indeed, for the first weeks after the flood water was removed, fluxes remained high from this treatment, before decreasing rapidly. Methane emissions were also observed from the non-flooded manure amended soil.

Figure 12. Methane emissions from freshwater flooded soils amended with broiler litter (20oC). Nitrous oxide emissions were low during the flooding period from all treatments, although there were small fluxes from the non-flooded (fresh water) manure treatments (data not shown). After the floodwater was removed, N2O emissions were observed from the freshwater manure treatments after a lag of around 20 days (Figure 13). Conversely the release of N2O (mg/m2) in the saline treatments remained below zero for the remainder of the experiment.

20

Figure 13. Nitrous oxide emissions from soils with / without broiler litter, and with / without freshwater flooding (20oC).

20

Days after water came off

0 10 20 30 40 50 60 70

N2O

(

g N

m2

h1)

0

600

1200

1800

C - Fresh

C + F - Fresh

M - Fresh

M + F - Fresh

C - Saline

C + F - Saline

M - Saline

M + F - Saline

21

3.2.2.3 Experiment 3 – Effect of freshwater flooding on soil and flood water chemistry, and GHG emissions following maize residue incorporation Brief summary of the experimental approach: Soil, a sandy loam, was taken from the non-flooded area at Site 4. Flood chambers were set up with ca. 800g of soil. Chopped (2 cm pieces) maize residues were added to half of the flood chambers and mixed into the soil. Flood water (taken from the River Aber) was added to half of the flood chambers, whilst the control soils received water to achieve field capacity. Flood water level and control soil water was topped up weekly to maintain the water status of the two treatments. No plants were sown. Flood water remained in place for 10 weeks, and the flood chambers incubated in a controlled temperature room in the dark at 10oC. After the 10 week flood period, the flood water was removed. Soil water sampling (using Rhizon samplers) and flood water sampling continued weekly during the recovery phase. After the 4 week recovery phase, soil was taken from each flood chamber and a growth test was performed using maize seedlings to determine any lasting detrimental effect on future crop production. The treatments were:

Soil + maize residue control

Soil + maize residue flooded

Soil only non-flooded

Soil only flooded Results: Soil water chemistry

Figure 14. Soil solution chemistry after flood water was removed (the first data point is just before flood water was removed); a) pH, b) EC, c) Fe concentration, d) total P concentration. Greenhouse gas emissions One GHG sample was taken from all flood boxes immediately before the flood water was removed; the CH4 flux was high from the flooded soil with the maize residue amendment at this stage of flooding, whilst the N2O flux was low at this point (Figure 15). Nitrous oxide fluxes increased 10 days after flood water was removed from both the maize residue flooded and non-amended flooded treatments, suggesting that

22

organic N mineralisation of both the maize residue and soil organic matter occurred during flooding, resulting in a pool of NH4

+ that was subsequently nitrified and denitrified and N2O production and emission.

Figure 15. Methane and nitrous oxide emissions after flood water was removed (the first data point is just before flood water was removed); a) methane, b) nitrous oxide.

23

3.2.2.4 Experiment 4 – Effect of flooding of intact grassland soil on P dynamics and GHG emissions Brief summary of the experimental approach: Intact soil samples were taken from a grazed grassland located in Abergwyngregyn (North Wales) to simulate a spring-like (15oC) long term flood event. Flood water was taken from the River Aber and applied to a depth of 10 cm under different conditions in a growth chamber, with a photoperiod of 18 h day ̶ 1, a light intensity of 350 µmol m ̶ 2 s ̶ 1, a temperature of 15oC and a relative humidity of 70%. Non-flooded treatments were also established where the soil moisture content was retained at field capacity. A 33P solution was applied to the soil surface at the beginning of the experiment in order to assess the movement of P in the water-vegetation-soil system. Soil water and flood water were sampled regularly during the flood (9 weeks) and recovery (4 weeks) periods and analysed for pH, EC, redox potential in soil, Fe, P, NH4

+, NO3 ̶ , dissolved organic C and N). Greenhouse gas emissions

were quantified only during the recovery period, whilst microbial community shifts after the extreme event were also assessed. Some treatments were kept in the dark to simulate deep and turbulent flood water. The containers were weighed weekly and watered with fresh water to keep soil moisture near field capacity during pre-flood and soil recovery stages in T1, T2, T4 and T5 containers, and flooding stage, only in T5 containers. In the flooding stage, fresh water was added to the flooded containers (T1, T2, T3 and T4) to replace the losses due to transpiration and evapotranspiration processes. The experiment had three different stages: (1) Pre-flood stage, in which the grass of the 16 containers got used to the growth chamber and grew for 2 weeks; (2) Flood stage, in which the different treatment were applied. (3) Soil recovery stage, in which the flood water was carefully removed No light restrictions were applied during this stage, which had a length of 5 weeks. The following treatments were established, with four replicates of each: T1-flooded soil without vegetation (dark to simulate deep flood water and/or sedimentation) T2-flooded soil with vegetation (dark) T3-flooded vegetation cut from T1 soil samples (dark) T4-flooded soil with vegetation (light) T5-non-flooded with vegetation (light) (control) Soil samples (30 g) from each container were collected at the beginning and at the end of soil recovery stage to determine PLFAs. Samples were frozen at ̶80º C until analysis were done in an external laboratory. A standard protocol was used to examine the PLFA profiles (University of Wisconsin, Madison; www.eco-system-microbiology.wisc.edu/methods). Greenhouse gas emissions were only measured during the recovery phase, i.e. after flood water had been removed. Results Soil and flood water chemistry A release of nutrients (Fe, P, 33P first from decomposed vegetation and then from the soil, NH4

+, organic C and N) was observed under flooding but was more evident in the dark (Figure 16), causing a rapid death of plants and increasing the amount of mineralised organic matter, which resulted in increased NH4 concentrations in both the soil water and flood water. Nitrate levels remained low until after the flood water had been removed and the soil dried out sufficiently for oxygen to diffuse into the soil and promote nitrification. Fe was released quickly almost immediately after flooding commenced (data not shown), reaching maximum concentrations in soil solution and floodwater by the end of the flooding phase. The release of Fe from the soil-grass system under light was slower than under dark conditions in soil solution and was negligible in the floodwater. This was probably the result of the more aerobic soil conditions under the

http://www.eco-system-microbiology.wisc.edu/methods

http://www.eco-system-microbiology.wisc.edu/methods

24

flooded light treatment. P in soil solution remained at concentrations <0.5 mg L−1 for the whole experiment irrespective of treatment. However, P (33P and P) in floodwater had a higher concentration for treatments containing grass developed under dark than the treatment under dark without grass and the treatment with grass under light (in this order). This suggests that a considerable amount of P came from grass degradation during the first few weeks after flooding, and a relatively small amount of P came from the soil during the following 6-7 weeks of flooding. The plant effect was evident in nutrient dynamics, decreasing their release in flood under light conditions (approximately half of the grass survived at the end of the flood stage). GHG emissions The negative redox potential in flooded soils allowed CH4 emissions at the beginning of soil recovery stage, and N2O emissions when aerobic conditions replaced anaerobic conditions (Figure 17). In addition, redox potential was one of the most important parameters controlling shifts in soil microbial community. The surviving vegetation after the flood event (T4 only) alleviated negative effects of flooding, slowed the nutrient release, kept a higher redox potential in soil than treatments under light restrictions, reduced N2O emissions (reaching negligible values as in control treatment) and kept a more similar soil microbial community than non-flooded or control treatment.

Figure 16. Soil and flood water concentration of nitrate and ammonium during flooding and recovery periods (arrows represent the time of flood water addition and removal).

25

Figure 17. GHG emissions during the recovery period only (left hand graphs = hourly fluxes; right hand side = cumulative fluxes). Discussion of Laboratory Experiments Effects of flooding on N dynamics Mineralisation Freshwater flooding has the potential to influence mineralisation, theoretically halting the nitrogen cycle at the mineralisation stage in soil zones which are devoid of oxygen and therefore increasing the amount of NH4

+ available within the soil (Ponnamperuma, 1984). This was observed in most of the experiments, and not only in flooded soils that had received N inputs. Previous studies assessing the impact of flooding on N mineralisation have produced varying results (Ono et al., 1989; Aulakh et al., 2000; Unger et al., 2009; Anggria et al., 2012; Chen et al., 2012), but in general, flooding has been found to increase N mineralisation. This is potentially due to the increase in soil pH induced by flooding (between 6.0 and 8.0) (Patrick and Wyatt, 1964; Ponnamperuma et al., 1966; Ono, 1991) which favours microbial activity (Paul and Clark, 1989; Aciego Pietri and Brookes, 2008). Analysis of the flood water in several of the flooded treatments showed an increase in the pH. Previous studies have found temperature to be a major influence on N mineralisation (Manguiat et al., 1996; Griffin and Honeycutt, 2000; Cookson et al., 2002; Agehara and Warncke, 2005; Gao et al., 2014), with the rate increasing as temperature increases, up to an optimum of 40oC (Thamdrup and Fleischer, 1998), suggesting that a summer flood event would increase the potential for NH4 accumulation in soil before flood waters receded. Whilst this effect of temperature was seen quite clearly in experiment 1, this was not observed in Experiment 2.

26

Nitrification Nitrification is an aerobic process, hence during the flood period when the soil becomes devoid of oxygen (Ponnamperuma, 1984; Reddy and Patrick, 1984), NO3 formation is very low, contributing to the accumulation of NH4 in the soil. Nitrification rates increase when the flood water is removed and sufficient oxygen diffuses into the soil to facilitate nitrification. Nitrification rates are known to increase with temperature (Benoit et al., 2015) to an optimum of ca. 32oC, hence after a summer flood nitrification rates could result in high concentrations of soil NO3 which could be used by the crop if it has survived, or by a newly planted crop. But high soil NO3 concentrations remaining in the soil with no / low crop uptake will result in an increased risk of N2O emission as seen in several of the laboratory experiments. Greenhouse Gas Emissions Flooding will induce N2O emissions from any residual soil NO3 at the time of inundation, but after this soil NO3 has been depleted N2O emission are low/negligible until the flood water recedes and the soil becomes more aerobic, facilitating nitrification and subsequent denitrification of NO3 to N2O. This was clearly demonstrated in several of the laboratory experiments. Significant CH4 production and emission generally only occurs in the flooding phase, when organic matter decomposes under anaerobic conditions (Levy et al., 2012). As soon as sufficient oxygen diffuses into the soil, CH4 emissions reduce rapidly to zero. In contrast, CO2 emissions are significantly reduced during the flooding phase, but increase markedly as the soil becomes more aerobic after the flood water recedes. Again, rates of the microbial processes responsible for CH4 and CO2 emissions increase with increasing temperature. So summer floods are likely to result in increased fluxes of CH4 than under cooler winter floods. Phosphorus and iron release Phosphorus and iron release is enhanced during flooding, predominantly due to the release of PO4

3- from FePO4 (iron phosphate) via the microbial reduction and dissolution of ferric oxides and the release of tightly bound, co-precipitated PO4

3- (Wright et al., 2001; Zhang et al., 2003). Furthermore, studies have found that the change in soil pH caused by flooding, which generally results in neutralisation, increases the hydrolysis and dissolution of iron and aluminium phosphates (Ponnamperuma, 1972; Wright et al., 2001; Zhang et al., 2003). In line with these findings, some of our laboratory studies resulted in the average total PO4

3- content to be significantly (P<0.05) higher. In contrast to the influence of freshwater flooding on PO4

3- release, saline flooding has the opposite effect in Experiment 2, with PO4

3- tending to sorb onto soil and so become less available (Reddy et al., 1999; Reddy and Delaune, 2008; Jun et al., 2013). Jun et al. (2013) found that 2 and 3 times more PO4

3- was sorbed when soil was exposed to 2 and 5 salinity compared to when exposed to 0 salinity. In line with the theory and the results of other studies, all of the saline flooded treatments, except one (non-control 12oC), were observed to contain a lower average total PO4

3- content than the respective freshwater flooded treatments. The difference was significant (P<0.05) in the control treatments at 12oC. This suggests that saline flooding caused by tidal surges or sea level rise may promote PO4

3- sorption rather than release.

27

3.3 Effects of intervention measures to promote recovery through pre- and post-flooding management – field trials

Prolonged waterlogging can have a serious impact on soil physical and chemical properties, as well as biological processes. In turn, this can have ‘knock-on’ effects on plant yields, thus it is important to understand how to alleviate flood damage in soils. For the most part, drying a flooded soil will remedy most of the negative impacts of flooding (Ponnamperuma, 1984), but some effects are not so easily corrected. For example, nitrate and sulphur can be easily leached out of the soil, macrofauna populations suffer, and soils can become compacted. As such, several methods have been used to try and alleviate flood damage to soils. However, many of these methods can end up exacerbating the effects of flooding by further compacting the soil, particularly if the soil is still too wet to work; i.e. when it is an a plastic state (Spoor, 1975). The effects of cultivating land immediately after flooding has been relatively well researched, in particular the length of time to leave after flooding depending on the type of soil, and what depth or type of machinery is best to use in those early stages (Davies et al., 1972; Spoor, 1975; Batey, 2009). However, little work has been done on alleviating the impacts of flooding on the long-term. The aim of this study was to compare several soil alleviation methods commonly used by farmers over a period of several months on two farms, to monitor the long-term benefits (or disadvantages) of each method. The aim being to provide an insight to the farming community about which method may be the best for alleviating flood damage in the future. 3.3.1 Methods Site selection: Two sites in Somerset were selected in August 2014. The sites were selected because they had both been flooded for a total of 12 weeks and neither field had had any intervention since the flood water had receded in April 2014. The sites were labelled as Site 12 (a grassland site on Curry Moor, a frequently flooded area in Somerset) and Site 16 (another grassland with slightly higher elevation that is not as frequently flooded). Experimental design: The aim of this experiment was to determine the differences between the main types of equipment commonly used by the farming community to alleviate flood damaged soils. All treatments were slot-seeded to reintroduce pasture species, and then four treatments were investigated, each designed to penetrate the soil to a different depth:

Sub-soiler: the deepest treatment. Penetrating to a depth of 12-14 inches, the sub-soiler consists of two tines that dig deep ruts into the soil approximately 2.5 metres apart (Photo 2a).

Sward lifter: the mid treatment. Penetrating to a depth of 8-10 inches, the sward lifter consists of three tines over a width of 2.5 metres, preceded by a row of sharp disks to break up the surface soil and followed by a roller to flatten the turf (Photo 2b). The sward lifter also vibrates as it is pulled through the soil.

Aerator: the shallowest treatment. Penetrating to a depth of 4-6 inches, the aerator consists of several sharp points over a width of 3 metres that roll over the surface of the soil creating several small holes (Photo 2c).

Slot seeding: slot seeded only, with no additional mechanical intervention

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Photos 2a – 2d. The different experimental treatments. (A) the sub-soiler, (B) the sward lifter, and (C) the aerator were used for three of the experimental treatments. (D) shows the sub soiler in use. The experimental plots were set up at the end of August 2014 and slot seeding was completed in mid-September, prior to initial sampling at the beginning of October 2014. The experimental design at each site was identical (Figure 18). There were four experimental areas: sward lifted and slot seeded, sub-soiled and slot seeded, aerated and slot seeded, and slot seeded only, as well as control areas at each end of the experimental area. Four plots were sampled in each experimental area and two plots were sampled in each control area. At one of the two sites, additional samples were taken from the surrounding field, as it had been re-seeded and was thus differed from the control plots.

Figure 18. Experimental design for the amelioration study. The experimental area on each field was approximately 150m long and 10m wide. Each experimental treatment area was approximately 25 x 10m in area and each control area was approximately 10 x 12m in area. Data collection: The plots were sampled every two months in October 2014, December 2014 and February 2015. A final sampling took place in August 2015. Within each plot the following measurements were made:

10

m

25m 12m 25m 25m 12m 25m

Slot Seeded

Only

Sward Lifted and

Slot Seeded

Sub Soiled

and

Slot Seeded

Aerated

and Slot Seeded

Control Control

A B

C D

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Aboveground biomass was measured to determine differences in crop yield or plant productivity between flooded and control areas over time. Plant aboveground biomass was cut down to the soil from within a 20 x 20 cm quadrat at each plot. These samples were then dried at 80°C for 16 hours and weighed to determine the dry weight.

Worm numbers were quantified to determine the health of the worm population. Worm numbers were counted from within a 20 x 20 x 20 cm hole for each plot.

Soil bulk density was measured as an estimate of soil compaction. A 100 cm3 bulk density ring was used to take three samples from each flooded and control area. These samples were weighed, then dried at 120°C for 16 hours before being weighed a second time. Bulk density and soil moisture content was then calculated.

Soil pH was measured to determine changes in soil conditions. Soil samples were taken from each plot and 10 g samples were extracted with 25 ml deionised water. The samples were then shaken thoroughly and left for approximately 4 hours to settle. The pH was then measured using a Hanna pH probe.

Soil electrical conductivity was measured as a proxy for soil salinity to determine changes in soil conditions. Soil samples were taken from each plot and 10 g samples were extracted with 25 ml deionised water. The samples were then shaken thoroughly and left for approximately 4 hours to settle. The electrical conductivity was then measured using a Jenway 4520 conductivity meter.

Soil phosphate (µg g-1) was measured to determine nutrient changes and uptake rates in the soil. Three soil samples were collected from each flooded and control area. Five grams of each sample was extracted with 25 ml of 0.5 molar Sodium Bicarbonate (NaHCO3) buffered to pH 8.5. Phosphate concentration was then determined using the methods from Murphy and Riley (1962): 180µl of Ames reagent and 30µl of 10% ascorbic acid was mixed with 80µl of each sample in a microplate. The colour was allowed to develop for 30 minutes before the absorbance of the samples was read at 820 nm using a Biotek Powerwave XS plate reader and Gen5. Standards were also run alongside the samples; these were used to create a standard curve from which the concentration of each sample was calculated.

Soil nitrate (µg g-1) was measured to determine nutrient changes and uptake rates in the soil. Three soil samples were collected from each flooded and control area. Five grams of each sample was extracted with 25 ml of 0.5 molar Potassium Sulphate (K2SO4). Nitrate concentration was then determined using the methods from Mulvaney (1996) and Miranda, Espey and Wink (2001): 100µl of vanadium chloride, 50µl of NEDD and 50µl of sulphonamide were mixed with 100µl of each sample. The colour was allowed to develop for 15 minutes and the absorbance of the samples was read at 540 nm using a Biotek Powerwave XS plate reader and Gen5. Standards were also run alongside the samples; these were used to create a standard curve from which the concentration of each sample was calculated.

Soil ammonium (µg g-1) was measured to determine nutrient changes and uptake rates in the soil. Three soil samples were collected from each flooded and control area. Five grams of each sample was extracted with 25 ml of 0.5 molar Potassium Sulphate (K2SO4). Ammonium concentration was determined using methods outlined in Mulvaney (1996): 15µl of EDTA, 60 µl of Na-salicylate-nitroprusside reagent and 30 µl of sodium hypochlorite reagent were mixed with 150 µl of each sample. The colour was allowed to develop for 15 minutes before the absorbance was read at 667 nm using a Biotek Powerwave XS plate reader and Gen5. Standards were also run alongside the samples; these were used to create a standard curve from which the concentration of each sample was calculated.

Infiltration rates were measured to determine soil water saturation and infiltration changes over time after the flood water receded. Infiltration rates were measured at each plot using Decagon Devices mini disk infiltrtrometers. The rate of infiltration per minute was measured over 30 minutes and an average infiltration rate was calculated for each plot for each month.

Soil respiration was measured as an indication of soil microbial activity in the soil. Microbial activity is a good indicator of soil conditions, but can also be affected by temperature and soil moisture (Amundson 2001; Pendall et al. 2004) and there can therefore be considerable seasonal changes in soil respiration rates (Bardgett et al. 1997; Bardgett et al. 1999). Soil respiration was measured at each plot using an EGM-4 infra-red gas analyser (PP-Systems 2010).

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Data analysis: The data were analysed as an overall data set (all months, both sites) as well as for the individual sites. The data was square root transformed before the analysis and Euclidean distance dissimilarity matrices were calculated for each analysis. Permutational multiple analyses of variances (PERMANOVAs) were then used to determine differences between treatments (flooded, control), months (n = 7), and sites (n = 5). Partial eta squared effect sized (η2

p) were calculated for PERMANOVA results, where a small effect ≥ 0.0099, a medium effect ≥ 0.0588, and a large effect ≥ 0.1379. When analysing the data by site and by month, an analysis of similarity was used on Bray-Curtis similarity matrix. For these tests, effect sizes were determined by Pearson’s r, where a small effect ≤ 0.1, a medium effect size ≤ 0.3, and a large effect size ≤ 0.5. Pairwise tests were conducted where appropriate to determine where any statistical differences lay. Where significant differences between treatments were found, principle component analyses (PCAs) were used to determine which factor(s) contributed most to the differences. 3.3.2 Results Overall (both sites pooled), there were no significant differences between the treatments but there were

significant differences with large effect sizes between months (PERMANOVA Factor: Month – Pseudo F =

73.62, P(perm) = 0.001, η2p = 0.750) and sites (PERMANOVA Factor: Site – Pseudo F = 9.64, P(perm) p =

0.001, η2p = 0.833). Pairwise tests on months showed that the significant differences lay between October

and February (p = 0.004), October and August (p = 0.003), December and February (p = 0.004), December

and August (p = 0.002), and February and August (p = 0.002). A principle component analysis (PCA) was run

to determine which factors were contributing to these differences. The PCA revealed four components

with eigenvalues exceeding 1 and together they explained 99.5% of the variance. Soil salinity loaded

strongly on the first factorial component, soil phosphate loaded strongly on the second factorial

component, soil nitrate loaded strongly on the third factorial component, and infiltration rate loaded

strongly on the fourth factorial component (Appendix 5. Table 10). Table 10 shows the descriptive statistics

of the factors loading on the four factorial components for both sites and each month.

Soil Salinity (µS cm-1)

Soil Phosphate (µg g-1)

Soil Nitrate (µg g-1)

Infiltration Rate (cm min-1)

Soil Ammonium (µg g-1)

Worm numbers (n)

�̅� SE �̅� SE �̅� SE �̅� SE �̅� SE �̅� SE

Site

12

Overall 621.75 86.77 10.54 1.51 35.09 6.43 3.63 0.78 4.39 0.64 1.78 0.32

Oct ‘14 239.53 14.12 57.48 15.78 21.17 4.93 0.06 0.03 2.66 0.61 0.05 0.05

Dec ‘14 260.76 32.57 61.13 13.02 5.38 1.05 0.17 0.06 11.07 1.83 2.90 0.96

Feb’15 1649.50 219.78 5.56 0.37 1.26 0.64 0.06 0.03 3.96 0.55 1.80 0.55

Aug ‘15 307.20 37.31 0.93 0.13 7.66 0.48 14.24 1.47 1.17 0.07 2.35 0.52

Site

16

Overall 361.18 36.80 39.06 5.98 36.63 2.37 5.42 2.35 2.12 0.22 7.96 0.67

Oct’14 209.22 19.48 68.64 12.67 33.14 3.95 0.41 0.27 2.12 0.69 0.95 0.27

Dec ‘14 205.91 29.18 48.49 11.42 41.86 7.05 0.28 0.05 2.68 0.37 9.15 1.05

Feb ‘15 860.70 60.25 37.09 14.02 44.84 4.32 0.17 0.04 1.42 0.11 7.35 0.53

Aug ‘15 168.90 9.68 0.59 0.10 28.06 2.84 20.82 2.48 1.98 0.13 14.40 1.12


overall data set (both sites, all months).

A separate analysis of Site 12 again showed no significant differences between treatments, but significant

differences between months (PERMANOVA Factor: Month – Pseudo F = 93.47, P(perm) = 0.001, η2p =

0.750). A pairwise test showed that there were significant differences between all month pairings: October

vs. December (p = 0.005), and all other pairings (p = 0.001). A PCA on the data from Site 12 revealed four

components with eigenvalues exceeding 1 and together these explained 99.7% of the variance in the data.

Soil salinity loaded strongly on the first factorial component, soil phosphate loaded strongly on the second

factorial component, soil nitrate loaded strongly on the third factorial component, and soil ammonium and

worm numbers loaded strongly on the fourth factorial component (Appendix 5 Table 11). Table 11 shows

the descriptive factors loading on the four factorial components for each month at Site 12.

When investigating each month separately at Site 12, there were significant differences between

treatments in December, although the effect size was small (p = 0.036, r = 0.192), and a significant

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difference between treatments in August with a large effect size (p = 0.003, r = 0.297). However, there

were no significant differences between treatments in October or February. Pairwise comparisons showed

that the differences in December lay between the ‘Control’ and ‘Slot seeded only’ (p = 0.029, r = 0.563), and

the ‘Sub soiled’ and ‘Slot seeded only’ (p = 0.029, r = 1.000) treatments with large effect sizes, while in

August, the differences lay between the ‘Control’ and ‘Sub soiled’ (p = 0.029, r = 0.625), ‘Sub soiled’ and

‘Aerated’ (p = 0.029, r = 0.656), and the ‘Sub soiled’ and Slot seeded only’ (p = 0.029, r = 0.625) treatments.

A PCA on the December data revealed four components with eigenvalues exceeding 1 and together these

explained 98.6% of the variance in the data. Soil salinity loaded strongly on the first factorial component,

soil phosphate loaded strongly on the second factorial component, and soil nitrate, soil ammonium and

worm numbers loaded strongly on the third and fourth factorial components (Appendix 5, Table 11). A PCA

on the August data revealed only one component with an eigenvalue exceeding 1, which explained 89.8%

of the variance in the data. Soil salinity loaded strongly on this factorial component with a factor loading of

0.995. Table 11 shows the descriptive statistics of the factors loading on the four factorial components for

the three treatments identified in the pairwise comparisons in December and August.

Soil Salinity (µS cm-1)

Soil Phosphate (µg g-1)

Worm Numbers (n)

Soil Nitrate (µg g-1)

Soil Ammonium (µg g-1)

�̅� SE �̅� SE �̅� SE �̅� SE �̅� SE

December

Control 1011.06 320.31 17.81 2.04 1.00 0.47 2.87 1.10 12.90 3.48 Slot Seeded Only 582.13 63.34 143.54 6.97 2.00 1.94 11.94 3.11 5.39 1.72 Sub Soiled 635.56 87.99 20.47 2.88 1.25 0.29 2.47 1.51 13.45 7.44

August

Control 155.75 49.68 1.38 0.35 3.25 1.91 7.18 1.48 1.52 0.19 Slot Seeded Only 228.25 21.25 0.81 0.28 0.50 0.50 6.34 0.39 1.05 0.10 Sub Soiled 458.25 68.53 0.69 0.25 2.75 1.28 7.97 1.55 0.94 0.03 Aerated 370.00 142.75 1.12 0.51 1.25 0.55 7.20 1.09 1.17 0.27


December data for Site 12.

A separate analysis of Site 16 again showed no significant differences between treatments, but significant

differences between months (PERMANOVA Factor: Month – Psuedo F = 115.26, P(perm) = 0.001, η2p =

0.750). A pairwise test showed that there were significant differences between all month pairings (p =

0.001) except October and December. A PCA on the data from Site 16 revealed two components with

eigenvalues exceeding 1 and together these explained 82.3% of the variance in the data. Soil salinity,

infiltration rate and soil nitrate loaded strongly on the first factorial component, and infiltration rate loaded

strongly on the second factorial component. Table 10 shows the descriptive factors loading on the four

factorial components for each month at Site 16.

When analysing each month separately at Site 16, there were significant differences between treatments in

December (p = 0.027, r = 0.199), although the effect size was small. Pairwise tests showed no significant

differences between treatments. There were also significant differences between treatments in February

(p = 0.026, r = 0.169), but again, the effect size was small. Pairwise tests showed that differences lay

between ‘Control’ and ‘Sward lifted’ (p = 0.029, r = 0.406) with a large effect size, between ‘Sward lifted’

and ‘Aerated’ (p = 0.029, r = 0.292) with a medium effect size, and between ‘Sward lifted’ and ‘Slot seeded’

(p = 0.029, r = 0.604) with a large effect size. A PCA revealed three components with eigenvalues exceeding

1 and together they explained 99.2% of the variance in the data. Soil salinity loaded strongly on the first

factorial component, soil phosphate loaded strongly on the second factorial component, and soil nitrate

loaded strongly on the third factorial component (Appendix 5, Table 12). Table 12 shows the descriptive

statistics of the factors loading on the four factorial components for the three treatments identified in the

pairwise comparisons.

32

Soil Salinity Soil Phosphate Soil Nitrate

�̅� SE �̅� SE �̅� SE

Control 2378.13 598.16 80.58 51.02 42.69 3.83 Sward lifted 1627.50 147.74 0.91 0.30 21.43 2.25 Aerated 2455.63 530.33 19.76 11.65 57.14 4.60 Slot Seeded Only 2418.75 152.38 79.64 51.12 63.96 15.83


February data for Site 16.

3.3.3 Discussion Overall there were no differences between treatments, but there were differences between the two sites and over time. These differences were due to soil salinity, soil nutrients, worm numbers and plant biomass. In particular, soil salinity showed a large increase in February, which may have been due to the rising water table. Site 12 became partially flooded again in February, so it is possible that the water table rose to bring salts deposited by the previous year’s flood or through the addition of fertilisers to the surface (Hanson et al., 1999). In addition to the changes in soil salinity, soil phosphate and nitrate decreased over time, perhaps due to continuing plant growth, worm numbers increased in December and remained high in February, and plant biomass showed a general decrease over time at both sites due to seasonal growth rates slowing down. Taking the sites separately, there were not big differences between the treatments, but some trends could be seen. At Site 12, there were differences between treatments in December but these did not persist until February – perhaps because part of the site became flooded again (particularly in the sub-soiled area). Again, in August significant differences were observed. The differences at Site 12 in December were between control and slot seeded and sub-soiled and slot seeded, and in August between control and slot seeded and sub-soiled and slot seeded and aerated. The slot seeded treatment showed higher soil phosphate levels, worm numbers and soil nitrate levels, but lower soil ammonium levels than in control and sub soiled areas, while the control areas showed higher soil salinity than in slot seeded and sub soiled areas. The higher nutrient levels in the slot seeded area may have been due to the plants not taking up nutrients, or perhaps the presence of more worms breaking down organic matter (Zorn et al, 2005; Lavelle et al., 2006). However, there was no evidence of lower plant growth rates and the worm numbers were not considerably higher than in other treatments. This suggests that the elevated nutrient levels could have been due to leaching from an adjacent area. Site 16 showed ‘marginal’ effects of treatments in December; although the main test showed a significant result, the effect size was small and none of the pairwise tests showed significant results. There were more robust statistical differences between treatments in February. The differences lay between sward lifted and control, sward lifted and aerated, and sward lifted and slot seeded. The sward lifted treatment showed lower salinity, lower phosphate and lower nitrate than in control and sward lifted treatments. This is perhaps indicative of greater plant growth rates in the sward lifted area, however there was no long-term impact on biomass growth when quantified in August 2015.

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4. Summarise impacts of prolonged flooding on soil properties and identify potential intervention measures to promote recovery through pre- and post-flooding management (literature review and survey) 4.1. Literature Review Introduction Over recent winters, the UK has been affected by unusually severe winter storms thought to be caused by major perturbations to the Pacific and North Atlantic jet streams (Slingo et al, 2014). In particular, the winter 2013-14 saw exceptional periods of winter rainfall throughout December and January (Slingo et al, 2014), which led to extreme and prolonged flooding in several low lying areas such as the Somerset Levels. Much of the agricultural land in these affected areas were under water for several weeks, some areas as much as 12 weeks. This literature review will address the impacts of prolonged flooding on agricultural soils and ways to alleviate the damage caused by the flooding. Impact on crops Perhaps the most obvious impact of prolonged flooding in agricultural fields is the damage to crops. Soil becomes anaerobic when it is waterlogged, and this has almost immediate effects on vegetation. Within 48 hours, plants begin to suffer from oxygen deprivation (Killham, 2006; Sairam et al., 2008), which causes a significant reduction in nutrient uptake rates (Drew and Sisworo, 2006). If waterlogged or anaerobic conditions persist, several products are produced under waterlogged or anaerobic conditions, such as hydrogen sulphide, acetic acid and butyric acid, which can be toxic to plants for several weeks after flooding (Ponnamperuma, 1972; Koch and Mendelssohn, 1989; Lynch, 2006). Furthermore, denitrification reduces the amount of available nitrogen in the soil (Wijler and Delwiche, 1954), further inhibiting plant growth (Drew and Sisworo, 2006). In fully or partially submerged plants, the gas exchange and the available photosynthetic light are constrained decreasing plant growth (Voesenek et al., 2006) and agricultural productivity (Shaw et al., 2013). When anaerobic conditions prevail in the soil, photosynthesis is altered due to turbid floodwaters that reduced light (Shaw et al., 2013). Furthermore, the continued lack of oxygen results in a decrease of redox potential in the soil (Reddy and De Laune, 2008). Although there are plant species that can tolerate it (Colmer, 2003) and contribute to keep the redox potential above certain values, flood-intolerant species suppress root metabolism and reduce the absorption of nutrients, which also contributed to a decreased plant production (Kozlowski, 1984). Impacts on soil physical properties Extreme flooding can also severely impact soil compaction and structure, especially in fine, clay soils (Jackson, 2004). Soil aggregate stability reduces during long-term flooding as a result of several chemical processes including increase cation exchange and elevated pH (Ponnamperuma, 1972; DeCampos et al., 2009). This reduced stability in the upper layers of soil reduces the likelihood of water draining into the sub soil and thus can affect long-term drainage (Ponnamperuma, 1984). Furthermore, the disaggregation and compaction of surface soils, particularly fine soils, increases the chance of surface capping – the development of a hard, thick cap on the soil surface – which can hinder plant growth and soil drying once the flood water recedes (Bean and Wells, 1953; Ponnamperuma, 1984; Horn et al., 1995). Impacts on soil chemistry The chemistry of the soil can change quite considerably under waterlogged conditions. Nutrients such as nitrogen and sulphate can leach out of the soil, and heavy metals can be deposited (Reddy and Rao, 1983; Burt and Arkell, 1987; Alloway, 1990). In anaerobic conditions, nitrogen mineralisation stops at the ammonium stage because of the lack of oxygen to carry the reaction through to nitrate. As a result ammonium builds up in flooded soils (Ponnamperuma, 1984; Unger et al., 2009). Soil pH usually rises immediately after flooding due to denitrification of nitrate to gaseous nitrogen. This depends on the amount of nitrate in the soil, but it can lead to a soil pH of 6.7 – 7.2 (Alloway, 1990). Also, soil nitrogen can

34

be immobilised by both chemical and biological processes where there is a source of organic carbon; this can make it temporarily unavailable for plant uptake, even after the soil has begun to dry out as it will only be released over time as the microbes decompose (Ponnamperuma, 1984). Specifically, prolonged flooding triggers a release of reduced elements (Schalenghe et al., 2012), soluble carbon and nitrogen (Jones et al., 2009), resulting in a loss of nutrients such as NO3

−, NH3 (Zhong-Cheng et al., 2012) P, an accumulation of Mn+2, Fe+2 and NH4

+, eutrophication in water and important alterations in nutrient cycles (Scalenghe et al., 2012; Brun and Baros, 2013; Shaw et al., 2013). The changes in nutrient cycles includes the production of methane (CH4) and nitrous oxide (N2O) under certain circumstances. Decomposition of organic matter (C source) at low redox potential in soil (<-100 mV, typical in long term flooding) causes CH4 production and emission until the flood water disappears (Hou et al., 2000). After that, the increase of redox potential due to the aeration of the soil allows a release of CO2 instead of CH4 (Zhang et al., 2012) and N2O production as a function of available nitrogen, temperature, soil moisture and carbon (Kim et al., 2010) It is important to mention that soils affected by alternate reducing and oxidizing conditions have a lower reactivity (Scalenghe et al., 2012) because of the precipitation of solubilised oxides as amorphous phases that act as electron acceptors (Schärer et al., 2009) removing organic acids, metal cations and oxyanions from the system. Therefore, extreme flooding may decrease soil quality to a great extent as a function of its frequency and duration. Impacts on soil biology Macrofaunal communities can all but disappear during prolonged flooding due to the lack of oxygen (Satchell, 1980; Plum 2005), and microbial communities change from a diverse aerobic assemblage to a much less diverse and less active anaerobic community that produces methane rather than carbon dioxide (Ponnamperuma, 1984; Freeman et al., 2001; Freeman et al., 2004). The changes in these communities can contribute to changes in soil structure and subsequent aeration, particularly if earthworm activity is significantly affected and channel / burrow formation is reduced (Zorn et al, 2005; Lavelle et al., 2006). The soil microbial community structure is quickly altered because of flooding. Gram positive bacteria are able to bear flood conditions for a longer period that Gram negative bacteria (Guckert el al., 1985), while fungi are too sensitive to these anaerobic environments (Reichardt et al., 2001). In this kind of event, there is normally an alteration in the soil profile of fatty acids that are commonly used as biomarkers for the different taxonomic groups. One of the most described change is an increase in the amount of saturated and cyclopropane fatty acids (Bossio and Scow, 1998), which are used as biomarkers. Alleviation of flood effects on soils Soil management To alleviate the effects of flooding on soils, the changes discussed above essentially need to be reversed. Namely, the soil needs to dry out, nutrients need to be restored and soil structure needs to be improved to facilitate plant growth and further drainage. Drying the soil is the crucial first step and will remedy most of the negative impacts of flooding (Ponnamperuma, 1984). If the soil remains waterlogged and above the upper plastic limit (i.e. the soil is in a ‘liquid’ state, has almost no strength and is readily compacted) (Spoor, 1975), it remains unworkable due to increased chances of wheel slip, rolling resistance resulting in smearing and soil compaction (Davies et al., 1972; Spoor, 1975). Furthermore, if the soil is worked while it is still too wet, there is a risk that severe soil structural damage can occur, particularly clay soils (Davies et al., 1972), in particular, bulk density can increase, water porosity decrease, aggregate stability decrease and the continuity of pores and links to any drainage systems can be damaged (Brady and Wiel, 2008). To help improve drainage, infiltration rates can be emhanced by reducing stocking density on grazed land to reduce soil compaction (Castellano and Valone, 2007), cover crops could be planted or organic matter could be added to the soil to improve soil structure (Angers and Caron, 1998; Franzluebbers, 2002), or cross field ploughing could be used to create rough a surface (Puustinen et al, 2005). Furthermore, increased

35

flow connectivity can be achieved by establishing vegetative buffer zones close to ditches, planting trees along a watercourse, introducing ponds with weeds to hold water during flood events, or clearing, engineering and maintaining ditches (Posthumus et al, 2008). Once the soils are sufficiently dry, heavier machinery can be used to break up the compact soil. Generally in wet soils, ploughing or sub soiling is often preferred as the mechanical disturbance aerates a greater depth (generally more than 20cm) than other mechanical means (Strudley et al. 2008). Other cultivation methods include sward lifters, which aerate the soil to a depth of 20cm, or aerators, which aerate the soil to a depth of around 10cm (Strudley et al. 2008). However, all of these cultivation methods require a tractor to pull the implement through the soil, which can cause compaction both on the surface and at plough depth, depending on the furrows created by each method (Strudley et al. 2008). This can eventually result in a ‘plough pan’, which can then lead to further compaction and reduced drainage in the future (Batey, 2009). However, this issue can be remedied somewhat by waiting for the soil to dry out before using heavy machinery (Spoor, 1975). Crop management While most standard crops and grasslands are not tolerant to flooding, some communities can tolerate flooding to a certain degree. For example, there is evidence that short-term grass leys are more susceptible to prolonged flooding than permanent grassland pastures with mixed swards. Resilient plants that are typically found in more stressful environments such as on the edges of salt marshes or in wetlands will be more resilient to flood damage and perhaps will recover more quickly (Grubb and Hopkins, 1986). Although these plants may not be suitable for agriculture, more diverse plant communities including some more resilient species, will increase the chance of recovery (Lin, 2011) and potentially speed the recovery of the soil after flooding. Another option is breeding flood tolerance in to commercial crops; although this has yet to achieve commercial success, research is being conducted on waterlogging-tolerant crops in Australia (Setter and Waters, 2003). Adaptation Farmers with land on flood plains are at risk of greater frequencies of inundation. Where this is the case, farmers may have adapted their cropping areas to reduce the risk. In this project, a survey has been designed to collate knowledge on what adaptation strategies farmers have taken to reduce the risk of prolonged flooding affecting their businesses. Anecdotal evidence from some of the commercial farmers we engaged with during site selection, was that arable farmers (i.e. farmers with vulnerable crops) were still prepared to grow vulnerable (from flooding) crops in low-lying areas, but that they have managed their risk by limiting the proportion of cropped land area in these parts of the farm, i.e. they were growing a greater proportion of vulnerable crops away from these flood affected areas. Gaps in our knowledge While the effects of short-term soil waterlogging are well understood, it is unclear how this will compare to the effects of extreme and prolonged flooding. If the flood water was several feet deep, it is possible that this would have a profoundly different impact on soil physical and chemical properties, and biological processes. If this water remains for a significant amount of time, as it did in winter 2013-14, perhaps even flood-tolerant crops may not be able to recover easily. Due to the rarity of these extreme floods in the past, relatively little is known of the long-term impacts of prolonged inundation and subsequent recovery. However, with potentially more extreme flood events in the future (Slingo et al, 2014), it is imperative that we understand these impacts and, more importantly, how to mitigate and alleviate the damage they might cause.

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4.2. Farmer survey of perceived relative threats of flooding to their business, and strategies for adaptation to flooding and alleviating flood affected soils 4.2.1 Introduction The UK witnessed unusually severe flooding in the winters of 2012-2013 and 2013-2014, with many areas across the UK remaining underwater for almost three months. Severe long-term flooding of agricultural land can have disastrous impacts on soil quality and subsequent agricultural production. Climate change scenarios predict that extreme weather events such as severe flooding will become more frequent in the future. Evaluating the short- and long-term impacts of flooding on agricultural soils and crops, and assessing the best ways to reduce and alleviate flooding impact on agricultural land, are crucial steps in formulating guidance to the farming community in anticipation of future flood events. Farmer adoption of flood impact alleviation and repair measures (or mitigation measures), is dependent on selecting mitigation methods which are not only effective, but also acceptable to individual farmers for implementation (e.g. they are practical to implement, and economic viable; e.g. Barnes et al., 2010). Additionally, the willingness of farmers to adopt mitigation methods is contingent upon flooding being perceived as a sufficiently severe threat when compared to other demands on the farm business, as well as being a threat which is perceived to be controllable to some extent. The efficacy of measures for repairing the impacts of flooding on agricultural land has been investigated experimentally in other parts of this project (see Section 3). The aim of this survey was to facilitate understanding of how extreme flooding affects farming businesses, and to determine which mitigation methods might be attractive to farmers considering flood impact management. 4.2.2 Methods Survey methods The surveys deployed in this study combined straightforward multiple choice questions, Likert-style scaling techniques, and Best-Worst Scaling (BWS) exercises. Best-Worst Scaling (BWS) is a choice-based approach which elicits extremes of preference along a continuum of interest, in which respondents compare sets of items selected from a pre-defined master list (Auger et al., 2007). In this study, we presented items relating to four separate continuums of interest: (1) The perceived severity of different potential threats (including flooding) to the farm business; (2) The degree of perceived control over these potential threats; (3) The perceived practicality of flood impact mitigation methods when applied before or after flood events; and (4) The perceived effectiveness of flood impact mitigation methods when applied before or after flood events. Respondents were invited to indicate their preferences within each set by selecting the “best” and “worst” items from each set, i.e. the “highest threat” and “lowest threat”, the “most control” and “least control”, the “most practical” and “least practical”, and the “most effective” and “least effective” items respectively. Different combinations of items from the pre-defined lists were presented as sets in a repeated exercise within each survey. By pooling the results of repeated selections for a cohort of respondents, a mean relative preference score for each listed item can be computed on an interval scale (e.g. Jones et al., 2013). Surveys were designed using Sawtooth SSI Web software (Sawtooth Software Inc., Orem, UT, USA), following standard guidelines for optimal survey design (Sawtooth Software, 2013). Detailed descriptions of the functioning and rationale of the BWS method can be found in Auger et al. (2007) and Sawtooth Software (2013). Survey design and data collection An online survey was developed (in consultation with Defra Survey Control Unit), targeted at flood affected farmers in the Somerset Levels and Severn Estuary (see Appendix 2). It comprised four sections: i) demographic (farm/farmer) information, ii) questions to establish how important farmers perceive flooding to be a threat to their business compared to other threats, iii) questions about how farmers may have

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adapted farming practices because of repeated flooding of parts of their land, and iv) questions to gather knowledge of what farming practices farmers have tried in order to alleviate flood damaged soils and crops (i.e. post flooding). The survey was designed with using best – worst scaling. Farmers in the flood affected areas were provided with the weblink to the survey via an email explaining the purpose of the survey and what the information would be used for. We used local NFU and FWAG contacts to distribute the survey to their farmer email distribution lists in September 2014 and again in January 2105. An example of the original survey design used in this study is shown in Appendix 2. Unfortunately, there was an extremely low response, with only two completed surveys, and 10 partially completed surveys. We believe that the low response was due to survey fatigue – this group of farmers have been asked to be part of numerous surveys in the past. The low response rate resulted in us asking Defra for a no-cost extension so we could attempt to obtain farmer responses to similar questions via an alternative route. Local livestock markets were considered as a way of asking farmers to complete the survey face-to-face, but since we were targeting such a narrow group of farmers, this approach did not seem the most cost-effective solution. Instead, we opted to target farmers at the Royal Welsh Agricultural Show (RWAS), the largest show of its type in the UK, attracting >240,000 farmers in 2015. The initial survey was divided into two (examples are shown in the Appendices), following discussions with the Defra contract officer. Both new surveys (a paper-based and a laptop-based survey) asked for farmer demographic information, and information relating to flood history and impacts on the farm. The remainder of each new survey focused on different aspects of the original survey, with the first including BWS “Threats” and “Control” exercises (hereafter referred to as the “BWS survey”), and the second exploring farmers’ use of MMs and their perception of barriers to management (hereafter referred to as the “Mitigation Methods (MM) survey”). Both new surveys were deployed by face-to-face interview at the Royal Welsh Agricultural Show in July 2015. Lists of options for all BWS exercises were drawn up by the project team, then used to populate the BWS exercises (Appendix 3). In all cases, respondents were presented with five sets of five item combinations for evaluation. Table 13 lists the items used to populate the “Threats” and “Control” exercises used in BWS survey.

Number Item

1 Extreme flooding 2 Severe drought 3 Pests and diseases 4 Increase in equipment costs (costs of replacements) 5 Severe frost or snow 6 Rising energy costs 7 Increase in fertiliser, herbicide and seed prices 8 Reduction in subsidies 9 More restrictive labour laws (e.g. working time directive, immigration policy)

10 Inadequate access to crop nutrient sources

Table 13. Items used in the “Threats” and “Control” Best-Worst Scaling exercises Data analysis Mean BWS scores were computed using the recommended method and default parameters described in Sawtooth Software (2013). Non-parametric comparison of mean scores between demographic sub-groups was achieved following the procedure detailed in Jones et al. (2013).

4.2.3 Results Response rate By May 2015, only two respondents had provided complete information for the original online questionnaire. Of the farmers approached to complete the two later surveys, up to 48 respondents

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provided enough information through the BWS survey to allow for subsequent data exploration, and two respondents provided complete information in the MM survey. Best Worst Scaling survey results Demographic characteristics and flood history The demographic characteristics and flood history of farmers who completed the BWS survey are presented in Figure 20, and Tables 14 and 15. Most respondents completing both the “Threats” and “Control” exercises were male, aged 45 or over, and farm owners (Table 14). The majority of respondents farmed ruminant livestock, on medium- to large-sized farms (greater than 50 ha in size, with over a quarter farming on more than 200 ha of land). Farm locations were primarily distributed across Wales, the Welsh Borders and Somerset (Figure 19). Approximately three quarters of respondents had not been flooded in the past decade (Table 15.; Figure 20). The remaining respondents had most commonly experienced flooding more than four times in the past decade, and usually since 2012. Although over half of those flooded had only experienced flooding on < 10 ha of land during their most recent flooding event, around a quarter of flooded respondents had experienced flooding on > 50 ha of land. On a percentage basis, most farms had experienced submersion of more than 5% of the total farm area, with more than 50% submersion on over 15% of farms. While more than 45% of respondents were flooded for < 1 week, over 40% of farms were flooded for > 3 weeks, in one case for an estimated 5 months (data not shown). Overall, two thirds of those flooded had experienced extreme flooding (Figure 20), where the definition of “extreme flooding” is taken as “flooding on more than 5% of your farm and/or a period of more than three weeks”. No. respondents (%)

Category Response “Threats” exercise “Control” exercise

Gender Male 34 (70.8%) 30 (69.8%) Female 14 (29.2%) 13 (30.2%) Age (yrs) 18-34 3 (6.3%) 3 (7.0%) 35-44 6 (12.5%) 5 (11.6%)

45-54 15 (31.3%) 13 (30.2%) 55-64 11 (22.9%) 9 (20.9%)

65 + 13 (27.1%) 13 (30.2%) Job

1 Owner 38 (79.2%) 32 (74.4%)

Manager 16 (33.3%) 13 (30.2%) Worker 12 (25.0%) 10 (23.3%) Other 5 (10.4%) 7 (16.3%) Farm type Lowland dairy 7 (14.6%) 8 (18.6%)

Lowland beef and/or sheep 12 (25.0%) 12 (27.9%) LFA dairy 5 (10.4%) 5 (11.6%) LFA beef and/or sheep 13 (27.1%) 10 (23.3%) Specialist poultry 0 (0.0%) 0 (0.0%)

Specialist pigs 0 (0.0%) 0 (0.0%) Cereals 0 (0.0%) 0 (0.0%) Horticulture 0 (0.0%) 0 (0.0%) General cropping 0 (0.0%) 0 (0.0%) Mixed 8 (16.7%) 7 (16.3%)

Other 3 (6.3%) 1 (2.3%) Farm area (ha) < 20 5 (10.4%) 1 (2.3%) 20 to 49.9 5 (10.4%) 4 (11.6%) 50 to 99.9 14 (29.2%) 13 (30.2%)

100 to 199.9 9 (18.8%) 8 (23.3%) 200 + 14 (29.2%) 13 (30.2%) Unknown 1 (2.1%) 1 (2.3%)

Table 14. Demographic characteristics of respondents completing the “Threats” & “Control” BWS exercises 1 Selection of multiple categories was permitted.

Clay was the most commonly affected soil type during flood events, and older pasture (> 5 years) the most typically affected crop type (Table 15). Reported physical impacts of flooding on agricultural land differed between respondents completing “Threats” exercises and those completing “Control” exercises, and a

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greater proportion of all impacts were reported as “Very serious” or “Serious” by “Threats” exercise respondents than “Control” exercise respondents (Figure 15). Farmers completing “Threats” exercises stated that soil erosion and loss of crops were the most serious effects of flooding, while farmers completing “Control” exercises specified domestic infrastructure loss as the most serious impact.

Figure 19 Postal area locations of farms for respondents completing “Threats” and “Control” BWS exercises. Respondents who completed both the “Threats” and “Control” BWS exercises are represented by a square; “Threats” only, by a circle; and “Control” only, by a triangle. Respondents who had experienced extreme flooding on their land in the past decade are represented in dark blue; less severe flooding, in light blue; and no flooding, by an open shape.

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No. respondents (%)


Frequency (2005 to 2015) None 36 (75%) 32 (74.4%) 1 3 (6.3%) 3 (7.0%) 2 2 (4.2%) 2 (4.7%)

3 1 (2.1%) 1 (2.3%) 4 2 (4.2%) 1 (2.3%) > 4 4 (8.3%) 4 (9.3%) Last flooded (year) Never flooded 36 (75.0%) 32 (74.4%)

Before 2005 1 (2.1%) 1 (2.3%) 2005 0 (0.0%) 0 (0.0%) 2006 0 (0.0%) 0 (0.0%) 2007 0 (0.0%) 0 (0.0%) 2008 1 (2.1%) 1 (2.3%)

2009 0 (0.0%) 0 (0.0%) 2010 0 (0.0%) 0 (0.0%) 2011 0 (0.0%) 0 (0.0%) 2012 4 (8.3%) 4 (9.3%)

2013 3 (6.3%) 4 (9.3%) 2014 1 (2.1%) 1 (2.3%) 2015 2 (4.2%) 0 (0.0%) Flooded area (ha) < 10 8 (66.7%) 6 (54.5%)

10 to 19.9 1 (8.3%) 1 (9.1%) 20 to 49.9 0 (0.0%) 1 (9.1%) > 50 3 (25.0%) 3 (27.3%) Flooded area (% of farm) < 5 5 (41.7%) 4 (36.4%)

5 to 9.9 1 (8.3%) 1 (9.1%) 10 to 19.9 2 (16.7%) 2 (18.2%) 20 to 49.9 2 (16.7%) 2 (18.2%) 50 + 2 (16.7%) 2 (18.2%)

Main soil type flooded Clay (heavy) 8 (66.7%) 9 (81.8%) Loam 3 (25.0%) 2 (18.2%) Sand (light) 1 (8.3%) 0 (0.0%) Peat / peaty 0 (0.0%) 0 (0.0%)

Crops flooded 1

Cereal 1 (8.3%) 1 (7.7%) Root crop 1 (8.3%) 1 (7.7%) Pasture (1-2 years) 0 (0.0%) 0 (0.0%) Pasture (2-5 years) 2 (16.7%) 2 (15.4%)

Pasture (> 5 years) 11 (91.7%) 10 (76.9%) Other 1 (8.3%) 0 (0.0%) Flood duration (weeks) < 1 6 (50.0%) 5 (45.5%) 1 to < 2 1 (8.3%) 1 (9.1%)

2 to < 3 0 (0.0%) 0 (0.0%) 3 + 5 (41.7%) 5 (45.5%) Extreme flooding

2 Yes 8 (66.7%) 8 (72.7%)

No 4 (33.3%) 3 (27.3%)

Table 15 Flood history of respondents completing the “Threats” and “Control” BWS exercises 1 Selection of multiple categories was permitted.

2 This variable combines responses to Flood area and Flood duration

questions to determine whether respondents’ land was subject to “extreme flooding”, defined in this survey as “flooding over more than 5% of your farm and/or a period of more than 3 weeks”.

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Figure 20 Physical impacts of flooding on agricultural land, reported by respondents completing the “Threats” (a) and “Control” (b) BWS exercises. Respondents were asked to state the effects of flooding as “Very serious”, “Serious”, “Moderately serious”, “Slightly serious”, or “Not at all serious” (represented in dark blue through to white bars respectively). Multiple responses were permitted within the “Other” physical impacts category.

Best-Worst Scaling exercises Mean scores for the perceived degree of threat, and perceived degree of control over threats, are compared in Figure 22. The zero-axis relating to each continuum of interest represents the overall mean of all ten items; positive scores represent above-average scores, and negative scores represent below-average scores. When averaged across all respondents, flooding was considered to pose the lowest threat to farm businesses compared to other potential threats; farmers also considered that they had a relatively low degree of control over the occurrence of flooding (item 1, Figure 22). Severe drought (item 2) also fell within this category, but with higher perceived degree of threat and a lower perceived degree of control. Items considered to pose an above-average threat to farm businesses, over which farmers felt they had little control, were items 5 (Severe frost and snow) and 8 (Reduction in subsidies). A reduction in subsidies was considered to pose the highest threat to farm businesses, and was the threat that farmers felt they had the least control over. Farmers perceived the remaining items listed in Table 14 as potential threats that they had an above-average degree of control over, although the items varied from being perceived as posing a relatively low threat (items 3, 6 and 7), to a relatively high threat (items 4, 9 and 10). To further understand variability in farmer opinion relating to threats and control, comparisons of mean scores were made between different groups of respondents. No significant differences in mean scores were found relating to degree of threat, or degree of control, for different categories of gender, age, job, farm type or farm size (data not shown). Only the degree of experience of flooding significantly influenced farmers’ perception of flood threat and control (Table 16). Bonferroni-corrected Mann-Whitney U tests revealed a significantly greater perception of flood threat in farmers who had experienced flooding more frequently in the past decade (4 times or more) than those who had not been flooded in the previous decade (p < 0.025; Table 16). Similarly, a significantly greater perception of threat was expressed by farmers who had been flooded recently (2013 or 2015) compared with those who had not experienced flooding in the past decade (p < 0.025; Table 16). The only variable to affect significant differences in the perception of both threat and control in the farmer cohort was that of extreme flooding (flooding over more than 5% of farm land and/or for more than three

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weeks duration), with farmers who had experienced extreme flooding giving a significantly higher score for degree of threat, and a significantly lower score for degree of control, than farmers who has experienced less serious flooding or no flooding at all (p < 0.05; Table 16).

Figure 21 Zero-centred scatter plot of relative mean threat and control scores for the ten items listed in Table 2.1. Numbers attached to data points represent item numbers listed in Table 2.1.

Mean (median) score, (n): Extreme flooding


Frequency (2005 to 2015) None 1.20 (0.30) (36) ns 1 2.13 (0.41) (3) ns

2 16.35 (16.35) (2) ns 3 0.90 (0.90) (1) ns 4 24.47 (24.47) (2) ns > 4 16.10 (14.68) (4) ns

Last flooded (year) Never flooded 1.19 (0.24) (36) ns Before 2005 0.41 (0.41) (1) ns 2008 13.01 (13.01) (1) ns 2012 2.52 (1.92) (4) ns

2013 25.29 (24.09) (3) ns 2014 20.91 (20.91) (1) ns 2015 16.64 (16.64) (2) ns Extreme flooding

1 Yes 14.05 (14.07) (8) 4.44 (0.94) (8)

No 2.10 (0.37) (40) 7.24 (4.57) (35)

Table 16 Mean (and median) respondent scores relating to the perceived degree of threat from, and perceived degree of control over, extreme flooding (“Threats” and “Control” BWS exercises). Scores are on a ratio scale of 0-100. Results are shown only where one or more statistically significant differences were found within the grouping variable. 1 This variable combines responses to Flood area and Flood duration questions to determine whether respondents’ land

was subject to “extreme flooding”, defined in this survey as “flooding over more than 5% of your farm and/or a period of more than 3 weeks”.

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Mitigation Methods survey results Only four respondents completed questions concerning measures they had used to prevent or repair flood damage, and questions related to perceived barriers to flood impact management. Despite this, a few general observations can be made with regards to these topics. An example of the Mitigation Methods survey is shown in Appendix 4. In terms of both flood damage prevention and repair, all listed potential MMs had been attempted by at least one farmer, with the exception of “Moving livestock away from flood prone areas when soil is wet” and “Re-introduction of worms” (Table 17). A single farmer had additionally attempted “Planting willow trees to stabilise river banks” as a stated prevention measure, and “Picking rubbish” and “Pestering the Environment Agency to better regulate the dam” as stated repair measures. All farmers but one had attempted multiple methods for reducing flood risk before flood events or reducing impact after flooding (data not shown), with all respondents attempting a greater selection of flood impact repair practices compared to flood impact prevention practices. The most commonly stated barrier to management was “Planning or environmental constraints” (75% of respondents; Table 17). Half of the four respondents stated multiple barriers to flood impact management, with one including all eleven options (data not shown). Additional barriers to management stated by respondents included “Other people doing nothing”, “No real support available”, “Flooding not on a large scale”, and “Environmental designations and schemes (e.g. ditch blocking; wildlife habitats) conflict with flood management”. Category Response Respondents

(%)

Damage prevention

1 Sow crops with low flood tolerance in safe areas not at risk of flooding, and grow flood tolerant crops where flooding has occurred previously

1 (25.0%)

Restrict flood areas to permanent grassland 1 (25.0%) Use soil equipment to improve aeration (e.g. sward lifter, aerator) 1 (25.0%)

Improve drainage of areas prone to flooding (e.g. ditch management or ‘guttering’) 2 (50.0%) Building levees or embankments 1 (25.0%) Creation of ponds, wetland areas, or waterlogged areas 0 (0.0%)

Moving livestock away from flood prone areas when soil is wet to avoid soil compaction

2 (50.0%)

Using low-ground-pressure tyres in flood prone areas to reduce soil compaction 2 (50.0%)

Do nothing 1 (25.0%) Other (please specify) 1 (25.0%)

Damage repair 1

Use of a soil aerator (spiker or slitter, typically operating to 10cm depth) 1 (25.0%)

Use of a sward (grass) lifter (typically operating at 20-25cm depth) 2 (50.0%) Use of a sub-soiler (typically operating at 35-50cm depth) 1 (25.0%)

Cultivation with a rotavator prior to re-sowing 1 (25.0%) Conventional ploughing and cultivation practices prior to re-sowing 1 (25.0%) Use of slot seeding with minimal or no cultivation 1 (25.0%) Nutrient applications (e.g. fertiliser or manure) 2 (50.0%)

Application of soil conditioner (e.g. lime) 2 (50.0%) Ditch or drainage improvement 2 (50.0%) Weed control 2 (50.0%) Re-introduction of worms 0 (0.0%) Done nothing 1 (25.0%)

Other (please specify) 1 (25.0%) Management barriers

1 Unpredictable nature of flood events 2 (50.0%)

Cost of labour 2 (50.0%)

Availability of contractors 1 (25.0%) Soil type 1 (25.0%) Cost of equipment 1 (25.0%) Time constraints 2 (50.0%)

Availability of funding and support 2 (50.0%) Availability of advice 1 (25.0%) Farm position in catchment area 2 (50.0%) Planning or environmental restraints 3 (75.0%)

Other (please specify) 2 (50.0%)

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Table 17. Responses to questions concerning flood damage prevention and alleviation or repair, and perceptions of barriers to management.1

Selection of multiple categories was permitted.

4.2.4 Discussion This survey aimed to facilitate understanding of how extreme flooding affects farming businesses, and to determine which MMs might be attractive to farmers considering flood impact management. While the response rate of flood-impacted farmers was low for both the online survey and subsequent face-to-face interviews, which was probably the result of ‘survey-fatigue’ for these farmers, it is possible to extract some useful information from the data available, which might be used as a basis for further investigation. Farmer demographic characteristics observed in this survey are similar to those found in comparable geographic areas (e.g. the most recently published national agricultural surveys in Wales and England; DEFRA, 2015); consequently, the views of the farmers interviewed here might be considered to be broadly representative of the wider farming community. Two thirds of respondents who had experienced flooding on their land had been exposed to “extreme flooding” (defined in this study as flooding over more than 5% of farm land and/or for more than three weeks). Typically these farms were characterised as lowland or LFA cattle and sheep holdings, with flooding occurring on slowly permeable (clay) soils, under older pasture. Although farms affected by “extreme” flooding tended to have experienced submersion of a large area of land, this was usually not for more than a few days. Despite this short duration of flooding, many respondents specified “Serious” or “Very serious” impacts, most frequently in relation to soil erosion, crop loss and domestic infrastructure damage. Several farmers additional listed ‘waste and debris’ as a serious or very serious impact. This illustrates that even over a short time period, flooding can have serious impacts on agricultural land and holdings. The BWS survey results indicated that on average, flooding was considered by the farming community to pose a relatively low threat to farm businesses when compared to other potential threats. However, respondents who had been frequently or recently affected by flooding expressed a significantly greater feeling of threat from flooding than those who had not. Within the flooded farmer cohort, differences between demographic groups were not explored statistically, as sub-groups were too small to allow for statistically meaningful comparisons. Future studies would benefit greatly from increasing response numbers within this group, to enable exploration of the degree of consensus within the community of flood-impacted farmers, and to identify factors contributing to differences in opinion. While the authors recognise that the very low response rate to the MM survey means that no firm conclusions can be drawn regarding flood impact prevention and repair, these data nevertheless illustrate that almost all of the MMs have been trialled by at least one farmer. It is not possible to determine from the data here whether the two MMs that were not selected by this small cohort are less desirable options for the majority of farmers, or whether some farmers in a larger sample would select these options. Other recent studies have revealed significant differences in preferences between different groups of farmers for implementing different MMs against environmental damage (e.g. Jones et al., 2013), and it is likely that variability would similarly be found in relation to flood impact MMs. Potential adoption rates for MMs are known to vary with farmer attitude, the extent of available financial and other support, and perception of barriers to implementation (e.g. Smith et al., 2007; Hyland et al., 2014). Encouragingly, one farmer had implemented additional MMs not included in the survey list, suggesting that some farmers might be willing to explore a wide range of strategies for adapting to or mitigating flood damage. Future work could expand on work presented here by exploring farmer attitudes, incentives and constraints to effective flood risk management, within the wider context of flood risk management at the catchment scale.

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5. Summary conclusions The project has demonstrated that flooding can result in large quantities of soil organic nitrogen being mineralised to NH4

+ during the inundation period. This is despite the development of what appear to be anoxic conditions. During the period, the anaerobic conditions inhibit nitrification of this NH4

+ to NO3-.

Hence, unless there is NO3- in the soil at the time of flood inundation, then it is not formed in the soil until

sufficient O2 diffusion occurs into the soil to promote nitrification. This does not occur until the soil starts to dry out. Any NO3

- in the soil at the time of flood inundation can be denitrified, so soil concentrations are reduced. We also found evidence that prolonged flooding can result in elevated ammonia emissions. Although we did not quantify it, the large pool of soil NH4

+ (and subsequent soil NO3-) would be available for

any recovering crop, or newly planted crop – so the fertiliser N requirements of crops on recently flood inundated soils may be met to some extent by this pool of soil mineral N, and the soil N supply from recently flooded soils should be considered as part of the fertiliser management plan in such instances. Greenhouse gas fluxes were directly affected by changes in soil aeration status. For example, N2O emissions during flooding were the result of denitrification of NO3

- already in the soil at the time of flood inundation. After this peak in emission, N2O fluxes remained low (as there is no source of NO3

-) until the flood water ‘receded’ and nitrification was stimulated by O2 diffusion. Hence, we observed evidence of increased N2O fluxes after the soil started to dry out and the large pool of soil NH4

+ could be microbially transformed into NO3

-, a source of available N for subsequent denitrification. Methane fluxes were largest during the flood inundation period, and reduced after the flooding had ‘receded’. Interestingly, ammonia emissions were observed during the flooding period in one of the laboratory experiments, presumably as a result of the high NH4

+ soil water and flood water concentrations. Sampling on commercial farms immediately after flood waters had receded showed clear differences in soil salinity between flooded and non-flooded areas, presumably the result of nutrients in the flood water. Earthworm numbers were reduced by flooding, but numbers recovered within 3-9 months. Plant biomass of some crops was significantly reduced as a result of prolonged flooding, especially for root crops and recently reseeded pastures. Permanent grassland swards were less affected.

6. Recommendations for Further Research 1. We recommend that tall tower atmospheric concentrations of N2O, CH4 and NH3 are explored for the

periods during and after prolonged flood events, to see if there is a relationship between flooding and gaseous emissions.

2. With increased frequencies of flooding predicted, the effects of repeated flooding on soil properties and the resilience of these properties to recover should be explored.

3. Temperature has a direct effect on the rates of soil processes. With increasing frequencies of summer flooding, we need to determine relative rates of e.g. GHG production, Fe solubility, N mineralisation rates from soils subject to summer vs winter floods. Crops are at different stages of growth in winter and summer, so impacts on direct crop production are likely to be different too.

4. Whilst we have focussed on the impacts of one extreme weather condition, i.e. heavy rainfall and prolonged flooding, summer droughts are also likely to increase in frequency. We have little knowledge of how combinations of these extremes impact on soil properties and function when they interact in short spaces of time, i.e. within the same year. Such interactions should be explored.

7. Project Legacy Bangor University has recently been awarded a Ser Cymru NRN – LCEE Cluster Project, Climate-smart grass: A strategy for grassland to safeguard forage production against extreme weather events through resilience

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to multiple-stresses. This project will address how soil and pasture species respond to repeated flooding, compare winter/spring and summer flooding, and the combined impacts of prolonged flooding, drought and elevated atmospheric ozone concentrations. The project will evaluate the resilience of different grass swards to these stressors. Other project collaborators include CEH Bangor and IBERS. Bangor-Extreme, a flood/drought experimental facility has been constructed to simulate winter & summer flooding and summer drought on a permanent grassland sward at Henfaes Research Centre, Bangor University’s farm. A PhD studentship, based at Bangor University, has been advertised via the NERC CDT programme, STARS ( ), to determine the effects of extreme weather events on greenhouse gas production and emission from agricultural soils. The studentship is collaborative between Bangor University, Rothamsted Research (North Wyke) and The Met Office.

8. Project outputs Presentations

HRH Princess Anne, at Henfaes Research Centre (February 2015)

China – Institute of Subtropical Agriculture (Chinese Academy of Sciences) Papers

Long term flooding effects on soil and water quality, greenhouse gas emissions and microbial structure in grassland soil (In advanced preparation)

Impacts of saline and freshwater flooding on soil nutrient dynamics and greenhouse gas fluxes (in preparation)

Posters

Royal Welsh Agricultural Show, Builth Wells, Wales. 2015 Website

http://agrisoils.bangor.ac.uk/index.php.en Farming Press

Feature article for the British Grassland Society Publication, Grass and Forage Farmer. Autumn 2015 Edition.

Flood affected soils and earthworms – Prof Davey Jones interview in the Western Morning News, http://www.westernmorningnews.co.uk/Worms-flown-Somerset-Levels-replenish-soil/story-21028279-detail/story.html

Acknowledgements The authors are especially grateful to the farmers who allowed sampling of their fields during the recovery period post 2013/2014 flooding, and the two farmers that provided land for the alleviation plot studies. We also thank the farmers that participated in the survey. Special thanks also go to Anne Langford (FWAG SW) and Matt Shepherd (Natural England) for arranging the emailing of the link to the online survey to farmer groups; to Rachel Lewis-Davies (NFU Cymru) and colleagues for hosting researchers at the Royal Welsh Agricultural Show (2015); and to Lara Pritchard for assistance with the survey.

http://agrisoils.bangor.ac.uk/index.php.en

http://www.westernmorningnews.co.uk/Worms-flown-Somerset-Levels-replenish-soil/story-21028279-detail/story.html

http://www.westernmorningnews.co.uk/Worms-flown-Somerset-Levels-replenish-soil/story-21028279-detail/story.html

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legacy effects of extreme flood events on soil quality and

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