Agroforestry impact on soil health – a soft systems model representing current knowledge

Keith Shepherd, World Agroforestry Centre (ICRAF), Nairobi, Kenya (k.shepherd@cgiar.org)

Introduction

This is a soft systems model that represents the author’s knowledge from scientific literature and field experience of the differential impact of agroforestry systems on soil health, implemented as a Bayesian Network (BN). Optimal decisions are made by combining available data and expert knowledge – this is what BNs allow you to do; and all data and knowledge is subjective – even the way we plan experiments, select data, analyse it, and report it; including the way we select literature for our review. At the end of the day (within the context of this review) practitioners want to know to what degree agroforestry will benefit soil health so that they can evaluate trade-offs.

To run the model you need to download and install a free version of AgenaRisk.

Model description

A BN is an explicit description of the direct dependencies between a set of variables. This description is in the form of a directed graph and a set of node probability tables (e.g. Fenton & Neil, 2012). BNs are particularly useful for combining limited data with expert judgement. Our BN model represents the differential on-site benefits of alternative agroforestry systems on soil health at farm or landscape scale compared to a situation with no trees. In most agricultural landscapes some trees are already present and so the benefit will be proportional to the degree of agroforestry intensification. We consider that the increase in benefit relative to no agroforestry will be fairly neutral across rainfall regimes or levels of potential productivity.

We consider the impact of agroforestry on soil health mediated through five major functions: (i) organic input from above- and below-ground sources, (ii) biological nitrogen fixation (BNF), (iii) deep uptake and recycling of nutrients from below the crop root zone, (iv) filter functions of trees by providing water infiltration sinks and barriers to overland water and sediment flows, and (v) protection of the soil surface by litter cover (Fig. 1). Various environmental factors condition these functions: soil phosphorus status conditions BNF, subsoil fertility level conditions deep uptake, and slope amplifies the filter and soil cover effects. BNF and deep uptake functions combine to determine organic input quality. The amount of organic inputs and their quality combine to determine soil organic related benefits. Filter efficacy and soil cover together confer soil protection benefits. Soil organic benefits and soil protection benefits determine overall soil health benefits The arcs from the functions to the health benefits synthesise a number of intermediate effects (Table 1). The direct benefits node indicates the relative size of the economic benefit expected from produce (timber, poles, fodder, fruits, etc) under the different practices. Users can evaluate the effect on soil health of a single agroforestry practice or a combination of practices and under specified environmental conditions. For rotational systems the benefits are assumed to be time-averaged.

Fig. 1. Bayesian Network model of the relative impact of agroforestry practices on soil health and on direct economic benefits. Agroforestry impacts on five soil functions, modified by environmental conditions. Organic and soil protection benefits are synthesised into an overall soil health benefit.

Table 1. Intermediate soil health benefits from functions of trees

Function Benefits

Organic input quantity/quality Nutrient supply amount and continuity

Water and nutrient holding capacity

Infiltration rate

Resistance to capping and wind and water erosion

Carbon sequestration

Biological diversity and functional capacity

Biological N fixation Nitrogen supply

Slow N release

Deep uptake Uptake of additional nutrients from deep soil layers

Recycling of nutrients that would otherwise been leached

Filter Water infiltration sinks

Reduced overland water flow and soil erosion

Habitat for soil fauna and flora

Soil cover Soil protection from raindrop impact

Reduced evaporation from soil

Habitat for soil fauna and flora

Node descriptions

Each node in the BN represents the probability of being in the low, medium or high range for a variable, scaled from 0 to 1 in even intervals. The definition of the levels are given in Fig. 2, which shows the risk table view where users can enter probabilities for each level of the input variables. You can enter soft evidence for agroforestry practices to represent proportional area under different practices.

Nodes that are conditioned on other variables have probability tables whose values are specified manually or using a function, based on the author’s knowledge and interpretation of the agroforestry literature (e.g. Figs. 3 & 4). Most of the nodes that have parents have their node probability tables specified using a function based on specifying the mean and variance of a double-truncated normal distribution (i.e. constrained between 0 and 1).

The probability tables or equations can be accessed by selecting a node in the Risk Map view and then clicking on the Sigma button in the toolbar. The uncertainty in the nodes represents all the factors that are not included in the model (e.g. variation within agroforestry practices due to differences in arrangements, species and management; variation in responses under different site conditions due to other environmental factors).

Improved soil health is expected to impact on a number of ecosystem services, including improved productivity, increased efficiency of water and nutrient use, reduced production risk (e.g. crop failures in dry years, yield decline when farmers may not be able to apply nutrients, soil erosion risk), as well as a series of off-site environmental impacts (e.g. flood damage, siltation of reservoirs, eutrophication of water bodies). There are likely to be thresholds not represented in this model, for example in situations of low soil organic matter, the benefit of agroforestry will be from build up organic matter levels, but at already high organic matter levels the benefit will one of maintenance.

Fig. 2. Definition of levels of the input nodes.

Fig. 3. This is an example of a node probability table with the probability values entered manually.

Fig. 4. These are examples of the use of weighted functions. For the soil organic benefits node, the mean is a simple weighted mean of organic input and organic quality, with even weighting. The variance level given expresses a moderate level of uncertainty over the relationship – if set very high then the results will be very insensitive to changes in the parent nodes, but if set very low it would almost perfectly mirror the parent node behaviour. For the BNF adjusted node, the mean is a weighted minimum of soil P level and the BNF level without constraint but weighted produce the behaviour that if P is high then BNF level is similar to the unadjusted levels, but if P is low then there is high probability that BNF is low.

Model results

Various scenarios are illustrated in the following figures.

Fig. 5. This is a scenario with scattered trees in fields only, with low soil P status, soil subsoil fertility and low slope. There is a strong probability that soil health benefits will be low and no chance of high benefits.

Fig.6. This is a scenario for improved fallows with low soil P status, low subsoil fertility and low slope. There is a strong probability of achieving moderate soil health benefits. Benefits are held back by low BNF due to low P status and the soil protection benefit is limited on low slopes.

Fig. 7. This is a scenario for improved fallows with high soil P status, high subsoil fertility and steep slope. There is a moderately strong probability of achieving large soil health benefits and little chance of achieving low benefits. Note that there is still considerable uncertainty on whether benefits will be high or moderate.

Fig. 8. This scenario is a farm or landscape with mixed proportions of agroforestry practices and favourable soil status with steep slope.

Overall, the model results (Fig. 9) demonstrate that tree density and time-averaged area under trees bound the potential relative impact of agroforestry on soil organic and protection benefits. Systems confined to boundaries or small areas of the landscape, or scattered trees in cropland do not provide large organic benefits on a whole farm or landscape basis but do provide significant soil protection benefits due to the filter function. Greatest overall soil health benefits derive from systems that maximise the area under dense cover of nitrogen-fixing trees (e.g. fodder banks) when soil P status is favourable. Large soil benefits are difficult to achieve under mixed systems, typical of tropical smallholder farming systems because of the trade-offs between the land area under trees versus crops. Soil health benefits in mixed systems are moderate under conditions of fertile soils on steep slopes but lower when these conditions are not present, but fertile soils do not occur widely on steep slopes. However, the filter function of agroforestry systems will have a significant off-site benefit in all areas with moderate to steep slopes.

Fig. 9. Fig. 4. Expected probability values for level of organic benefit, soil protection benefit, overall soil health benefit, and direct benefit from produce for different agroforestry systems relative to equivalent systems without trees. The systems are rotational woodlots (Wood), improved fallows (Fallow), multistrata (Multi), fodder bank (Fodder), boundary planting (Boundary), live fences (Fence), hedgerow intercropping (Hedges), scattered trees in cropland (Park), mixed system dominated by scattered trees (Mix 1), and mixed system dominated by fodder bank (Mix 2).

Conclusion

Future work should give priority to quantifying our uncertainty in agroforestry performance. We recommend constructing probability distributions on the different costs and benefits of agroforestry and conditioning relationships based on available data and expert knowledge. Value of information analysis (e.g. Hubbard, 2014) can then be used to efficiently guide where further information to narrow uncertainties will most improve our ability to predict what agroforestry practices under which conditions can maximize overall benefit.

References

Fenton N and Neil M. 2012. Risk assessment and decision analysis with Bayesian networks. Boca Raton, Fl, USA: CRC Press.

Hubbard D. 2014. How to measure anything. Finding the value of “intangibles” in business. 3rd edition. Hoboken, NJ, USA: John Wiley & Sons, Inc.