Download - Assessment of the possible drought impact on farm production in the SE of the province of Buenos Aires, Argentina

Assessment of the possible drought impact on farm production in

the SE of the province of Buenos Aires, Argentina1

Tomas Hartmann*, Carlos Di Bella, Patricio Oricchio

Instituto de Clima y Agua, INTA-Castelar-Las Cabanas y Los Reseros S/N-(1712) Buenos Aires, Argentina

Received 8 April 2002; accepted 21 October 2002

Abstract

The extended drought situation in the southeast of Buenos Aires during the second half of year 2000 caused the government

to invoke emergency laws. This action allowed farmers in the area to receive waivers for taxes and loans. The emergency laws

remained in force during 2001, without further verification of environmental conditions for agriculture. Developing an

assessment of the actual drought situation was relevant for taxing and national credit institutions.

An assessment was performed of the actual drought situation of farms during the spring of 2001 in seven counties in Buenos

Aires Province area. The assessment was done by comparing vegetation index values (NDVI)—as measured from NOAA-

AVHRR satellite data—of September 2001 against NDVI time series values from previous years. Five categories were

established to describe the relationship between the present index and the average of the time series. Farms within the area

covered by the study were assigned to the appropriate category using GIS tools. It was confirmed that most of the area had

NDVI values that were similar to the average values, or even higher. It was found that there were subareas where the vegetation

index had decreased. For those cases, LANDSAT TM images of the area of September and October of 2001 were used for a

detailed inspection. The study included rainfall data as well, confirming a normal regional situation. Both low and high-

resolution satellite images were found to be useful tools for obtaining fast, economic, objective and conclusive results about the

production capability of individual farms as well as the region as a whole.

D 2002 Elsevier Science B.V. All rights reserved.

Keywords: drought; NOAA-AVHRR; LANDSAT TM; crop condition; NDVI

1. Introduction

The Argentine Pampean Region covers an area of

about 450,000 km2, and its agriculture and cattle

grazing production accounts for about 60% of the

National Gross Production (NGP) and 80% of the

total country exports (Baldy and Rebella, 1990). The

significance of the region to the national economy

makes any factor that might influence agricultural

production of the area exceedingly critical. As a

result, abnormal weather situations are closely moni-

tored by government agencies because most agricul-

tural production occurs under rainfall conditions. In

2000, some counties in southern Buenos Aires prov-

ince were affected by a severe drought. As a conse-

quence, the government declared them to be in an

0924-2716/02/$ - see front matter D 2002 Elsevier Science B.V. All rights reserved.

doi:10.1016/S0924-2716(02)00153-3

* Corresponding author. Tel.: +54-114-621-0125; fax: +54-114-

621-5663.

E-mail address: [email protected] (T. Hartmann).1 Best paper, 29th International Symposium of ICORSE on

Remote Sensing of Environment, 8–12 April 2002, Buenos Aires.

www.elsevier.com/locate/isprsjprs

ISPRS Journal of Photogrammetry & Remote Sensing 57 (2003) 281–288

emergency situation in accordance with provincial

laws 2587/00 and 3051/00, and later by laws 63/01,

97/01 and 126/01. Drought conditions relented from

July 2001 onwards, and the question was raised

regarding the actual production capacity of 3256

farms under emergency or disaster status that had

been declared in 2000.

A fast and low cost evaluation was required. Since

1970, multispectral and multitemporal satellite remote

sensing data analysis has been shown to be a useful

tool to evaluate vegetation status in terms of (a) net

primary productivity or biomass (e.g. Tucker, 1977;

Paruelo et al., 2000), (b) crop condition assessment

(Di Bella and Rebella, 1997), or (c) water balance (Di

Bella et al., 2000). Remote sensing was selected as an

appropriate tool for examining agricultural perform-

ance based on these previous successes.

The primary objective of the work was to find an

indicator to evaluate the crop situation for each farm

using remote sensing and use it as a trigger for the

decision whether to continue support of farms under

emergency law.

2. Materials and methods

The area under study includes the counties of

Carmen de Patagones, Villarino, Puan, Bahıa Blanca,

Saavedra, Tornquist and Coronel Pringles in the

southeastern part of Buenos Aires Province, covering

4,500,000 ha at a latitude between 37j20VS and

41j05VS (Fig. 1). Wheat production of the area repre-

sents 9% of national production (12,000,000 tons)

(SAGPyA, 2000).

For monitoring vegetation condition, we processed

both low and high-resolution satellite images, as well

as meteorological and cadastral data:

(a) Satellite data:

(a.1) NOAA-AVHRR LAC daily satellite images

(1 km2 spatial resolution) captured by the

Institute of Climate and Water (INTA) from

September 1996 up to the present were

processed to calculate the NDVI. Daily

maximum NDVI values were composited

over a 10-day period to eliminate clouds and

other degradation effects (Holben, 1986).

This index has been proved to be a good

indicator of the photosynthetically active

radiation intercepted by crops or natural

vegetation. When radiation use efficiency is

more or less constant, the index is propor-

tional to the productivity of biomass (Gamon

et al., 1995). NDVI values range from � 1 to

1. If photosynthetically active vegetation

active is present, the index has positive

values from 0 (low or none photosynthesis

activity) to 1 (high). For each month of the

year, we generated a ‘‘layer’’ of NDVI

values. This resulted in a ‘‘stack’’ of all

‘‘historical’’ data (1996–2000) of that month.

For each month, we calculated the maximum,

minimum, mean and standard deviation of

the period 1996–2000 for each 1 km2 pixel.

(a.2) LANDSAT TM images of the area taken in

September 2001 were processed. A set of five

scenes was used to cover the total area (path/

row 227/86, 227/87, 227/88, 226/86 and 226/

87). We used bands 3, 4 and 5 representing

the information in the visible, near infrared

and middle infrared, respectively. All images

were geometrically corrected and co-regis-

tered with the NOAA AVHRR images.

(b) Rainfall data over the area were collected from

seven meteorological stations in the area: Pigue,

Hilario Ascasubi, Cnel. Suarez, Bordenave, Bahıa

Blanca, Barrow and Tres Arroyos (Fig. 1) to

compare the monthly precipitation in year 2000

and 2001 with a long-term (1965–2000) histor-

ical time series.

(c) Cadastral maps were purchased to identify the farm

units covered by at least one of the Emergency

Laws. The maps were used in digital format,

projected into the Gauss-Kruger zone 4 coordinate

system, as were all LANDSAT images and NDVI

maps generated from NOAA-AVHRR images.

(d) Data analysis: the NDVI Comparative Map for

September 2001 was used to assess its relative

value against the 1996–2000 series. Five catego-

ries of crop condition were developed based on

comparison of the current year against historical

values:� Very poor: less than the minimum of the

historical record;� Poor: between the minimum and 1 j below

the mean;

T. Hartmann et al. / ISPRS Journal of Photogrammetry & Remote Sensing 57 (2003) 281–288282

Fig. 1. Study area. Points on the bottom image represent the meteorological station locations. BO=Bordenave; PI = Pigue; CS =Coronel Suarez;

HA=Hilario Ascasubi; BB=Bahia Blanca and BD=Barrow.

T. Hartmann et al. / ISPRS Journal of Photogrammetry & Remote Sensing 57 (2003) 281–288 283

� Fair: within 1j above or below themean value;� Good: between 1 j above of mean and the

maximum value;� Very good: greater than the maximum histor-

ical value.

Values—less than 1 j below the mean—indicate

poor or very poor development of the vegetation.

However, this behaviour can be attributed to more

than one cause. It might indicate regional drought, but

might also be due to flood, burned biomass, and also

fallow indicating an increase of agriculture activity. To

compensate for situations such as these, LANDSAT

images were used to examine areas with below

average AVHRR NDVI values to understand local

conditions.

3. Results

3.1. Analysis of rainfall data

Rainfall data from July 2000 to December 2001 are

presented in Fig. 2. In 2000, precipitation showed a

marked decrease in November (less than the average)

and December (less than the 10% tail). That period is

critical for wheat grain filling, and drought in this

period can cause severe crop losses. In contrast, total

rainfall in the first quarter of the 2001 season (July,

August, September) was higher than the historical

average, leaving soils moist to wet, creating optimal

conditions for crop development.

3.2. Analysis on the NDVI for the area

The NDVI distribution map for September 2000

(Fig. 3) showed extended drought over the area,

mainly in the counties of Villarino, Puan, Bahıa

Blanca, Saavedra, Tornquist and Coronel Pringles,

with a majority of pixels falling in the very poor

category, consistent with the decrease in rainfall

indicated in Fig. 2.

The map built with September 2001 data (Fig. 4),

shows that most of the area under study had NDVI

values higher than (mean—1 j) of the time series,

indicating a healthy status for vegetation or crops.

Nonetheless, with the exception of the southern part

of the province, significant areas had NDVI values

below the average. In Table 1 we present the area and

percentage of the total for each class of crop con-

dition, for each of the seven counties under study. As

mentioned before, the actual situation of the locations

Fig. 2. Monthly rainfall data for the BB station from September 2000 until December 2001. Bars represent the rainfall that occurred during

2000–2001 period and lines represent 10%, 50% and 90% tails of the frequency distribution of rainfall during the period 1965–2000.


Fig. 3. September 2000 conditions resulting from comparing this particular month with the same months of the period 1996–2000. VG=Very

good condition; G=Good condition; F = Fair condition; VP=Very poor condition and P= Poor condition.


Fig. 4. September 2001 conditions resulting from comparing this particular month with the same months of the period 1996–2000. VG=Very

good condition; G =Good condition; F = Fair condition; VP=Very poor condition and P= Poor condition.


with low NDVI values was established with the help

of LANDSAT TM 30-m resolution images.

3.3. Use of LANDSAT images

Several places that were classified as poor or very

poor condition were analysed in detail by visual

examination of LANDSAT images. Previous exper-

tise in the recognition of land use in the area using

LANDSAT imagery, as well as field data, identified

normal agriculture activity in the areas where it is

usually practised: crops and grasslands were in

excellent growing stage, showing healthy vegetation

with high chlorophyll content, as indicated in the

wide differences among the responses of the near

infrared channel (Band 4) and Band 3. Recently,

plowed soil, prepared for summer crops, was also

recognised.

With respect to low NDVI values, this analysis

illustrates that there are two other explanations that are

not related to drought:

(a) Soils with low water storage capacity (very

shallow or sandy soils), where vegetation can be

under water stress after a few days of insolation

and lack of rain. Those soils are not used for

agriculture. These places are found next to water

bodies or crops showing good health, so the low

NDVI value is due to the local soil characteristics

and cannot be considered an exceptional emer-

gency situation.

(b) Areas located mainly in the northern part of the

area. They show a high incidence of water bodies

and plowed soil, which contribute to a reduction

in monthly NDVI values of the 100 ha pixel. This

is evidence of good conditions for agriculture,

rather than an emergency situation.

4. Conclusions

In September 2001, 89% of the area represented by

the seven counties was found to be in good or very

good biomass condition based on a comparison of

current AVHRR NDVI data with historic means and

standard deviations. The remainder of the area (11%)

in which drought might be suspected was examined

using LANDSAT TM satellite images. Results

obtained showed that locations with NDVI more than

1 j below the historic NDVI mean during September

2001 were adjacent to areas that were ‘‘normal’’ with

no evidence of water stress. In the cases of low water

retention soils (county of Villarino and C. Pringles),

neighbouring areas of higher productive capacity were

in excellent condition. In the northern area, paddocks

between water surfaces showed excellent conditions

as well. Thus, we judged that the area was in generally

good condition.

Additionally, rainfall measurements also demon-

strated that the counties of interest were not experi-

encing drought.

The complementary use both low and high-reso-

lution satellite images proved to be useful in obtain-

ing fast and conclusive results about the true

production status of the region at farm level. The

material used in this work (i.e., satellite images,

Vegetation Index Maps, cadastral maps and its corre-

sponding databases) were integrated into a Geo-

graphic Information System to be used by the

authority established by emergency law. The system

allows us to categorise the entire region with respect

to vegetation condition, identify farms with potential

problems, and proceed to detailed examination

through the use of LANDSAT images to verify

problems and eliminate ambiguities.

Table 1

Area covered by different biomass conditions in September 2001

Very good Good Fair Poor Very poor

Hectares

Patagones 454,640 121,556 737,621 1072 15,207

Villarino 329,575 88,706 505,914 5751 72,232

Puan 240,090 68,527 252,177 3607 74,571

B. Blanca 58,487 19,301 138,030 585 11,697

Saavedra 38,407 19,983 179,458 3997 105,862

Tornquist 106,544 35,190 240,090 3509 38,114

C. Pringles 59,364 33,825 314,954 6336 109,858

Total 1,287,107 387,088 2,368,244 24,857 427,541

Percentage

Patagones 34% 9% 55% 0% 1%

Villarino 33% 9% 50% 1% 7%

Puan 38% 11% 39% 1% 12%

B. Blanca 26% 8% 61% 0% 5%

Saavedra 11% 6% 52% 1% 30%

Tornquist 25% 8% 57% 1% 9%

C. Pringles 11% 6% 60% 1% 21%

Total 29% 9% 53% 1% 10%


Acknowledgements

We want to thank people from both high and low-

resolution satellite image processing laboratories at

the Institute of Climate and Water. We also want also

thank especially Ing. Agr. Rafael Rodriguez for the

meteorological data he provided. The research was

sponsored by the Banco de la Provincia de Buenos

Aires.

References

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