Abstract
This study shows the use of remote sensing to find the change in groundnut crop inventory in Gujarat for two consecutive years(1992-93 and 1993-94). The 1993-94 being a rainfall deficient year, the groundnut production had drastically reduced from the production had drastically reduced from the production in the nrmal year 1992-93. Crop acreage was estimated using satellite based remote sensing data. A cumulative soil moisture balance model, which computer water requirement satisfaction Index (WRSI) was used for yield estimation. The district - level acreage, yield and production estimates were given for five district of Saurashtra region. The groundnut production of the group of five districts decreased fro 1497 thousands tones in 1992-93 to 547 thousand tones in 1993-94. These estimates were in fairly good agreement with the estimates made by the deptt. Of agriculture.
1. Introduction
Remote sensing (RS) technology has been extensively used for forecasting crop production for major crops under normal weather conditions in India (Anon. 1990 and Navalgund et al. 1991). However, large areas in the country are rainfed and hence crop production varies widely from year to year depending on weather conditions. RS technology coupled with metereological models can prove very useful for crop inventory study in the abnormal weather conditions.
India strands first both in acreage and production of groundnuts (Arachis hypogaea L.) in the world. Among the states in India, Gujarat occupies first position in groundnut acreage and production. Hence, it is essential to estimates groundnut production for Gurajat in both normal and abnormal weather situations because they play and important role in determining the oil economy in the country. Pokharna et al 91991) and Medhavy et. Al. (1989) have studied the possibility of oilseeds acreage estimation at district level using a single date RS data. However, for yield estimation in rainfed situation, agrometereological models prove to be very useful (Ray et. Al 1994). Frere and Popov (1979) computed a Water requirement satisfaction in Index (WRSI) from their simplified cumulative water balance model for which the major input is rainfall. This model has an advantage over the RS based yield model as groundnut is a Kharif crop and it is difficult to get cloud free RS data during this season (Ray et.al 1993). Also since this model is strongly rainfall dependent, it could be very useful under abnormal weather conditions.
In this context the present study deals with i) district level acreage estimation using RS technique for groundnut and ii) district level yield estimation using WRSI based models under both normal as well as abnormal weather conditions.
2. Materials and Methods
2.1 Study Area
Groundnut in Gurajat is mainly grown in five districts of Saurashtra lying between Gulf of Kachch and Gulf of Khambat. These district are Amreli, Bhavnagar, Jamnagar, Junagadh and Rajkot. These five districts together, covering a geographical area of 53,850 sq km, contribute around ninety per cent to the groundnut production of the state. Groundnut is grown as rainfed Kharif crop in this area. Together crops grown in Saurashtra region during Kahrif season are seasamum, pearl millet, sorghum and cotton etc.
2.2 Data Used
2.2.1 Satellite Date :
The five districts of groundnut are covered by five IRS LISS I scenes. The path and row of each scene is given in Table 1. The dates of acquisition were so chosen that these corresponded to the maximum vegetative stage of the concerned crop. In 1993-94 cloud free Rs data were not available during the peak vegetative stage i.e., second week of September. Hence, initially NDAA AVHRR data was used to give combined acreage estimates for the group of five districts. Later on, Landsat TM as well as IRS-IB data of October first week (Table 1) were used to give district level estimates.
Table 1 List of satellite data used for acreage estimation
| Year |
Satellite |
Path-Row |
Date of Acquisition |
| 1992-93 |
IRS-1A |
32-53 |
24.09.1992 |
| |
|
33-52 |
25.09.1992 |
| |
|
33-53 |
25-09-1992 |
| |
|
34-53 |
26.09.1992 |
| |
|
34-53 |
26.09.1992 |
| 1993-94 |
NDAA |
------- |
01.10.1993 |
| |
IRS-1B |
33-52 |
04.10.1993 |
| |
Landsat-TM |
149-44, 45 |
02.10.1993 |
| |
|
150-44,45 |
09.10.1993 |
2.2.2 Agrometeroloqical Data : The district-wise historical yield data for groundnut for 32 years i.e. 1961-1991 were collected from DES (Dept. of Economics and Statistics) publications: District-wise fortnightly rainfall and number of rain days data (needed for yield forecast for the same period were collected from Dept. of Agriculture, Gujarat state (DDA). The soil physical data were collected from the soil science department of the Gujarat Agricultural University, Junagadh. Crop coefficients for were taken from Dorrendbos and Kassam (19979).
2.3 Acreage Estimation
For analysis of digital data, a stratified random sampling approach was and a sampling fraction of ten per cent. For stratification, historical taluka-wise acreage estimates as well as vegetation density found in he FCC prints of satellite data of previous year was used The ground truth information about different land cover classes was collected in fourth week of September and the first week of October 1992. for the year 1993-94, groundtruth information was collected in fourth week of September and also during November 9-8 for yield information.
Digital analysis of satellite data for the year 1992-93 was carried out on VIPS-32 image processing system (VAX 11/780) at RRSSC, Jodhpur using supervised classification method with a maximum likelihood classifier. Aggregatin was done on both Dipix image processing system at SAC, Ahmedabad and VIPS-32 system at Jodhpur.
2.4 Yield Estimation
A cumulative water balance model developed by Frere and Popov (1979) based on periodic values of precipitation and PET was used. The main relationship is given by:
Si = Pi - WRi
Where
S = net water added to soil in ith period
P
i = precipitation in ith Period
WR
i = water requirement ith Period
WR = KC * PET and
KC = crop coefficient
The WRSI indicates (in percent) the extent to which the water requirements cumulatively, duing the growth cycle. It is taken as equal to 100 at the time of sowing and in case of a deficit the WRSI is reduced by the percentage ratio of the water deficit and the total crop water requirements over the whole crop season. If the water surplus is greater than 100 mm in a fortnight and the number of rainy days is less than 6, WRSI is decreased by 2.1 units for each 100 mm of excess water. The final value of WRSI at the end of growing season is related exponentially to the yield.
The model was run on daily basis starting from June 1 of every year. Normally onset of the monsoon in the study area takes place after June 10. The value of KC for bare soil evaporation, was taken to be 0.2. For three major stages of the crop viz. vegetative, flowing and maturity, the values of crop coefficient chosen were 0.6, 1.1 and 0.8 respectively. The field capacity, permanent wilting point and solid depth were 150mm, 25,, and 60 cm, respectively. It was assumed that the sowing started after the cumulative rainfall exceeded 30 mm.
District level production estimation was made using the acreage values from RS method and yield values estimated by yield -WRSI models.
