Modelling impact of climate variability on rainfed groundnut

We present here a heuristic model for the indirect impact of climate variability via the triggering of pests/diseases/weeds for rainfed groundnut over the Anantapur region. A simple hydrological model is used to determine the soil moisture for the rainfall pattern, in any given year. The criteria for determining when specific farming operations, such as ploughing and sowing, are performed are defined in terms of the soil moisture, on the basis of the farming practices in the region. With the sowing date so determined, the dates of occurrence of the different life history stages are known for any specific year. The events that trigger the growth and incidence/infestation of the major pests/diseases, viz. wet/dry spells, are also defined in terms of the soil moisture and/or rainfall. The probabilities of the occurrence of the different pests/diseases incorporated in the model are then calculated by running the model for eighty years for which daily rainfall data at the Ananthapur observatory have been obtained from the India Meteorological Department. The model has been validated by comparison of the results with the observations of the incidence/infestation of specific pests/diseases at the Anantapur agricultural station of the Andhra Pradesh University. The PNUTGRO model, which is a complex model for the growth and development of the groundnut plant, is able to simulate well the variability of the yield at the Anantapur agricultural station, where pests and diseases are prevented by plant protection measures. However, the observed district yields are generally much lower. We find that when the heuristic model for pests/diseases is used in conjunction with the PNUTGRO model, the simulated variation of the yield during 1970–90 is rather close to the observed district yield. This suggests that such models which incorporate the direct impact of climate on growth and development as well as the indirect impact via triggering of pests and diseases can be used for understanding the response of the groundnut yield to climate variability and in decision support systems for the region.