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International Conference on Reimagining Rainfed Agro-ecosystems: Challenges & Opportunities during 22-24, December 2022 at ICAR-CRIDA, Hyderabad Emerging approaches (RS, AI, ML, Drones etc) for crop management &assessment T5-03aO-1041 Big Data Analytics for Identifying Districts with Untapped Potential in Rainfed Agriculture and Bridging Gaps B. M. K. Raju*, C. A. Rama Rao, R. Nagarjuna Kumar, K. V. Rao, V. K. Singh, Josily Samuel, A. V. M. Subba Rao, M. Osman and N. Swapna ICAR-Central Research Institute for Dryland Agriculture, Santoshnagar, Hyderabad-500059 * bmkraju@yahoo.com Government of India set a target of doubling farmers income by 2022-23. Niti Aayog target that onethird of the enhanced income should be contributed by yield increase. Other compelling forces for yield expansion in India are food & nutritional security and burden of importing oilseeds and pulses. Enhancing the productivities in rainfed agriculture is important from the perspective of inclusive agricultural growth. Climate, soil, irrigation and season (kharif/rabi) are the key drivers of productivity of a crop in a region. Farmer has no choice towards climate and soil while the factors of access to irrigation and season in which the crop is grown are relatively less amenable to changes. On the other hand, farmer has choice in case of factors like adoption of technologies such as improved variety, nutrient management, etc., which are relatively more amenable at farm level with appropriate policy and other interventions. If major districts of a crop are divided into clusters homogeneous in terms of climate, soil, share of irrigated area under the crop and growing season the differences in productivity within the cluster can be majorly attributed to the factors that are amenable to changes. As the resources (that farmer has less or no choice) for raising the crop within a cluster are more or less same, the district producing highest yield in a cluster may be regarded as potential target and its yield as potential yield for the remaining districts in the cluster. Untapped yield potential (yield gap) for a district may be computed as the difference between potential yield and yield of the district (Raju et al., 2018). It is important to identify crop-wise districts where potential exists for yield growth in rainfed agriculture and explore the ways to bridge the gaps in order to achieve inclusive agricultural growth in India. Methodology Twenty important crops covering cereals, pulses, oilseeds and commercial crops are included in the study. Multivariate cluster analysis has been used for dividing major districts of a crop into clusters. The clustering variables used are moisture index, available water holding capacity of soil, per cent irrigated area under the crop and share of a particular season in area under the crop at district level. Moisture index (MI) was considered as a summarised indicator of various climatic variables. Available water holding capacity (AWHC) of the soil was considered as a summary indicator of soil properties such as soil texture and soil depth (Raju et al., 2015). Before carrying out the analysis, the clustering variables were standardized using mean and standard deviation. Ward’s agglomerative hierarchical clustering algorithm was employed to derive clusters from distance matrix computed using squared euclidean distance if the number of major districts is less than 200. K means clustering algorithm on euclidean distance was used in case of rice and wheat which were having more than 200 major districts. The analysis was carried out using SPSS. Criterion used for determining the number of clusters was to increase the number of clusters sequentially (one at a time) till share of intra-cluster variation goes 704 | Page
International Conference on Reimagining Rainfed Agro-ecosystems: Challenges & Opportunities during 22-24, December 2022 at ICAR-CRIDA, Hyderabad below 5%. Yield efficiency of a district in a given crop is computed as the ratio of untapped yield and potential yield. Yield efficiency of less than 0.5 indicates scope for doubling of yield in the district. For each crop, the clusters of districts for which mean area under irrigation (as a percent of sown area) was less than 25% were considered as rainfed. Let Z is the rainfed yield efficiency of jth district with respect to ith crop predominantly cultivated under rainfed conditions then Overall Rainfed Yield Efficiency of jth district is Z = ∑ W Z where W ij = Proportion of area sown to i th crop (considering the crops which are predominantly cultivated under rainfed conditions) in j th district such that ∑ W = 1 (for all j). Results The resultant map showing overall rainfed yield efficiency at district level has been given as Fig 1. The number of districts having rainfed yield efficiency less than 0.5 are more in Madhya Pradesh (10), Rajasthan (8), Jharkhand (8), Tamil Nadu (6) and Orissa (5) states. Aggregate rainfed yield in these districts can be doubled. The variation in factors of crop management such as consumption of fertilizer nutrients of Nitrogen (N), Phosphorous (P) and Potassium (K) and adoption of technology such as extent of use of High Yielding Varieties (HYV) in the crop at district level are found to explain the variation in yield within a cluster to a large extent. Strategies were identified to improve yield efficiency in 60 districts found to have low rainfed yield efficiency based on crop-wise gaps in adoption of HYV and nutrient use. A decision support system (DSS) has been developed which brings out untapped yield potential for a selected crop in a district. The DSS identifies 3 model districts and displays information on extent of adoption of HYVs and nutrient use in them indicating strategies for bridging yield gap in selected crop of the district. The DSS has been hosted on ICAR-CRIDA website at http://www.icarcrida.res.in:8129/. Overall rainfed yield efficiency at district level (with 20 crops) 705 | Page Emerging approaches (RS, AI, ML, Drones etc) for crop management &assessment
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Extended Summaries 22-24 December,
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Citation Rama Rao, C.A., Srinivasa
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Resilience through land and water m
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International Conference on Reimagi
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Theme- 2 Ecosystem based approaches
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Resource conservation and rainfed a
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Theme- 6 Institutional and policy i
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