Book of Extended summaries ISDA

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
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