Technologies for Intensification of Production and Uses of Grain Legumes for Nutrition Security

Malnutrition resulting from intake of food poor in nutritional value, particularly lacking in micronutrients, has been recognized as a serious health problem in developing countries including India. Nutritional security is a priority for India. Crop diversification in agriculture contributes to balanced diet and nutritional security. Production intensification of nutrient-dense crops, contributes to their increased production, and consequently enhances their accessibility at affordable prices to meet nutritional security. Grain legumes produce nutrient-dense grains rich in proteins, vitamins, minerals and micronutrients essential for growth and development. However, cultivation of grain legumes is often neglected resulting in poor production in the country, and consequently poor access to legumes at affordable prices. Pigeonpea or red gram (Cajanus cajan L.), chickpea or bengal gram (Cicer arietinum L.) and groundnut (Arachis hypogaea L.), the three nutritious grain legumes are grown widely across the country and are major constituents of Indian diets. They are climate- resilient crops adapted to water-limiting conditions making them choice crops for cultivation in adverse conditions. Policy options for promoting cultivation and increased production of pigeon pea, chickpea and groundnut are needed. Technology options for intensification of their cultivation include improved cultivars of grain legumes with enhanced adaptation and nutritional properties, their processing, plugging post-harvest and storage losses, and development of alternative food products. The chapter discusses the contribution of agriculture to nutritional security and the need to diversify cultivation of crops to include nutrient-dense grain legumes, and intensification of their cultivation to achieve their enhanced production and productivity. The scope to develop bio-fortified grain legumes is also discussed. Some countries have successfully harnessed the potential of processed grain legumes for use as food supplements for children and elderly, as well as to prepare ready-to-use-therapeutic-food products to treat acute malnutrition

Detecting Soil pH from Open-Source Remote Sensing Data: A Case Study of Angul and Balangir Districts, Odisha State

Soil sampling, collection, and analysis are a costly and labor-intensive activity that cannot cover the entire farmlands; hence, it was conceived to use high-speed open-source platforms like Google Earth Engine in this research to estimate soil characteristics remotely using high-resolution open-source satellite data. The objective of this research was to estimate soil pH from Sentinel-1, Sentinel-2, and Landsat-8 satellite-derived indices; data from Sentinel-1, Sentinel-2, and Landsat-8 satellite missions were used to generate indices and as proxies in a statistical model to estimate soil pH. Step-wise multiple regression (SWMR), artificial neural networks (ANN), and random forest (RF) regression were used to develop predictive models for soil pH, SWMR, ANN, and RF regression models. The SWMR greedy method of variable selection was used to select the appropriate independent variables that were highly correlated with soil pH. Variables that were retained in the SWMR are B2, B11, Brightness index, Salinity index 2, Salinity index 5 of Sentinel-2 data; VH/VV index of Sentinel 1 and TIR1 (thermal infrared band1) Landsat-8 with p-value\0.05. Among the four statistical models developed, the class-wise RF model performed better than other models with a cumulative correlation coefficient of 0.87 and RMSE of 0.35. The better performance of class-wise RF models can be attributed to different spectral characteristics of different soil pH groups. More than 70% of the soils in Angul and Balangir districts are acidic soils, and therefore, the training of the dataset was affected by that leading to misclassification of neutral and alkaline soils hindering the performance of single class models. Our results showed that the spectral bands and indices can be used as proxies to soil pH with individual classes of acidic, neutral, and alkaline soils. This study has shown the potential in using big data analytics to predict soil pH leading to the accurate mapping of soils and help in decision support

Detecting Soil pH from Open Source Remote Sensing Data: A Case Study of Angul and Balangir districts, Odisha State

International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) is implementing ‘Odisha Bhoochetana’, an agricultural development project in Angul and Balangir districts in India. Under this project, soil health improvement activity was initiated by collecting soil samples from selected villages of the districts. Soil information before sowing helps farmers not only to choose a crop but also in planning crop nutritional inputs. Soil sampling, collection, and analysis is a costly and laborintensive activity that cannot cover the entire farmlands, hence it was conceived to use high-speed open-source platforms like Google Earth Engine in this research to estimate soil characteristics remotely using high-resolution open-source satellite data. The objective of this research was to estimate soil pH from Sentinel1, Sentinel 2, and Landsat satellite-derived indices; Data from Sentinel 1, Sentinel 2, and Landsat satellite missions were used to generate indices and as proxies in a statistical model to estimate soil pH. Step-wise multiple regression, Artificial Neural networks (ANN) and Random forest (RF) regression, and Class-wise random forest were used to develop predictive models for soil pH. Step-wise multiple regression, ANN, and RF regression are single class models while class-wise RF models are an integration of RF-Acidic, RF-Alkaline, and RF- Neutral models (based on soil pH). The step-wise regression model retained the bands and indices that were highly correlated with soil pH. Spectral regions that were retained in the step-wise regression are B2, B11, Brightness Index, Salinity Index 2, Salinity Index 5 of Sentinel 2 data; VH/VV index of Sentinel 1 and TIR1 (thermal infrared band1) Landsat with p-value <0.001. Amongst the four statistical models developed, the class-wise RF model performed better than other models with a cumulative R 2 and RMSE of 0.78 and 0.35 respectively. The better performance of class-wise RF models over single class models can be attributed to different spectral characteristics of different soil pH groups. Though neural networks performed better than the stepwise multiple regression model, they are limited to a regression while the random forest model was capable of regression and classification. The large tracts of acidic soils (datasets) in the study area contributed to the training of the model accordingly leading to neutral and alkaline soils that were misclassified hindering the single class model performance. However, the class-wise RF model was able to address this issue with different models for different soil pH classes dramatically improving prediction. Our results show that the spectral bands and indices can be used as proxies to soil pH with individual classes of acidic, neutral, and alkaline soils. This study has shown the potential in using big data analytics to predict soil pH leading to the accurate mapping of soils and help in decision support

Farmer Producer Organization in Andhra Pradesh: A Scoping Study. Rythu Kosam Project. Research Report IDC-16.

The declining profitability and rising risk associated with agriculture and allied its activities is being considered some of the major challenges in improving the livelihoods of the rural population in India. Mainly small and marginal farmers constitute the largest group of cultivators (about 85%) in Indian agriculture; having smaller than or about two hectares of operational holdings. The vulnerability to these households is largely attributed to lower scale of operation, lack of information, poor access to cheaper credit, weak participation in the consumers’ markets and consequently, exploitation by intermediaries in procuring inputs and marketing of their produce. A variety of approaches have emerged over the years to address these problems. Agricultural cooperatives, formed under the Co-operative Credit Societies Act, 1904, have long been the dominant form of farmer collectives; however, the experience with cooperatives point to many limitations, except few successful exceptions in the field of dairy farming. In recent years, collectivization of producers, especially small and marginal farmers, into producer organizations has emerged as one of the most effective pathways to address the many challenges of agriculture. Hence, on the recommendations of a high-power committee, the Government of India introduced the Companies (Amendment) Act 2002, which paved the way to Producer Companies (PCs)..

Mapping and Monitoring Of Water Hyacinth In Lake Victoria Using Polarimetric Radar Data

Water hyacinth, an invasive species originating from South America, has become a significant concern since its introduction in Lake Victoria (Kenya), particularly in the Winam Gulf, where large annual blooms are observed. Monitoring the occurrence and location using in situ methods is expensive and challenging due to the lake's vastness. Remote sensing monitoring methods offer an alternate option due to the ability to cover vast areas. This study explores the potential of polarimetric Synthetic Aperture Radar (PolSAR), specifically utilising Sentinel-1 VV-VH data to map and monitor water hyacinth cover. The change detection method based on Optimisation of Power Difference (OPDiff) and minimum eigenvalue selection achieves a remarkable accuracy of 98.89% in separating clear and water hyacinth-infested water. Using polarimetric data offered better separability, enabling spatial and temporal monitoring. The analysis reveals that in 2018 water hyacinth cover peaked, spanning over 200 km 2 . Temporal variability showcases a seasonal rise and peak from September to December. This research demonstrates the capability of using PolSAR data to accurately map and monitor water hyacinth's spatial and temporal dynamics, offering valuable insights for effective management strategies

Mapping and Monitoring Of Water Hyacinth In Lake Victoria Using Polarimetric Radar Data

Water hyacinth, an invasive species originating from South America, has become a significant concern since its introduction in Lake Victoria (Kenya), particularly in the Winam Gulf, where large annual blooms are observed. Monitoring the occurrence and location using in situ methods is expensive and challenging due to the lake's vastness. Remote sensing monitoring methods offer an alternate option due to the ability to cover vast areas. This study explores the potential of polarimetric Synthetic Aperture Radar (PolSAR), specifically utilising Sentinel-1 VV-VH data to map and monitor water hyacinth cover. The change detection method based on Optimisation of Power Difference (OPDiff) and minimum eigenvalue selection achieves a remarkable accuracy of 98.89% in separating clear and water hyacinth-infested water. Using polarimetric data offered better separability, enabling spatial and temporal monitoring. The analysis reveals that in 2018 water hyacinth cover peaked, spanning over 200 km 2 . Temporal variability showcases a seasonal rise and peak from September to December. This research demonstrates the capability of using PolSAR data to accurately map and monitor water hyacinth's spatial and temporal dynamics, offering valuable insights for effective management strategies