Pilot studies on GP Crop yield estimation using Technology (Kharif 2019) using SENTINEL- 2 satellite data (in Andhra Pradesh, Telangana and Odisha States (Five Districts)) for Groundnut, Chickpea, Maize and Rice

The Government of India plans to optimize Crop Cutting Experiments (CCEs) using different technologies including satellite derived metrics on crop performance and spatial variability to guide the selection and number of ground data sites. This requires the development of an approach for different crops for the different agro-climatic regions of India. The present study plans to develop an approach for following crops viz., Groundnut, Chickpea, Rice and Maize. The above crops will be studied in five districts of three states viz. Andhra Pradesh, Telangana and Odisha. The study will use comprehensive and existing environmental, weather and management data along with satellite derived crop spatial data. This information will be modelled using statistical optimization techniques to assess the optimal numbers of CCE’s that can be undertaken

Agronomic management options for sustaining chickpea yield under climate change scenario

The impact of future climate change on the chickpea productivity was studied using the sequence analysis tool of DSSAT V 4.5 to simulate fallow-chickpea rotation at four locations viz Anantapur, Kurnool, Kadapa and Prakasam of Andhra Pradesh State. The results indicated that as compared to baseline climate, the climate change to be anticipated by 2069 (Mid –century period) would decrease the yield of chickpea by 4.3 to 18.6 per cent across various locations tested. Yield benefits obtained based on the simulation study from various adaptation options revealed that advancing the sowing window by one fortnight and application of one critical irrigation at 60 DAS found to be beneficial in increasing chickpea yields under climate change scenario

Identifying irrigation and nitrogen best management practices foraerobic rice–maize cropping system for semi-arid tropics using CERES-rice and maize models

Research based development of best management options for aerobic rice–maize cropping systems must be developed to improve water and nitrogen use efficiency. The main objective of this study was to identify water saving rice production technology for rice grown in sandy loam soils in semi-arid conditions using the calibrated CERES-Rice and Maize models of the Decision Support System for Agro Technology Transfer (DSSAT). A two-year experiment with two different crop establishment methods viz., aerobic rice and flooded rice with four nitrogen rates followed by maize under zero tilled conditions was used to calibrate and evaluate DSSAT CERES-Rice and CERES-Maize models. The calibrated models were used to develop best management options for an aerobic rice–maize sequence which can produce similar yields with water savings relative to that of traditional flooded rice–maize system. The results showed that application of 180 kg N ha−1 in four splits and automatic irrigation with 40 mm, when soil available water (ASW) in top 30 cm fell below to 60% was the best management combination for aerobic rice, saving 41% of water while producing 96% of the yield attainable under flooded conditions. Similarly for maize, application of 120 kg N ha−1 and irrigation with 30 mm of water at 40% ASW in the top 30 cm soil was the most dominant management option. Further, application of 180 kg N ha−1 with rice followed by 120 kg N ha−1 in maize provided stable yield for both aerobic and flooded rice systems over time as simulated by the model. The results illustrate that DSSAT model is a useful tool for evaluating alternative management options aimed at maintaining yields and saving water in rice–maize systems in semi-arid regions

Impact of Aerobic Rice Cultivation on Growth, Yield, and Water Productivity of Rice–Maize Rotation in Semiarid Tropics

Limited water availability is a major constraint for cultivation of rice (Oryza sativa L.) in the traditional flooded systems, particularly in the semiarid regions of the world. Aerobic rice cultivation provides feasible alternative to traditional rice production in these regions, allowing significant water savings. Field experiments were conducted at the ANGR University Agricultural Research Station, India during 2009–2010 and 2010–2011 to compare crop growth, yield, and water savings under aerobic rice–maize (R–M) and flooded R–M rotation systems. The effect of aerobic rice on the succeeding maize crop was also studied. The total amount of water applied (including rainfall) in the aerobic plots was 967 and 645 mm compared to 1546 and 1181 mm in flooded rice system, during 2009 and 2010, respectively. This resulted in 37 to 45% water savings with the aerobic method. The soil moisture in aerobic treatment was maintained in the –30 to –40 kPa range throughout the crop growth. The aerobic rice system produced significantly lower grain yields in 2009 and 2010, where differences between flooded and aerobic rice were 39 and 15.4%, respectively. The yield differences were attributed to the differences in spikelet number per panicle and grain weight. Significant increase in yields was recorded in both systems with increased N rates up to 120 kg ha−1. Significantly higher yields were obtained in no-till maize grown subsequent to the aerobic rice than flooded rice, possibly due to residual soil N and improved soil physical conditions

Assessment of climate change and vulnerability in Indian state of Telangana for better agricultural planning

Climate variability and change pose ever-growing challenges in the semiarid tropics, where majority of the population depend on climate-dependent activities such as agriculture. This has rendered these countries more vulnerable to climate change–induced variability. In spite of the uncertainties about anticipated magnitude of climate change on regional scale, an assessment of the possible changes in key climatic elements to identify most vulnerable locations becomes important for formulating adaptation strategies. This study compiles the existing knowledge about observed climate and projections of future change in Telangana state of India. The agriculture in this semiarid state has to adapt to changes in mean climate variables to increased variability with greater risk of extreme weather events, such as prolonged dry spells. Based on climatic vulnerability assessment, we found that the number of vulnerable mandals (currently 28%) will be increased to 45% during early century and to 59% by mid-century. As per the climate exposure index scores, Jogulamba-Gadwal district was found to be most sensitive. Overall, vulnerability index scores indicated that Adilabad, Nagarkurnool, Nalgonda, Peddapalli, Suryapet, Wanaparthy, and Yadadri are extremely vulnerable districts in the state. The ranking of vulnerable mandals in each district envisages the need for a holistic approach for each mandal or a group of mandals to reduce their sensitivity though implementation of site-specific adaptation strategies to minimize climate-related shocks not only in agriculture but also in other sectors

Study of Spatial Water Requirement of Rice under Various Crop Establishment Methods Using GIS and Crop Models

Application of crop simulation models at larger spatial scales is very essential to develop best management practices in order to maximize yields and reduce environmental pollution. In the present study, spatial analysis of long- term simulations were carried out with DSSAT spatial analysis tool linked with GIS to estimate irrigation requirements and nitrate leaching under alternate rice establishment methods in the Wargal watershed, Andhra Pradesh, India. Rice yields were compared among three management scenarios: rainfed, aerobic and flooded systems. Grain yield, seasonal water balance components, nitrate leaching and water use efficiency were calculated, visualized and mapped with GIS. The rice productivity increased by 22% and 27% under aerobic and flooded management compared to rainfed rice. The adoption of new water efficient aerobic rice cultivation in the watershed resulted in 36% water saving with a relatively small yield reduction of 4%, thus increasing the water productivity to 0.77 g kg-1 in aerobic compared to 0.56 g kg-1 in flooded rice. The aerobic rice method reduced the overall water pumping hours to 88 h ha-1 during rice crop season compared to 299 h ha-1 with flooded rice cultivation, resulting in 71% energy savings

Measuring Sustainable Intensification of Agricultural Productivity in Semi-Arid Tropics (SAT) of India – Case studies Synthesis Report

The very concept of sustainable intensification involves synthesis of two opposite forces. Intensification relates to the more intensive use of inputs to enhance the yields further. Sustainability looks at the longer term productivity of resources like land and water, which by its nature, applies brakes on the efforts to increase production by intensifying the use of inputs due to the fear that they may adversely impair the longer term productivity and resource quality/quantity. There may be a limited scope for increasing the use of inputs for realizing higher yields without impairing the longer term productivity of the critical resources. Sustainable intensification precisely looks at these limited opportunities. Over time, many researchers and institutions have used different contexts to define these terms. Very few researchers have attempted to systematically measure them on ground with selected cropping systems. The present study tried to use innovative approaches for generating profound evidences on sustainable intensification in semi-arid tropics of India with three dominant cropping systems located in Andhra Pradesh and Maharashtra states. The results are summarized in three case studies for better brevity of results and comparison

Delivering climate risk information to farmers at scale: the Intelligent agricultural Systems Advisory Tool (ISAT)

One of the strategies for helping smallholder farmers cope with climate variability and change is the provision of climate services that better decision making around the planning and management of agricultural systems. However, providing such services with location specific timely and actionable information to millions of farmers operating across diverse conditions requires innovative solutions. ICRISAT and its partners have developed and piloted one such system called “Intelligent agricultural Systems Advisory Tool – ISAT” capable of generating and disseminating data driven location specific advisories that assist farmers in anticipating and responding to the emerging conditions through the season. Using a decision tree approach, a structured and systematic approach to decision making was devised that considers the insights obtained from the analysis of historical climatic conditions, climate and weather forecasts and prevailing environmental conditions. Microsoft India developed a platform to access real time data from various ‘public’ sources, perform the data analytics, implement the decision tree and generate and disseminate messages to farmers and associated actors. The ISAT generated advisories are designed to support both pre-season planning and in-season management. During the 2017 monsoon, ISAT was piloted with 417 farmers across four different locations. The messaging system worked extremely well in picking appropriate location specific message from the database and delivering the same to the mobiles of the registered farmers. Mid and end season surveys revealed that more than 80% of the farmers from all villages were satisfied with the frequency, relevance and understandability of the messages delivered. About 58% of the farmers rated the messages are reliable by being correct more than 75% of the times and helped them in managing their farms better by conducting farm operations timely with reduced risk. Compared to farmers in the control villages, groundnut yields of farmers in 5 treatment villages are higher by ~ 16% but this results varied between -7.7 to 56.2%. This study has demonstrated the opportunities available to harness the untapped power of digital technologies to provide actionable advisories timely to smallholder farmers using appropriate data analytics and information dissemination systems

Engaging farmers on climate risk through targeted integration of bio-economic modelling and seasonal climate forecasts

Seasonal climate forecasts (SCFs) can be used to identify appropriate risk management strategies and to reduce the sensitivity of rural industries and communities to climate risk. However, these forecasts have low utility among farmers in agricultural decision making, unless translated into a more understood portfolio of farm management options. Towards achieving this translation, we developed a mathematical programming model that integrates seasonal climate forecasts to assess ‘what-if?’ crop choice scenarios for famers. We used the Rayapalli village in southern India as a case study. The model maximises expected profitability at village level subject to available resource constraints. The main outputs of the model are the optimal cropping patterns and corresponding agricultural management decisions such as fertiliser, biocide, labour and machinery use. The model is set up to run in two steps. In the first step the initial climate forecast is used to calculate the optimal farm plan and corresponding agricultural management decisions at a village scale. The second step uses a ‘revised forecast’ that is given six weeks later during the growing season. In scenarios where the forecast provides no clear expectation for a dry or wet season the model utilises the total agricultural land available. A significant area is allocated to redgram (pigeon pea) and the rest to maize and paddy rice. In a forecast where a dry season is more probable, cotton is the predominant crop selected. In scenarios where a ‘normal’ season is expected, the model chooses predominantly cotton and maize in addition to paddy rice and redgram. As part of the stakeholder engagement process, we operated the model in an iterative way with participating farmers. For ‘deficient’ rainfall season, farmers were in agreement with the model choice of leaving a large portion of the agriculture land as fallow with only 40 ha (total area 136 ha) of cotton and subsistence paddy rice area. While the model crop choice was redgram in ‘above normal and wet seasons, only a few farmers in the village favoured redgram mainly because of high labour requirements, and the farmers perceptions about risks related to pests and diseases. This highlighted the discrepancy between the optimal cropping pattern, calculated with the model and the farmer's actual decisions which provided useful insights into factors affecting farmer decision making that are not always captured by models. We found that planning for a ‘normal’ season alone is likely to result in losses and opportunity costs and an adaptive climate risk management approach is prudent. In an interactive feedback workshop, majority of participating farmers agreed that their knowledge on the utility and challenges of SCF have highly improved through the participation in this research and most agreed that exposure to the model improved their understanding of the role of SCF in crop choice decisions and that the modelling tool was useful to discuss climate risk in agriculture

An integrated crop model and GIS decision support system for assisting agronomic decision making under climate change

The semi-arid tropical (SAT) regions of India are suffering from low productivity which may be further aggravated by anticipated climate change. The present study analyzes the spatial variability of climate change impacts on groundnut yields in the Anantapur district of India and examines the relative contribution of adaptation strategies. For this purpose, a web based decision support tool that integrates crop simulation model and Geographical Information System (GIS) was developed to assist agronomic decision making and this tool can be scalable to any location and crop. The climate change projections of five global climate models (GCMs) relative to the 1980–2010 baseline for Anantapur district indicates an increase in rainfall activity to the tune of 10.6 to 25% during Mid-century period (2040–69) with RCP 8.5. The GCMs also predict warming exceeding 1.4 to 2.4 °C by 2069 in the study region. The spatial crop responses to the projected climate indicate a decrease in groundnut yields with four GCMs (MPI-ESM-MR, MIROC5, CCSM4 and HadGEM2-ES) and a contrasting 6.3% increase with the GCM, GFDL-ESM2M. The simulation studies using CROPGRO-Peanut model reveals that groundnut yields can be increased on average by 1.0%, 5.0%, 14.4%, and 20.2%, by adopting adaptation options of heat tolerance, drought tolerant cultivars, supplemental irrigation and a combination of drought tolerance cultivar and supplemental irrigation respectively. The spatial patterns of relative benefits of adaptation options were geographically different and the greatest benefits can be achieved by adopting new cultivars having drought tolerance and with the application of one supplemental irrigation at 60 days after sowing