Bundling subsurface drip irrigation with no-till provides a window to integrate mung bean with intensive cereal systems for improving resource use efficiency

The future of South Asia’s major production system (rice–wheat rotation) is at stake due to continuously aggravating pressure on groundwater aquifers and other natural resources which will further intensify with climate change. Traditional practices, conventional tillage (CT) residue burning, and indiscriminate use of groundwater with flood irrigation are the major drivers of the non-sustainability of rice–wheat (RW) system in northwest (NW) India. For designing sustainable practices in intensive cereal systems, we conducted a study on bundled practices (zero tillage, residue mulch, precise irrigation, and mung bean integration) based on multi-indicator (system productivity, profitability, and efficiency of water, nitrogen, and energy) analysis in RW system. The study showed that bundling conservation agriculture (CA) practices with subsurface drip irrigation (SDI) saved ~70 and 45% (3-year mean) of irrigation water in rice and wheat, respectively, compared to farmers’ practice/CT practice (pooled data of Sc1 and Sc2; 1,035 and 318 mm ha−1). On a 3-year system basis, CA with SDI scenarios (mean of Sc5–Sc8) saved 35.4% irrigation water under RW systems compared to their respective CA with flood irrigation (FI) scenarios (mean of Sc3 and Sc4) during the investigation irrespective of residue management. CA with FI system increased the water productivity (WPi) and its use efficiency (WUE) by ~52 and 12.3% (3-year mean), whereas SDI improved by 221.2 and 39.2% compared to farmers practice (Sc1; 0.69 kg grain m−3 and 21.39 kg grain ha−1 cm−1), respectively. Based on the 3-year mean, CA with SDI (mean of Sc5–Sc8) recorded −2.5% rice yield, whereas wheat yield was +25% compared to farmers practice (Sc1; 5.44 and 3.79 Mg ha−1) and rice and wheat yield under CA with flood irrigation were increased by +7 and + 11%, compared to their respective CT practices. Mung bean integration in Sc7 and Sc8 contributed to ~26% in crop productivity and profitability compared to farmers’ practice (Sc1) as SDI facilitated advancing the sowing time by 1 week. On a system basis, CA with SDI improved energy use efficiency (EUE) by ~70% and partial factor productivity of N by 18.4% compared to CT practices. In the RW system of NW India, CA with SDI for precise water and N management proved to be a profitable solution to address the problems of groundwater, residue burning, sustainable intensification, and input (water and energy) use with the potential for replication in large areas in NW India

Diverse and healthy cropping systems trial protocol

On-Farm Research Trials are part of TAFSSA’s Work Package 2 (WP2) activities. WP2 emphasizes farm-and landscape-level interdisciplinary research to identify strategies to increase farmers’ profits and nutritional yields, conserve resources, and maintain or enhance ecological services, while also mitigating greenhouse gas (GHG) emissions from farms and agricultural landscapes. Going beyond typical agriculture-nutrition programs in South Asia, we explore field-and landscape-scale crop and animal farm diversification options supporting multiple benefits, including potential nutritional yield, across environmental and socioeconomic gradients of rice and maize-based farming systems. ICAR-CSSRI (Central Soil Salinity Research Institute) Karnal of Haryana in the northwest Indo-Gangetic Plains of India has been selected as basic research and learning site based on key information on food and nutrition security gaps, environmental stresses, air pollution due to residue burning, groundwater exploitation and climate challenges as well as the prevalence of commodities and farming systems that offer the greatest potential to achieve TAFSSA’s outcomes

WP 3: Sustainable farming systems intensification for climate resilient decomposition of yield gaps

From Fragility to Resilience in Central and West Asia and North Africa (F2R CWANA) WP 3: Sustainable farming systems intensification for climate resilient decomposition of yield gaps Inception Workshop Morocco May 17, 2022

Diverse and healthy cropping systems trial protocol

On-Farm Research Trials are part of TAFSSA’s Work Package 2 (WP2) activities. WP2 emphasizes farm-and landscape-level interdisciplinary research to identify strategies to increase farmers’ profits and nutritional yields, conserve resources, and maintain or enhance ecological services, while also mitigating greenhouse gas (GHG) emissions from farms and agricultural landscapes. Going beyond typical agriculture-nutrition programs in South Asia we explore field-and landscape-scale crop and animal farm diversification options supporting multiple benefits, including potential nutritional yield, across environmental and socioeconomic gradients of rice and maize-based farming systems. Nalanda district of Bihar in eastern India has been selected as a learning site based on key information on food and nutrition security gaps, environmental stresses and climate challenges as well as the prevalence of commodities and farming systems that offer the greatest potential to achieve TAFSSA’s outcomes

Analysing performance of SLEUTH model calibration using brute force and genetic algorithm–based methods

Present study is aimed to compare the performance of SLEUTH model from two different calibration methods, that is, brute force and GA in term of computational efficiency of calibration processes, capturing urban growth, a form of growth or growth pattern and its spatial distribution. SLEUTH has been parameterized for Ajmer city (India) and its performance has been compared in term of eight parameters/methods, that is, computational efficiency, model fitness that is, OSM, urban shape index, best fit coefficient values, hit-miss-false alarm method, kappa statistics, accuracy percentage and visual analysis. GA-based calibration has been found to be computationally more efficient and relatively better in capturing urban growth and form of growth as compared to brute force. Brute force calibration seems to be slightly better considering urban hits as compared to GA, however, GA is better with respect to lesser false alarms

Sensitivity analysis and retrieval of optimum SLEUTH model parameters

The Cellular Automata (CA) based SLEUTH model has emerged as a widely applied model to many cities for land use land cover (LULC) change and urban growth modelling due to its simplicity, robustness, and ease of implementation. The present study employed a rigorous sensitivity testing of self-modifying constants, Monte Carlo runs and critical slope to determine their influence on model calibration performance. Calibration performance has been examined in terms of statistical measures i.e., urban area, clusters, edges, mean cluster size, and cluster radius, best model fitness measure (i.e., Optimal SLEUTH Metrics (OSM)), overall accuracy percentage and hit-miss-false alarm method have been used. The sensitivity analysis reveals the optimum values for self-modifying parameters as {1.3, 0.10, 0.90, and 1.25} for boom, bust, critical low and critical high respectively; Monte Carlo runs as sixty (60) and critical slope as 15 to simulate the urban growth of the study area

Application of geo-spatial techniques and cellular automata for modelling urban growth of a heterogeneous urban fringe

Urban growth monitoring and assessment are essential for the sustainable natural resources planning & optimum utilization and reducing the risk of problems arising from unplanned urban growth like pollution, urban heat island and ecological disturbances. Cellular Automata (CA) based modelling techniques have become popular in recent past for simulating the urban growth. Present study is aimed to evaluate the performance of the CA based SLEUTH model in simulating the urban growth of a complex and relatively more heterogeneous urban area, Ajmer city of Rajasthan (India) which is quite different as compared to areas where SLEUTH has been tested in developed countries. Seven multispectral satellite imageries spanning over 21 years have been processed and used for SLEUTH parameterisation. Results of urban growth predicted by SLEUTH has been compared with other methods of land use/land cover extraction. The study has been proved to be successful in giving significant insight into issues contributing uncertainties in forecasting of urban growth of heterogeneous urban areas

Prediction of land use land cover changes of a river basin using the CA-Markov model

Simulation and prediction of land use land cover (LULC) has been presented using Hybrid CA-Markov model for the Mahi River basin. LULC information for the year 2000, 2010, and 2020 have been obtained from classification of Landsat TM, IRS -LISS III, and Sentinel 2 sensors, respectively. Fuzzy membership function and Analytical Hierarchical Process (AHP), a Multi-Criteria Evaluation (MCE) have been used for LULC change suitability map preparation. Population density, distance from road, settlement, streams, and reservoir/lakes, slope, and DEM are used as biophysical and socio-economic LULC change drivers. Model results have been validated for year 2020 and accuracy has been found as satisfactory 2020. Results revealed a significant change in forest, agriculture, barren and built-up LULC classes during different years. Agriculture and the built-up area may increase by 641 and 21 sq.km, respectively in 2030. The predicted LULC information provides necessary data to investigate the future hydrological and climatological scenario

RETRACTED: Characterization of wheat (Triticum aestivum L.) genotypes unraveled by molecular markers considering heat stress

The current study focuses and emphasis on the potential of heat stress to negatively affect crop physiology. Here, we have screened 19 wheat (Triticum aestivum L.) genotypes for their tolerance of heat stress. Significant differences were observed among the genotypes for all the traits under consideration. Exploitable extent of genetic variability amongst the entries was present as revealed by considerably higher estimates of mean %. On the basis of Heat susceptibility Index, Halna, Mon’s Ald’s, genotypes Cuo/79/Prulla and K 307 were identified as heat-tolerant whereas SAWSN 3041, SAWSN 3101 and K 0583 were identified as heat-susceptible. The 17 wheat microsatellite markers were capable of detecting 89 alleles with an average of 4.6 alleles per locus. Polymorphism Information Content value ranged from 0.16 for the primer XGWM 516 to 0.83 for DUPW 117 with an average of 0.60. A perusal of similarity coefficients clearly reflected that a very high degree of similarity exists between wheat variety Mon’s Ald’s and SAWSN 3101 (0.70). On the other hand, the two most distantly related cultivars were found to be AKAW 4008 and PBW 343 (0.034). BARC 4, BARC 170, BARC 311, PSP 3058, WHE014.H04 and GWM 458 were strongly associated with the heat tolerance for traits TGW and BARC 311 was strongly associated with terminal heat tolerance for number of grains/plant respectively. Considering all the parameters it is adjudged that relatively stable genotypes may be evaluated at various agro climatic regions for grain yield and heat tolerance along with other contributing characters and ideal plant type