Climate-smart agricultural practices influence the fungal communities and soil properties under major agri-food systems

Fungal communities in agricultural soils are assumed to be affected by climate, weather, and anthropogenic activities, and magnitude of their effect depends on the agricultural activities. Therefore, a study was conducted to investigate the impact of the portfolio of management practices on fungal communities and soil physical–chemical properties. The study comprised different climate-smart agriculture (CSA)-based management scenarios (Sc) established on the principles of conservation agriculture (CA), namely, ScI is conventional tillage-based rice–wheat rotation, ScII is partial CA-based rice–wheat–mungbean, ScIII is partial CSA-based rice–wheat–mungbean, ScIV is partial CSA-based maize–wheat–mungbean, and ScV and ScVI are CSA-based scenarios and similar to ScIII and ScIV, respectively, except for fertigation method. All the scenarios were flood irrigated except the ScV and ScVI where water and nitrogen were given through subsurface drip irrigation. Soils of these scenarios were collected from 0 to 15 cm depth and analyzed by Illumina paired-end sequencing of Internal Transcribed Spacer regions (ITS1 and ITS2) for the study of fungal community composition. Analysis of 5 million processed sequences showed a higher Shannon diversity index of 1.47 times and a Simpson index of 1.12 times in maize-based CSA scenarios (ScIV and ScVI) compared with rice-based CSA scenarios (ScIII and ScV). Seven phyla were present in all the scenarios, where Ascomycota was the most abundant phyla and it was followed by Basidiomycota and Zygomycota. Ascomycota was found more abundant in rice-based CSA scenarios as compared to maize-based CSA scenarios. Soil organic carbon and nitrogen were found to be 1.62 and 1.25 times higher in CSA scenarios compared with other scenarios. Bulk density was found highest in farmers' practice (Sc1); however, mean weight diameter and water-stable aggregates were found lowest in ScI. Soil physical, chemical, and biological properties were found better under CSA-based practices, which also increased the wheat grain yield by 12.5% and system yield by 18.8%. These results indicate that bundling/layering of smart agricultural practices over farmers' practices has tremendous effects on soil properties, and hence play an important role in sustaining soil quality/health

State Level Maize Days

Northwest India has become a major challenge owing to faster depletion of groundwater table, stagnating or declining productivity growth, degrading soil health and environmental quality including air pollution and public health concerns, and diminishing farm profitability. Therefore, diversification of rice crop is the need of hour to sustain the declining natural resources to ensure food security in long-run in Punjab and Haryana. To save earth and to save health, paddy is to be replaced with maize. Currently this coarse grain is cultivated in about 10.2 million ha in India. The increasing interest of the consumers in nutritionally enriched products and rising demand as poultry feed, which accounts 47% of total maize consumption, are the driving forces behind increasing consumption of maize in the country. State Levels Maize Days organized this year in Punjab and Hariyana states of northwest India has gained in stature and popularity on account of the focus on covering very relevant participation from right stakeholders and covering topical issues. The programmes were focused on solutions that will help increase maize productivity by building efficiencies in entire chain and thus generating higher value for the farmers. Scientists and private partners to convince farmers with credible solution to enhance credibility by giving a proof of the technology at the farmers’ field. Timely availability of the input is critical for farmers and providing good market is essential for realisation of diversification with maize replacing rice. In these programmes, knowledge on improved agronomic production technologies and agro-inputs such as seeds of improved maize varieties, herbicides, and pesticides to control fall armyworm have been distributed to the farmers

Conservation Agriculture and Scale of Appropriate Agricultural Mechanization in Smallholder Systems

This manual has focused on the need to amplify and accelerate adoption of conservation agriculture (CA) practices that enable productivity increases on a sustainable basis. The development of the training manual on ‘Conservation Agriculture and Scale Appropriate Agricultural Mechanization in Smallholder Systems’ is an outcome of the series of advanced training programs on Conservation Agriculture over past one decade. The objectives of this training manual are; (1) To foster capacity building of researchers, extension workers, farmers and machinery manufacturers to promote CA in Asia and Africa; and (2) To raise the awareness of policy planners and decision makers to develop a strategic plan for the development of CA and agricultural mechanization in the developing world. There are several initiatives in South Asia and Africa to promote CA practices as environment-friendly and alternative to conventional agriculture. However, little has been done to document the CA practices or even lessons learnt from these initiatives. Farmers today still lack access to information on CA practices. This is a comprehensive manual that explains in a step by step easy to follow manner on how to implement CA by smallholders in Asia and Africa. It explains what CA is, and why it is important, how to use CA principles in the field and highlights the issues and challenges that researchers, farmers, machinery manufacturers and service providers may encounter when they adopt and adapt CA practices. This manual aims to be a valuable reference and is intended for use by researchers, agricultural extension officers/workers, farmers, machinery manufacturers and service providers to promote CA in Asia and Africa for increasing productivity and reducing poverty. It is written in clear, easy-to-understand language, and is illustrated with numerous figures and tables. It is not intended to cover the subject of conservation agriculture comprehensively but to provide an overview of the principles and practices. Indeed, as the training draws from many distinct disciplines, it is unlikely that any one person will have the necessary technical skills to cover the complete course content. Manual also focuses on two crucial aspects: the provision of farm mechanization services as a viable business opportunity for entrepreneurs, and the essential criteria of raising productivity in an environmentally sensitive and responsible way. This manual is also designed to serve as source of information for custom hire service providers – whether already in the business or intending to start their own hire service business – with skills and competencies in both the technical and the management aspects of the small-scale mechanization business. CA to reach smallholder farmers needed the publication of simplified technical manual. This manual contains useful technical information on CA practices that offer practical answers to questions normally asked by farmers of what, why, how

Behaviour of Quality Protein Maize (QPM) genotypes under well irrigated and water stress conditions in subtropical climate

Drought or water stress is one of the prime problems affecting production of maize at global level. A major objective of QPM breeding programs in semi arid tropics or subtropical climatic conditions is to increase genetic potential of QPM genotypes under water stress conditions. In order to identify drought tolerant single cross QPM hybrids an experiment with 85 genotypes was conducted under well irrigated and water stress conditions. Six drought tolerance indices viz, mean productivity (MP), geometric mean productivity (GMP), yield index (YI), tolerance index (TOL), stress susceptibility index (SSI), and superiority measures (SM) were used on the basis of grain yield in water stress (Ys) and well irrigated (Yp) conditions. Highest significant positive correlations were observed among MP, GMP and YI indices. The hybrids 75, 38, 27, and 50 were more drought tolerant based on drought tolerance indices. Three dimensional plot, bi-plot and cluster analysis confirmed these results. Principal component analysis reduced six indices down to two components with 90.71% proportional cumulative variance. Genotypes were grouped by two ways cluster analysis (using Ward’s method) based on Yp, Ys and drought tolerance indices. Also, the results of correlation, 3D graphs, bi-plot and cluster analysis reveals that the most suitable indices to screen QPM genotypes in drought stress conditions were MP, GMP and YI. These indices could be used in QPM breeding programs to introduce drought tolerance in single cross hybrids

The optimization of conservation agriculture practices requires attention to location-specific performance: Evidence from large scale gridded simulations across South Asia

The ways in which farmers implement conservation agricultural (CA) practices – which entail reduced tillage, maintenance of soil cover, and crop rotations – varies considerably in different environments, farming systems, and by the intensity with which farmers administer management practices. Such variability requires an efficient tool to evaluate the cost-benefit of CA, to inform agricultural policymakers and development priorities to facilitate expanded use of CA under appropriate circumstances. Rice-wheat rotation is the principal production system in South Asia (SA). Research has shown that CA can be promising in this rotation because of improved irrigated water, energy, and labor use efficiencies, in addition to the reduction in atmospheric pollution and potentially long term improvements in soil quality. Yield responses to CA are however varying across studies and regions. With a nine-year rice-wheat CA experiment in Eastern Gangetic Plains of South Asia, this study parameterizes the Environmental Policy Climate (EPIC) model to simulate five CA and conventional managements on the RW cropping system. Information from geospatial datasets and farm surveys were used to parameterize the model at the regional scale, increasing the management flexibility and range of localities in the simulation. Yield potential of the CAs in the whole SA was thereby explored by utilizing the model with various management strategies. Our results demonstrate how geospatial and survey data, along with calibration by a long-term experiment, can supplement a regional simulation to increase the model's ability to capture yield patterns. Yield gains from CA are widespread but generally low under current management regimes, with varied yield responses among CAs and environments. Conversely, CA has considerable potential in SA to increase rice-wheat productivity by up to 38%. Our results highlight the importance of applying an adaptive definition of CA, depending on environmental circumstances, while also building the capacity of farmers interested in CA to apply optimal management practices appropriate for their environment

Influence of residue type and method of placement on dynamics of decomposition and nitrogen release in maize-wheat-mungbean cropping on permanent raised beds: a litterbag study

Decomposition influences carbon and nutrient cycling from crop residues. The nylon-mesh-bag technique was implied to study the decomposition and N-release dynamics from different crop residues under field conditions. The four types of residues were: maize (lower than 50% below the cob), wheat (lower than 25% of wheat stubbles), a whole mung bean residue, and a mixture of wheat + mung bean residue (1:1 ratio) put on the soil surface and in below the sub-surface. Decomposition and N release from both at-surface- and below-surface-placed residues were accurately described by a single-pool first-order exponential decay function as a function of thermal time (based on the accumulative daily mean temperature). The simple first-order exponential model met the criteria of goodness of fit. Throughout the decomposition cycle (one thermal year), the rate of decomposition as measured by a decrease in residue mass and the release of total N were statistically higher from the sub-surface compared to the surface-placed residue, irrespective of the residue type. At the end of the 150-day decomposition cycle, the release of total N was highest in mung bean (32.0 kg N ha−1), followed by maize (31.5 kg N ha−1) > wheat + mung bean (16.1 kg N ha−1), and the minimum (6.54 kg N ha−1) in wheat residue. Crop residues with a wider C/N ratio such as maize and wheat, when applied on the soil surface in conservation agriculture, caused the decomposition to occur at slower rates, thereby providing long-term beneficial effects on the soil thermal regime, soil moisture conservation, and C sequestration in North-West India

Biological nitrogen fixation and prospects for ecological intensification in cereal-based cropping systems

The demand for nitrogen (N) for crop production increased rapidly from the middle of the twentieth century and is predicted to at least double by 2050 to satisfy the on-going improvements in productivity of major food crops such as wheat, rice and maize that underpin the staple diet of most of the world's population. The increased demand will need to be fulfilled by the two main sources of N supply – biological nitrogen (gas) (N2) fixation (BNF) and fertilizer N supplied through the Haber-Bosch processes. BNF provides many functional benefits for agroecosystems. It is a vital mechanism for replenishing the reservoirs of soil organic N and improving the availability of soil N to support crop growth while also assisting in efforts to lower negative environmental externalities than fertilizer N. In cereal-based cropping systems, legumes in symbiosis with rhizobia contribute the largest BNF input; however, diazotrophs involved in non-symbiotic associations with plants or present as free-living N2-fixers are ubiquitous and also provide an additional source of fixed N. This review presents the current knowledge of BNF by free-living, non-symbiotic and symbiotic diazotrophs in the global N cycle, examines global and regional estimates of contributions of BNF, and discusses possible strategies to enhance BNF for the prospective benefit of cereal N nutrition. We conclude by considering the challenges of introducing in planta BNF into cereals and reflect on the potential for BNF in both conventional and alternative crop management systems to encourage the ecological intensification of cereal and legume production

Rice yield gaps and nitrogen-use efficiency in the Northwestern Indo-Gangetic Plains of India: Evidence based insights from heterogeneous farmers’ practices

A large database of individual farmer field data (n = 4,107) for rice production in the Northwestern Indo-Gangetic Plains of India was used to decompose rice yield gaps and to investigate the scope to reduce nitrogen (N) inputs without compromising yields. Stochastic frontier analysis was used to disentangle efficiency and resource yield gaps, whereas data on rice yield potential in the region were retrieved from the Global Yield Gap Atlas to estimate the technology yield gap. Rice yield gaps were small (ca. 2.7 t ha−1, or 20% of potential yield, Yp) and mostly attributed to the technology yield gap (ca. 1.8 t ha−1, or ca. 15% of Yp). Efficiency and resource yield gaps were negligible (less than 5% of Yp in most districts). Small yield gaps were associated with high input use, particularly irrigation water and N, for which small yield responses were observed. N partial factor productivity (PFP-N) was 45–50 kg grain kg−1 N for fields with efficient N management and approximately 20% lower for the fields with inefficient N management. Improving PFP-N appears to be best achieved through better matching of N rates to the variety types cultivated and by adjusting the amount of urea applied in the 3rd split in correspondance with the amount of diammonium-phosphate applied earlier in the season. Future studies should assess the potential to reduce irrigation water without compromising rice yield and to broaden the assessment presented here to other indicators and at the cropping systems level

Interpretable machine learning methods to explain on-farm yield variability of high productivity wheat in Northwest India

The increasing availability of complex, geo-referenced on-farm data demands analytical frameworks that can guide crop management recommendations. Recent developments in interpretable machine learning techniques offer opportunities to use these methods in agronomic studies. Our objectives were two-fold: (1) to assess the performance of different machine learning methods to explain on-farm wheat yield variability in the Northwestern Indo-Gangetic Plains of India, and (2) to identify the most important drivers and interactions explaining wheat yield variability. A suite of fine-tuned machine learning models (ridge and lasso regression, classification and regression trees, k-nearest neighbor, support vector machines, gradient boosting, extreme gradient boosting, and random forest) were statistically compared using the R2, root mean square error (RMSE), and mean absolute error (MAE). The best performing model was again fine-tuned using a grid search approach for the bias-variance trade-off. Three post-hoc model agnostic techniques were used to interpret the best performing model: variable importance (a variable was considered “important” if shuffling its values increased or decreased the model error considerably), interaction strength (based on Friedman’s H-statistic), and two-way interaction (i.e., how much of the total variability in wheat yield was explained by a particular two-way interaction). Model outputs were compared against empirical data to contextualize results and provide a blueprint for future analysis in other production systems. Tree-based and decision boundary-based methods outperformed regression-based methods in explaining wheat yield variability. Random forest was the best performing method in terms of goodness-of-fit and model precision and accuracy with RMSE, MAE, and R2 ranging between 367 and 470 kg ha−1, 276–345 kg ha−1, and 0.44–0.63, respectively. Random forest was then used for selection of important variables and interactions. The most important management variables explaining wheat yield variability were nitrogen application rate and crop residue management, whereas the average of monthly cumulative solar radiation during February and March (coinciding with reproductive phase of wheat) was the most important biophysical variable. The effect size of these variables on wheat yield ranged between 227 kg ha−1 for nitrogen application rate to 372 kg ha−1 for cumulative solar radiation during February and March. The effect of important interactions on wheat yield was detected in the data namely the interaction between crop residue management and disease management and, nitrogen application rate and seeding rate. For instance, farmers’ fields with moderate disease incidence yielded 750 kg ha−1 less when crop residues were removed than when crop residues were retained. Similarly, wheat yield response to residue retention was higher under low seed and N application rates. As an inductive research approach, the appropriate application of interpretable machine learning methods can be used to extract agronomically actionable information from large-scale farmer field data

Conservation Agriculture Benefits Indian Farmers, but Technology Targeting Needed for Greater Impacts

Rice and wheat production in the intensive, irrigated farming systems of the Indo-Gangetic Plains (IGP) is associated with significant negative environmental and health externalities. Conservation Agriculture (CA) has the potential to curb some of these externalities while enhancing farm income. However, farmer adoption of CA remains modest in the Indian IGP. The present study focuses on the constraints to adopting the major CA component, zero tillage (ZT). We examine whether ZT wheat is feasible for smallholders and the potential of technology targeting to realize faster and wider diffusion. Econometric models and machine learning algorithms were used to analyze remote sensing data and farm household data collected from the Indian states of Punjab and Bihar, two contrasting agrarian economies of the IGP. While farmer adoption was low among smallholders (owning <2 ha of land), the on-farm effects of ZT on variable cost reduction and yield and profit enhancement for smallholders are comparable to large farmers. We estimate the economic potential of technology targeting using an equilibrium displacement model. In the relatively developed state of Punjab, technology targeting based on landholding size does not appear to add substantive economic benefits. In Bihar, a less prosperous state with a dominance of smallholders in the population, technology targeting could markedly enhance economic surplus and reduce rural poverty