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

Farmers’ perspectives as determinants for adoption of conservation agriculture practices in Indo-Gangetic Plains of India

Understanding the farmer's perspective has traditionally been critical to influencing the adoption and out-scaling of CA-based climate-resilient practices. The objective of this study was to investigate the biophysical, socio-economic, and technical constraints in the adoption of CA by farmers in the Western- and Eastern-IGP, i.e., Karnal, Haryana, and Samastipur, Bihar, respectively. A pre-tested structured questionnaire was administered to 50 households practicing CA in Western- and Eastern-IGP. Smallholder farmers (<2 ha of landholding) in Karnal are 10% and Samastipur 66%. About 46% and 8% of households test soil periodically in Karnal and Samastipur, respectively. Results of PCA suggest economic profitability and soil health as core components from the farmer's motivational perspective in Karnal and Samastipur, respectively. Promotion and scaling up of CA technologies should be targeted per site-specific requirements, emphasizing biophysical resource availability, socio-economic constraints, and future impacts of such technology

Energy and economic efficiency of climate-smart agriculture practices in a rice–wheat cropping system of India

Intensive tillage operations, indiscriminate use of irrigation water, chemical fertilizers, and pesticides and crop biomass burning have made the conventional rice–wheat (RW) system highly energy-intensive and inefficient. In the recent past, portfolios of climate-smart agricultural practices (CSAP) have been promoted as a potential alternative to improve the energy efficiency in conventional RW system. Therefore, to evaluate the energy input–output relation, energy flow and economic efficiency in various combinations of crop management options, a 3-year (2014–2017) on-farm study was conducted at Karnal, India. Various portfolio of management practices; Sc1-Business as usual (BAU) or Conventional tillage (CT) without residue, Sc2-CT with residue, Sc3-Reduce tillage (RT) with residue + recommended dose of fertilizer (RDF), Sc4-RT/Zero tillage (ZT) with residue + RDF, Sc5-ZT with residue + RDF + GreenSeeker + Tensiometer, Sc6-Sc5 + Nutrient expert were investigated. Present study results revealed that net energy, energy use efficiency and energy productivity were 11–18, 31–51 and 29–53% higher under CSAP (mean of Sc4, Sc5 and Sc6) in RW system than Sc1, respectively. However, renewable and non-renewable energy inputs were 14 and 33% higher in Sc1 compared to CSAP (4028 and 49,547 MJ ha−1), respectively, it showed that BAU practices mostly dependents on non-renewable energy sources whereas CSAP dependents on renewable energy sources. Similarly, the adoption of CSAP improved the biomass yield, net farm income and economic efficiency by 6–9, 18–23 and 42–58%, respectively compared to Sc1. Overall, the adoption of CSAP could be a viable alternative for improving energy use efficiency, farm profitability and eco-efficiency in the RW system

Can agroecological transition of intensive cereal system of Indo-Gangetic plains deliver sustainable and nutritious food?

As of Jan. 18, 2023 this article is listed as a pre-print and as such has not been peer reviewed.Indo-Gangetic plains (IGP) of South Asia have supported bulk of human and bovine population in the region since ages, and a spectacular progress has been made here on food production. However, this cereal-system-dominated region still suffers with challenges of malnourishment, declining total factor productivity and natural resource degradation with potential threats of climate change. Addressing these challenges would require a transition towards agroecological cropping systems. A study was, therefore, conducted on crop diversification and sustainable intensification options using agro-ecological approaches such as Conservation Agriculture (CA) to ensure food and nutritional security while sustaining the natural resources. On 2 years mean basis, CA-based cropping system management scenarios (mean of Sc2-Sc7) using diversified rotations; increased the system yield by 15.4%, net return by 28.7%, protein yield by 29.7%while using 53.0% less irrigation water compared to conventional tillage (CT)-based rice-wheat system (Sc1). Maize-mustard-mungbean on permanent beds (Sc4) recorded the highest productivity (+40.7%), profitability (+60.1%), and saved 81.8% of irrigation water compared to Sc1 (11.8 Mg ha-1; 2190 USD ha-1; 2514 mm ha-1). It was closely followed by Sc5 (32.3, 57.4, 413.8, 75.5%) i.e. maize-wheat-mungbean on permanent beds. In terms of nutritional value, Sc5 was more balanced than other scenarios, and produced 43.8, 27.5 and 259.8% higher protein, carbohydrate and fat yields, respectively, compared to Sc1 (0.93, 8.55 and 0.14 Mg ha-1). Scenario 5 was able to meet the nutrient demand of 19, 23 and 32 more persons ha-1 year-1 with respect to protein, carbohydrate and fat demand, respectively, compared to Sc1 (44, 86 and 13 persons ha-1 year-1).However, the highest protein and fat yield and their adult equivalents was associated with Sc6 (soybean based) and Sc4 (maize based), respectively. Soybean based system (Sc6) was economically more efficient with respect to nutrients than other systems. Mungbean integration improved the system productivity by 17.2 % and profitability by 32.1%, while improving the irrigation water productivity by three times compared to CT-based systems. In western IGP, CA-based maize-wheat-mungbean system was the most productive, profitable and nutritionally rich and efficient system compared to other systems. Therefore, CA- based crop diversification is an option to ensure quality and nutritious food for the dwelling communities in the region

Scalable diversification options delivers sustainable and nutritious food in Indo-Gangetic plains

Indo-Gangetic plains (IGP) of South Asia have supported bulk of human and bovine population in the region since ages, and a spectacular progress has been made in food production. However, malnutrition, diminishing total factor productivity, and natural resource degradation continue to plague this cereal-dominated region, which is also vulnerable to climate change. Addressing these challenges would require a transition towards diversifying cereal rotations with agroecological cropping systems. A study was, therefore, conducted at the experimental farm of ICAR-CSSRI, Karnal on crop diversification and sustainable intensification options using agro-ecological approaches such as Conservation Agriculture (CA) and diversified cropping systems to ensure food and nutritional security while sustaining the natural resources. On 2 years mean basis, CA-based cropping system management scenarios (mean of Sc2–Sc7) using diversified crop rotations; increased the system yield by 15.4%, net return by 28.7%, protein yield by 29.7%, while using 53.0% less irrigation water compared to conventional tillage (CT)-based rice–wheat system (Sc1). Maize-mustard-mungbean on permanent beds (PBs) (Sc4) recorded the highest productivity (+ 40.7%), profitability (+ 60.1%), and saved 81.8% irrigation water compared to Sc1 (11.8 Mg ha−1; 2190 USD ha−1; 2514 mm ha−1). Similarly, Sc5 (maize-wheat-mungbean on PBs) improved productivity (+ 32.2%), profitability (+ 57.4%) and saved irrigation water (75.5%) compared to Sc1. In terms of nutritional value, Sc5 was more balanced than other scenarios, and produced 43.8, 27.5 and 259.8% higher protein, carbohydrate and fat yields, respectively, compared to Sc1 (0.93, 8.55 and 0.14 Mg ha−1). Scenario 5 was able to meet the nutrient demand of 19, 23 and 32 additional persons ha−1 year−1 with respect to protein, carbohydrate and fat, respectively, compared to Sc1. The highest protein water productivity (~ 0.31 kg protein m−3 water) was recorded with CA-based soybean-wheat-mungbean (Sc6) system followed by maize-mustard-mungbean on PBs (Sc4) system (~ 0.29 kg protein m−3) and lowest under Sc1. Integration of short duration legume (mungbean) improved the system productivity by 17.2% and profitability by 32.1%, while triple gains in irrigation water productivity compared to CT-based systems. In western IGP, maize-wheat-mungbean on PBs was found most productive, profitable and nutritionally rich and efficient system compared to other systems. Therefore, diversification of water intensive cereal rotations with inclusion of legumes and CA-based management optimization can be potential option to ensure nutritious food for the dwelling communities and sustainability of natural resources in the region

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

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

A decade of climate-smart agriculture in major agri-food systems: Earthworm abundance and soil physico-biochemical properties

Earthworms (EWs) could be a viable indicator of soil biology and agri-food system management. The influence of climate-smart agriculture (CSA)-based sustainable intensification practices (zero tillage, crop rotations, crop residue retention, and precision water and nutrients application) on earthworms’ (EWs) populations and soil physico-biochemical properties of rice-wheat cropping system in the Indo-Gangetic plains of South Asia was investigated. This study investigates the effect of 10-years adoption of various CSA practices on the abundance of earthworms and physical and biochemical properties of the soil and EWs’ casts (EWC). Five scenarios (Sc) were included: conventionally managed rice-wheat system (farmers’ practices, Sc1), CSA-based rice-wheat-mungbean system with flood irrigation (FI) (Sc2) and subsurface drip irrigation (SDI) (Sc3), CSA-based maize-wheat-mungbean system with FI (Sc4), and SDI (Sc5). Results revealed that EWs were absent under Sc1, while the 10-year adoption of CSA-based scenarios (mean of Sc2–5) increased EWs’ density and biomass to be 257.7 no. m−2 and 36.05 g m−2, respectively. CSA-based maize scenarios (Sc4 and Sc5) attained higher EWs’ density and biomass over rice-based CSA scenarios (Sc2 and Sc4). Also, SDI-based scenarios (Sc3 and Sc5) recorded higher EWs’ density and biomass over FI (Sc2 and Sc4). Maize-based CSA with SDI recorded the highest EWs’ density and EWs’ biomass. The higher total organic carbon in EWC (1.91%) than in the bulk soil of CSA-based scenarios (0.98%) and farmers’ practices (0.65%) suggests the shift of crop residue to a stable SOC (in EWC). EWC contained significant amounts of C and available NPK under CSA practices, which were nil under Sc1. All CSA-based scenarios attained higher enzymes activities over Sc1. CSA-based scenarios, in particular, maize-based scenarios using SDI, improved EWs’ proliferation, SOC, and nutrients storage (in soil and EWC) and showed a better choice for the IGP farmers with respect to C sequestration, soil quality, and nutrient availability

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

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