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

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

A Compendium of Key Climate Smart Agriculture Practices in Intensive Cereal Based Systems of South Asia

CSA initially proposed by FAO in 2010 at “The Hague Conference on Agriculture, Food Security and Climate Change (CC)”, to address the need for a strategy to manage agriculture and food systems, under climate change. The CSA by its original proponents describes the three objectives; i) sustainably increasing agricultural productivity to support equitable increases in incomes, food security and development; ii) adapting and building resilience to climate change from the farm to national levels; and iii) developing opportunities to reduce GHG emissions from agriculture compared with past trends. Since then, these three objectives (in short food security, adaptation and mitigation) are designated as the three “pillars” (or criteria) of CSA within the agricultural science and development communities. Climate Smart (Sustainable Management of Agricultural Resources and Techniques) Agriculture is an approach of crop production, which deals with the management of available agricultural resources with latest management practices and farm machinery, under a particular set of edaphic and environmental conditions. It works to enhance the achievement of national food security and Sustainable Development Goals (SDGs). CSA is location specific and tailored to fit the agro-ecological and socio-economic conditions of a location. CSA may be defined as “agriculture that sustainably increases productivity, resilience (adaptation), reduces/removes greenhouse gases (mitigation), and enhances achievement of national food security and development goals.” Therefore, if CSA implemented at right time with required resources, techniques and knowledge in a particular typological domain, will lead towards food security while improving adaptive capacity and mitigating potential for sustainable agriculture production

Impact of Different Salt-stressed Conditions on Growth, Mineral Nutrition and Fruiting of Bael (Aegle marmelos) Cultivars

Bael (Aegle marmelos) cultivars (NB-5, NB-9, CB-1 and CB-2) grown in normal (ECe 1.3 dS m-1) and saline soils (ECe 6.5 and 10.7 dS m-1) exhibited initial signs of salt injury as yellowing and scorching of leaves followed by leaf necrosis, abscission and wilting of plants. After 24 months of exposure to 6.5 dS m-1 salinity, the minimum (9.5%) and the maximum (58%) decreases in plant height were noted in NB-5 and CB-2 cultivars, respectively. High salinity (10.7 dS m-1) severely suppressed growth in all the cultivars, and NB-9 and CB-2 plants did not survive. Data on ion partitioning indicated restricted uptake of Na+ ions by NB-5 plants at moderate salinity. At high salinity, however, Na+ accumulation significantly increased in leaves and stems indicating soil saturation extract salinity (ECe) tolerance threshold limit of 6.0-7.0 dS m-1 in this cultivar. All the tested cultivars except CB-2 produced fruits in moderately saline soils with average number of fruits per plant being 2 in NB-5 and 0.67 in each NB-9 and CB-1 cultivars. However, fruit yield contributing traits like fruit length, breadth and weight significantly declined in salinized plants regardless of the cultivar. Salt stressed NB-5 plants not only prevented the accumulation of Na+ to toxic levels but also exhibited higher K+ concentrations in different parts resulting in better growth in saline soils.ICA

Genotypic differences for salt tolerance in bael (Aegle marmelos) cultivars

Salinity induced changes in physiological relations and the concomitant effects on plant growth were recorded in four bael (Aegle marmelos Correa) cultivars, viz. NB-5, NB-9, CB-1 and CB-2. Plants raised in normal soil (ECe 1.3 dS/m) were irrigated with tap (ECiw 0.5 dS/m) and saline (ECiw 3 and 6 dS/m) waters. Data were recorded for growth, physiological parameters and mineral nutrition 180 days after imposing the salt treatments. NB-5 outperformed other cultivars under saline conditions by maintaining higher leaf chlorophyll and proline levels, retaining Na+ ions in stem and root tissues and by preferentially accumulating K+ and Ca2+ ions to overcome the toxic effects of Na+. Break down of salt tolerance in other cultivars at 6 dS/m salinity can be explained by build up of Na+ to the toxic levels and an accompanying decrease in leaf and stem K+ concentrations. Based on these findings, bael cultivar NB-5 appears to be suitable for commercial cultivation in salt-affected soils.ICA

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Not AvailableTo explore the effect of salt stress on photosynthetic traits and gene expression in Indian mustard, four genotypes CS 54 (national check for salinity), CS 52-SPS- 1-2012 (salt tolerant mutant), CS 614-4-1-4-100-13 (salt sensitive mutant) and Pusa bold (high yielding variety) were evaluated under irrigation water salinity (ECiw 12, and 15 dS m-1). Results suggest genotype CS 52-SPS-1- 2012 followed by CS 54 performed better under imposed salt stress due to differential regulation of Na? accumulation in the roots and main stem, restriction of Na? influx from root to shoot, maintaining higher net photosynthetic traits under saline stress compared to CS 614-4-1-4-100-13 and Pusa bold. Further, overexpression of antiporters (SOS1, SOS2, SOS3, ENH1 and NHX1) and antioxidant (APX1, APX4, DHAR1 and MDHAR) genes in salt tolerant genotypes CS 52-SPS-1-2012 and CS 54 demonstrated their significant role in imparting salt tolerance in Indian mustard.Not Availabl

Topsoil Bacterial Community Changes and Nutrient Dynamics Under Cereal Based Climate-Smart Agri-Food Systems

Soil microorganisms play a critical role in soil biogeochemical processes, nutrient cycling, and resilience of agri-food systems and are immensely influenced by agronomic management practices. Understanding soil bacterial community and nutrient dynamics under contrasting management practices is of utmost importance for building climate-smart agri-food systems. Soil samples were collected at 0–15 cm soil depth from six management scenarios in long-term conservation agriculture (CA) and climate-smart agriculture (CSA) practices. These scenarios (Sc) involved; ScI-conventional tillage based rice-wheat rotation, ScII- partial CA based rice-wheat-mungbean, ScIII- partial CSA based rice-wheat-mungbean, ScIV is partial CSA based maize-wheat-mungbean, ScV and ScVI are CSA based scenarios, were similar to ScIII and ScIV respectively, layered with precision water & nutrient management. The sequencing of soil DNA results revealed that across the six scenarios, a total of forty bacterial phyla were observed, with Proteobacteria as dominant in all scenarios, followed by Acidobacteria and Actinobacteria. The relative abundance of Proteobacteria was 29% higher in rice-based CSA scenarios (ScIII and ScV) and 16% higher in maize-based CSA scenarios (ScIV and ScVI) compared to conventional-till practice (ScI). The relative abundance of Acidobacteria and Actinobacteria was respectively 29% and 91% higher in CT than CSA based rice and 27% and 110% higher than maize-based scenarios. Some taxa are present relatively in very low abundance or exclusively in some scenarios, but these might play important roles there. Three phyla are exclusively present in ScI and ScII i.e., Spirochaetes, Thermi, and Euryarchaeota. Shannon diversity index was 11% higher in CT compared to CSA scenarios. Maize based CSA scenarios recorded higher diversity indices than rice-based CSA scenarios. Similar to changes in soil bacterial community, the nutrient dynamics among the different scenarios also varied significantly. After nine years of continuous cropping, the soil organic carbon was improved by 111% and 31% in CSA and CA scenarios over the CT scenario. Similarly, the available nitrogen, phosphorus, and potassium were improved by, respectively, 38, 70, and 59% in CSA scenarios compared to the CT scenario. These results indicate that CSA based management has a positive influence on soil resilience in terms of relative abundances of bacterial groups, soil organic carbon & available plant nutrients and hence may play a critical role in the sustainability of the intensive cereal based agri-food systems