Integrated nutrient management for improving crop yields, soil properties, and reducing greenhouse gas emissions

Recently, most agrarian countries have witnessed either declining or stagnant crop yields. Inadequate soil organic matter (SOM) due to the poor physical, chemical, and biological properties of the soil leads to an overall decline in the productivity of farmlands. Therefore, the adoption of integrated nutrient management (INM) practices is vital to revive sustainable soil health without compromising yield potential. Integrated nutrient management is a modified nutrient management technique with multifarious benefits, wherein a combination of all possible sources of plant nutrients is used in a crop nutrition package. Several studies conducted in various parts of the world have demonstrated the benefits of INM in terms of steep gain in soil health and crop yields and at the same time, reducing greenhouse gas emissions and other related problems. The INM practice in the cropped fields showed a 1,355% reduction in methane over conventional nutrient management. The increase in crop yields due to the adoption of INM over conventional nutrient management was as high as 1.3% to 66.5% across the major cropping systems. Owing to the integration of organic manure and residue retention in INM, there is a possibility of significant improvement in soil aggregates and microbiota. Furthermore, most studies conducted to determine the impact of INM on soil health indicated a significant increase in overall soil health, with lower bulk density, higher porosity, and water-holding capacity. Overall, practicing INM would enhance soil health and crop productivity, in addition to decreasing environmental pollution, greenhouse gas emissions, and production costs

COVID-19, deforestation, and green economy

Corona has severely impacted many sectors in the past 2. 5 years, and forests are one of the major hits among all sectors affected by the pandemic. This study presents the consolidated data on deforestation patterns across the globe during COVID and also analyzes in depth the region-specific contributing factors. Exacerbated deforestation during COVID alarms biodiversity conservation concerns and pushes back the long-term efforts to combat pollution and climate change mitigation. Deforestation also increases the risk of the emergence of new zoonotic diseases in future, as deforestation and COVID are intricately related to each other. Therefore, there is a need to check deforestation and inculcation of conservation measures in building back better policies adopted post-COVID. This review is novel in specifically providing insight into the implications of COVID-19 on forests in tropical as well as temperate global regions, causal factors, green policies given by different nations, and recommendations that will help in designing nature-based recovery strategies for combating deforestation and augmenting afforestation, thus providing better livelihood, biodiversity conservation, climate change mitigation, and better environmental quality

A Life Cycle Assessment of Rice–Rice and Rice–Cowpea Cropping Systems in the West Coast of India

Crop diversification is essential in lowland rice cropping systems to achieve sustainability, improve soil health, and as a climate-resilient practice to reduce greenhouse gas (GHG) emissions. A life cycle assessment (LCA) was conducted for the farms in the west-coast region of India to assess the environmental impact of the rice–rice and rice–cowpea cropping systems. The life cycle impact assessment (LCIA) was evaluated in a “cradle-to-gate” perspective. A higher energy consumption was found in the rice–rice system (32,673 vs. 18,197 MJ/ha), while the net energy output was higher in the rice–cowpea system (211,071 vs. 157,409 MJ/ha). Energy consumption was 44% lower in the rice–cowpea system, which was coupled with a higher energy efficiency (11.6 vs. 4.8), attributed to the lower energy consumption and the higher energy output. Further, the results indicated an energy saving potentialin the rice–cowpea system due to the higher use of renewable resources such as farmyard manure. Field emissions, fertilizer production, and fuel consumption were the major contributors to the greenhouse gas (GHG) emissions in both cropping systems. The total GHG emissions were 81% higher in the rice–rice system (13,894 ± 1329 kg CO2 eq./ha) than in the rice–cowpea system (7679 ± 719 kg CO2 eq./ha). The higher GHG emissions in the rice–rice system were largely due to the higher use of fertilizers, diesel fuel, and machinery. Hence, diversifying the winter rice with a cowpea crop and its large-scale adoption on the west coast of India would provide multiple benefits in decreasing the environmental impact and improving the energy efficiency to achieve sustainability and climate resilience in rice-based cropping systems

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Not AvailableRice is generally grown under completely flooded condition and providing food for more than half of the world’s population. Any changes in weather parameters might affect the rice productivity thereby impacting the food security of burgeoning population. So, the crop yield forecasting based on weather parameters will help farmers, policy makers and administrators to manage adversities. The present investigation examines the application of stepwise multiple linear regression (SMLR), artificial neural network (ANN) solely and in combination with principal components analysis (PCA) and penalised regression models (e.g. least absolute shrinkage and selection operator (LASSO) or elastic net (ENET)) for rice yield prediction using long-term weather data. The R2 and root mean square error (RMSE) of the models varied between 0.22–0.98 and 24.02–607.29 kg ha−1, respectively during calibration. During validation with independent dataset, the RMSE and normalised root mean square error (nRMSE) ranged between 21.35–981.89 kg ha−1 and 0.98–36.7%, respectively. For evaluation of multiple models for multiple locations statistically, overall average ranks on the basis of R2 and RMSE of calibration; RMSE and nRMSE of validation were calculated and non-parametric Friedman test was applied to check the significant difference among the models. The ranking of the models revealed that LASSO (2.63) was the best performing model followed by ENET (3.07) while PCA-ANN (4.19) was the worst model which was found significant at p < 0.001. The reason behind good performance of LASSO and ENET is that these models prevent overfitting and reduce model complexity by penalising the magnitude of coefficients. Then, pairwise multiple comparison test was performed which indicated LASSO as the best model which was found similar to SMLR and ENET. So, for prediction of rice yield, these models can very well be utilised for west coast of India.Not Availabl

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Not AvailableRice is generally grown under completely flooded condition and providing food for more than half of the world’s population. Any changes in weather parameters might affect the rice productivity thereby impacting the food security of burgeoning population. So, the crop yield forecasting based on weather parameters will help farmers, policy makers and administrators to manage adversities. The present investigation examines the application of stepwise multiple linear regression (SMLR), artificial neural network (ANN) solely and in combination with principal components analysis (PCA) and penalised regression models (e.g. least absolute shrinkage and selection operator (LASSO) or elastic net (ENET)) for rice yield prediction using long-term weather data. The R2 and root mean square error (RMSE) of the models varied between 0.22–0.98 and 24.02–607.29 kg ha−1, respectively during calibration. During validation with independent dataset, the RMSE and normalised root mean square error (nRMSE) ranged between 21.35–981.89 kg ha−1 and 0.98–36.7%, respectively. For evaluation of multiple models for multiple locations statistically, overall average ranks on the basis of R2 and RMSE of calibration; RMSE and nRMSE of validation were calculated and non-parametric Friedman test was applied to check the significant difference among the models. The ranking of the models revealed that LASSO (2.63) was the best performing model followed by ENET (3.07) while PCA-ANN (4.19) was the worst model which was found significant at p < 0.001. The reason behind good performance of LASSO and ENET is that these models prevent overfitting and reduce model complexity by penalising the magnitude of coefficients. Then, pairwise multiple comparison test was performed which indicated LASSO as the best model which was found similar to SMLR and ENET. So, for prediction of rice yield, these models can very well be utilised for west coast of India.Not Availabl

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Not AvailableRice is generally grown under completely flooded condition and providing food for more than half of the world’s population. Any changes in weather parameters might affect the rice productivity thereby impacting the food security of burgeoning population. So, the crop yield forecasting based on weather parameters will help farmers, policy makers and administrators to manage adversities. The present investigation examines the application of stepwise multiple linear regression (SMLR), artificial neural network (ANN) solely and in combination with principal components analysis (PCA) and penalised regression models (e.g. least absolute shrinkage and selection operator (LASSO) or elastic net (ENET)) for rice yield prediction using long-term weather data. The R2 and root mean square error (RMSE) of the models varied between 0.22–0.98 and 24.02–607.29 kg ha−1, respectively during calibration. During validation with independent dataset, the RMSE and normalised root mean square error (nRMSE) ranged between 21.35–981.89 kg ha−1 and 0.98–36.7%, respectively. For evaluation of multiple models for multiple locations statistically, overall average ranks on the basis of R2 and RMSE of calibration; RMSE and nRMSE of validation were calculated and non-parametric Friedman test was applied to check the significant difference among the models. The ranking of the models revealed that LASSO (2.63) was the best performing model followed by ENET (3.07) while PCA-ANN (4.19) was the worst model which was found significant at p < 0.001. The reason behind good performance of LASSO and ENET is that these models prevent overfitting and reduce model complexity by penalising the magnitude of coefficients. Then, pairwise multiple comparison test was performed which indicated LASSO as the best model which was found similar to SMLR and ENET. So, for prediction of rice yield, these models can very well be utilised for west coast of India.Not Availabl

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Not AvailableEnergy flow and environmental impact are the key factors toward sustainable development of agricultural production. The objective of the current study is to optimize the energy consumption and assess the environmental impacts of arecanut production. The data were collected from 70 arecanut producers from the Goa state of India. The environmental impacts were investigated with a cradle to gate perspective; using, raw materials extraction, manufacture, use, and supply of inputs to the farm. The functional units considered were one tonne of arecanut production (mass-based) and 1-ha area of arecanut (land-based). The data envelopment analysis indicated the average technical efficiency of arecanut farms as 0.89, implying the potential of saving the resources to the tune of 11% with a mean economic saving of 413 $ ha 1 year 1 without reducing the arecanut yield. Human labor, irrigation, manures, and chemical fertilizers were the major energy consumers in the system. The life cycle assessment indicated on-farm emissions as the hotspot for the respiratory inorganics, terrestrial acid/nutria, and aquatic acidification impact categories. Arecanut production had the highest negative impact on human health followed by ecosystem quality. The global warming potential of arecanut production works out to be 959.87 and 2399.25 kg CO2 eq. per tonne and per hectare basis, respectively. In conclusion, efficient use of inputs in synchrony with crop requirement, advance irrigation methods, and efficient machinery usage may be adopted to curtail the environmental impact of arecanut production in the region.ICAR-Indian Institute of Farming System Research, ModipuramICAR-Central Coastal Agricultural Research Institute, Go

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Not AvailableCoconut is a major plantation crop of coastal India. Accurate prediction of its yield is helpful for the farmers, industries and policymakers. Weather has profound impact on coconut fruit setting, and therefore, it greatly affects the yield. Annual coconut yieldandmonthlyweatherdatafor2000–2015werecompiledforfourteendistrictsofthewestcoastofIndia.Weatherindiceswere generatedusingmonthlycumulativevalueforrainfallandmonthlyaveragevalueforotherparameterslikemaximumandminimum temperature, relative humidity, wind speed and solar radiation. Different linear models like stepwise multiple linear regression (SMLR), principal component analysis together with SMLR (PCA-SMLR), least absolute shrinkage and selection operator (LASSO) and elastic net (ELNET) with nonlinear models namely artificial neural network (ANN) and PCA-ANN were employed to model the coconut yield using the monthly weather indices as inputs. The model’s performance was evaluated using R2, root mean square error (RMSE) and absolute percentage error (APE). The R2 and RMSE of the models ranged between 0.45–0.99 and 18–3624 nuts ha−1 respectively during calibration while during validation the APE varied between 0.12 and 58.21. The overall average ranking of the models based the se performance statistics were in the order of ELNET >LASSO >ANN >SMLR> PCASMLR > PCA-ANN. Results indicated that the ELNET model could be used for prediction of coconut yield for the regionNot Availabl

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Not AvailableCoconut is a major plantation crop of coastal India. Accurate prediction of its yield is helpful for the farmers, industries and policymakers. Weather has profound impact on coconut fruit setting, and therefore, it greatly affects the yield. Annual coconut yield and monthly weather data for 2000–2015 were compiled for fourteen districts of the west coast of India. Weather indices were generated using monthly cumulative value for rainfall and monthly average value for other parameters like maximum and minimum temperature, relative humidity, wind speed and solar radiation. Different linear models like stepwise multiple linear regression (SMLR), principal component analysis together with SMLR (PCA-SMLR), least absolute shrinkage and selection operator (LASSO) and elastic net (ELNET) with nonlinear models namely artificial neural network (ANN) and PCA-ANN were employed to model the coconut yield using the monthly weather indices as inputs. The model’s performance was evaluated using R2, root mean square error (RMSE) and absolute percentage error (APE). The R2 and RMSE of the models ranged between 0.45–0.99 and 18–3624 nuts ha−1 respectively during calibration while during validation the APE varied between 0.12 and 58.21. The overall average ranking of the models based these performance statistics were in the order of ELNET > LASSO > ANN > SMLR > PCA-SMLR > PCA-ANN. Results indicated that the ELNET model could be used for prediction of coconut yield for the region.ICAR-Central Coastal Agricultural Research Institut