Agricultural drought,characterized by insufficient soil moisture crucial for crop growth,poses significant chal lenges to food security and economic sustainability,particularly in water-scarce regions like Senegal.Thi...Agricultural drought,characterized by insufficient soil moisture crucial for crop growth,poses significant chal lenges to food security and economic sustainability,particularly in water-scarce regions like Senegal.This study addresses this issue by developing a comprehensive geospatial monitoring system for agricultural drought using the Regional Hydrologic Extremes Assessment System(RHEAS).This system,with a high-resolution of 0.05°,effectively simulates daily soil moisture and generates the Soil Moisture Deficit Index(SMDI)-based agricultural drought monitoring.The SMDI derived from the RHEAS has effectively captured historical droughts in Senegal over the recent 30 years period from 1993 to 2022.The SMDI,also provides a comprehensive understanding of regional variations in drought severity(S),duration(D),and frequency(F),through S-D-F analysis to identify key drought hotspots across Senegal.Findings reveal a distinct north-south gradient in drought conditions,with the northern and central Senegal experiencing more frequent and severe droughts.The study highlights that Senegal experiences frequent short-duration droughts with high severity,resulting in extensive spatial impact.Addition ally,increasing trends in drought severity and duration suggest evolving climate change effects.These findings emphasize the urgent need for sustainable interventions to mitigate drought impacts on agricultural productiv ity.Specifically,the study identifies recurrent and intense drought hotspots affecting yields of staple crops like maize and rice,as well as cash crops like peanuts.The developed high-resolution drought monitoring system for Senegal not only identifies hotspots but also enables prioritizing sustainable approaches and adaptive strategies,ultimately sustaining agricultural productivity and resilience in Senegal’s drought-prone regions.展开更多
Increased nighttime respiratory losses decrease the amount of photoassimilates available for plant growth and yield. We hypothesized that the increased respiratory carbon loss under high night temperatures(HNT) could ...Increased nighttime respiratory losses decrease the amount of photoassimilates available for plant growth and yield. We hypothesized that the increased respiratory carbon loss under high night temperatures(HNT) could be compensated for by increased photosynthesis during the day following HNT exposure. Two rice genotypes, Vandana(HNT-sensitive) and Nagina 22(HNT-tolerant), were exposed to HNT(4 ℃ above the control) from flowering to physiological maturity. They were assessed for alterations in the carbon balance of the source(flag leaf) and its subsequent impact on grain filling dynamics and the quality of spatially differentiated sinks(superior and inferior spikelets). Both genotypes exhibited significantly higher night respiration rates. However, only Nagina 22 compensated for the high respiration rates with an increased photosynthetic rate, resulting in a steady production of total dry matter under HNT. Nagina 22 also recorded a higher grain-filling rate, particularly at 5 and 10 d after flowering, with 1.5- and 4.0-fold increases in the translocation of ^(14)C sugars to the superior and inferior spikelets, respectively. The ratio of photosynthetic rate to respiratory rate on a leaf area basis was negatively correlated with spikelet sterility, resulting in a higher filled spikelet number and grain weight per plant, particularly for inferior grains in Nagina 22. Grain quality parameters such as head rice recovery, high-density grains, and gelatinization temperature were maintained in Nagina 22. An increase in the rheological properties of rice flour starch in Nagina 22 under HNT indicated the stability of starch and its ability to reorganize during the cooling process of product formation. Thus, our study showed that sink adjustments between superior and inferior spikelets favored the growth of inferior spikelets, which helped to offset the reduction in grain weight under HNT in the tolerant genotype Nagina 22.展开更多
The market demand for gluten free foods is increasing due to frequent incidences of celiac disease and increasing awareness on consumption of gluten free foods.Millets have become the major constituent of diet as they...The market demand for gluten free foods is increasing due to frequent incidences of celiac disease and increasing awareness on consumption of gluten free foods.Millets have become the major constituent of diet as they are gluten-free and also excellent sources of micro and macro nutrients such as vitamins,minerals,dietary fibers and phenolic compounds.Among various millets,the finger millet and the pearl millet are the two most important and common millet varieties grown extensively.Since,they are regarded as the staple foods of the poor and vulnerable populations,development of new products and improvements in their nutritional quality will aid in the general health of these population.Processing of millets and production of variable gluten-free ready-to-eat and nutritional supplements has increased their market value in the recent years.Furthermore,processing can also help in shelf-life extension of the millets with nutritional enrichment,expanding its markets to non-traditional millet consumers.In this context,the present review is aimed to focus on the current processing methods to develop products from the two millet varieties that are gluten free and outline their nutritional benefits.展开更多
In this study,we employ advanced data-driven techniques to investigate the complex relationships between the yields of five major crops and various geographical and spatiotemporal features in Senegal.We analyze how th...In this study,we employ advanced data-driven techniques to investigate the complex relationships between the yields of five major crops and various geographical and spatiotemporal features in Senegal.We analyze how these features influence crop yields by utilizing remotely sensed data.Our methodology incorporates clustering algorithms and correlation matrix analysis to identify significant patterns and dependencies,offering a comprehensive understanding of the factors affecting agricultural productivity in Senegal.To optimize the model's performance and identify the optimal hyperparameters,we implemented a comprehensive grid search across four distinct machine learning regressors:Random Forest,Extreme Gradient Boosting(XGBoost),Categorical Boosting(CatBoost),and Light Gradient-Boosting Machine(LightGBM).Each regressor offers unique functionalities,enhancing our exploration of potential model configurations.The top-performing models were selected based on evaluating multiple performance metrics,ensuring robust and accurate predictive capabilities.The results demonstrated that XGBoost and CatBoost perform better than the other two.We introduce synthetic crop data generated using a Variational Auto Encoder to address the challenges posed by limited agricultural datasets.By achieving high similarity scores with real-world data,our synthetic samples enhance model robustness,mitigate overfitting,and provide a viable solution for small dataset issues in agriculture.Our approach distinguishes itself by creating a flexible model applicable to various crops together.By integrating five crop datasets and generating high-quality synthetic data,we improve model performance,reduce overfitting,and enhance realism.Our findings provide crucial insights for productivity drivers in key cropping systems,enabling robust recommendations and strengthening the decision-making capabilities of policymakers and farmers in datascarce regions.展开更多
Biochar is a widely known soil amendment.Here we synthesize the available information on influence of biochar application on different soil properties and crop productivity using meta-analysis.Global data on influence...Biochar is a widely known soil amendment.Here we synthesize the available information on influence of biochar application on different soil properties and crop productivity using meta-analysis.Global data on influence of biochar applications on different soil physical,chemical,microbial properties,and crop productivity were extracted from literature and statistically analyzed.Based on selection criteria,59 studies from the literature published between 2012 and 2021 were selected for the meta-analysis.Correlations were developed between effect size of biochar application on different soil properties and crop productivity.Application of biochar increased soil pH,cation exchange capacity,and organic carbon by 46%,20%,and 27%,respectively,with greater effects in coarse and fine-textured soils.Effects on chemical properties were variable among biochar prepared from different feedstocks.Among physical properties,biochar application reduced bulk densities by 29%and increased porosity by 59%.Biochar prepared at higher pyrolytic temperatures(>500℃)improved bulk density and porosity to greater extents(31%and 66%,respectively).Biochar prepared at lower pyrolytic temperatures(<500℃)had a greater effect on microbial diversity(both bacterial and fungal),with more diverse bacterial populations in medium and coarse textured soils,while fungal diversity increased in fine textured soils.Biochar applications increased crop productivity only in fine and coarse textured soil.The effect size of biochar application on crop productivity was correlated with responses to physical properties of soils.The meta-analysis highlighted the need to conduct long-term field experiments to provide better explanations for changes in biochar properties as it undergoes aging,its longer-term effects on soil properties,and timing of re-application of different biochars.展开更多
基金supported by the NASA(Grant No.80NSSC21K0403)USAID Kansas State University subcontract KSU-A20-0163-S035 with Michigan State University.
文摘Agricultural drought,characterized by insufficient soil moisture crucial for crop growth,poses significant chal lenges to food security and economic sustainability,particularly in water-scarce regions like Senegal.This study addresses this issue by developing a comprehensive geospatial monitoring system for agricultural drought using the Regional Hydrologic Extremes Assessment System(RHEAS).This system,with a high-resolution of 0.05°,effectively simulates daily soil moisture and generates the Soil Moisture Deficit Index(SMDI)-based agricultural drought monitoring.The SMDI derived from the RHEAS has effectively captured historical droughts in Senegal over the recent 30 years period from 1993 to 2022.The SMDI,also provides a comprehensive understanding of regional variations in drought severity(S),duration(D),and frequency(F),through S-D-F analysis to identify key drought hotspots across Senegal.Findings reveal a distinct north-south gradient in drought conditions,with the northern and central Senegal experiencing more frequent and severe droughts.The study highlights that Senegal experiences frequent short-duration droughts with high severity,resulting in extensive spatial impact.Addition ally,increasing trends in drought severity and duration suggest evolving climate change effects.These findings emphasize the urgent need for sustainable interventions to mitigate drought impacts on agricultural productiv ity.Specifically,the study identifies recurrent and intense drought hotspots affecting yields of staple crops like maize and rice,as well as cash crops like peanuts.The developed high-resolution drought monitoring system for Senegal not only identifies hotspots but also enables prioritizing sustainable approaches and adaptive strategies,ultimately sustaining agricultural productivity and resilience in Senegal’s drought-prone regions.
基金the financial assistance provided by ICAR-IARI in the form of IARI Fellowship and Department of Science and Technology, Innovation in Science Pursuit for Inspired Research during the PhD programme。
文摘Increased nighttime respiratory losses decrease the amount of photoassimilates available for plant growth and yield. We hypothesized that the increased respiratory carbon loss under high night temperatures(HNT) could be compensated for by increased photosynthesis during the day following HNT exposure. Two rice genotypes, Vandana(HNT-sensitive) and Nagina 22(HNT-tolerant), were exposed to HNT(4 ℃ above the control) from flowering to physiological maturity. They were assessed for alterations in the carbon balance of the source(flag leaf) and its subsequent impact on grain filling dynamics and the quality of spatially differentiated sinks(superior and inferior spikelets). Both genotypes exhibited significantly higher night respiration rates. However, only Nagina 22 compensated for the high respiration rates with an increased photosynthetic rate, resulting in a steady production of total dry matter under HNT. Nagina 22 also recorded a higher grain-filling rate, particularly at 5 and 10 d after flowering, with 1.5- and 4.0-fold increases in the translocation of ^(14)C sugars to the superior and inferior spikelets, respectively. The ratio of photosynthetic rate to respiratory rate on a leaf area basis was negatively correlated with spikelet sterility, resulting in a higher filled spikelet number and grain weight per plant, particularly for inferior grains in Nagina 22. Grain quality parameters such as head rice recovery, high-density grains, and gelatinization temperature were maintained in Nagina 22. An increase in the rheological properties of rice flour starch in Nagina 22 under HNT indicated the stability of starch and its ability to reorganize during the cooling process of product formation. Thus, our study showed that sink adjustments between superior and inferior spikelets favored the growth of inferior spikelets, which helped to offset the reduction in grain weight under HNT in the tolerant genotype Nagina 22.
基金The manuscript is a part of the contribution no.20-351-J from the Kansas State University Agricultural Experiment Station.
文摘The market demand for gluten free foods is increasing due to frequent incidences of celiac disease and increasing awareness on consumption of gluten free foods.Millets have become the major constituent of diet as they are gluten-free and also excellent sources of micro and macro nutrients such as vitamins,minerals,dietary fibers and phenolic compounds.Among various millets,the finger millet and the pearl millet are the two most important and common millet varieties grown extensively.Since,they are regarded as the staple foods of the poor and vulnerable populations,development of new products and improvements in their nutritional quality will aid in the general health of these population.Processing of millets and production of variable gluten-free ready-to-eat and nutritional supplements has increased their market value in the recent years.Furthermore,processing can also help in shelf-life extension of the millets with nutritional enrichment,expanding its markets to non-traditional millet consumers.In this context,the present review is aimed to focus on the current processing methods to develop products from the two millet varieties that are gluten free and outline their nutritional benefits.
文摘In this study,we employ advanced data-driven techniques to investigate the complex relationships between the yields of five major crops and various geographical and spatiotemporal features in Senegal.We analyze how these features influence crop yields by utilizing remotely sensed data.Our methodology incorporates clustering algorithms and correlation matrix analysis to identify significant patterns and dependencies,offering a comprehensive understanding of the factors affecting agricultural productivity in Senegal.To optimize the model's performance and identify the optimal hyperparameters,we implemented a comprehensive grid search across four distinct machine learning regressors:Random Forest,Extreme Gradient Boosting(XGBoost),Categorical Boosting(CatBoost),and Light Gradient-Boosting Machine(LightGBM).Each regressor offers unique functionalities,enhancing our exploration of potential model configurations.The top-performing models were selected based on evaluating multiple performance metrics,ensuring robust and accurate predictive capabilities.The results demonstrated that XGBoost and CatBoost perform better than the other two.We introduce synthetic crop data generated using a Variational Auto Encoder to address the challenges posed by limited agricultural datasets.By achieving high similarity scores with real-world data,our synthetic samples enhance model robustness,mitigate overfitting,and provide a viable solution for small dataset issues in agriculture.Our approach distinguishes itself by creating a flexible model applicable to various crops together.By integrating five crop datasets and generating high-quality synthetic data,we improve model performance,reduce overfitting,and enhance realism.Our findings provide crucial insights for productivity drivers in key cropping systems,enabling robust recommendations and strengthening the decision-making capabilities of policymakers and farmers in datascarce regions.
基金a National Institute of Food and Agriculture,United States Department of Agriculture research award(Number 2019-68012-29888)the Sustainable Intensification Innovation Lab funded by the United States Agency for International Development(Grant Number AID-OAA-L-14-00006)+1 种基金funds allocated to the USDA-ARS project 3070-21610-003-00Dthe sole responsibility of authors and do not reflect the views of funding agencies and representing organizations.Contribution No.21-310-J from the Kansas Agricultural Experiment Station.The US Department of Agriculture(USDA)is an equal opportunity employer and provider of services in all its programs and activities.
文摘Biochar is a widely known soil amendment.Here we synthesize the available information on influence of biochar application on different soil properties and crop productivity using meta-analysis.Global data on influence of biochar applications on different soil physical,chemical,microbial properties,and crop productivity were extracted from literature and statistically analyzed.Based on selection criteria,59 studies from the literature published between 2012 and 2021 were selected for the meta-analysis.Correlations were developed between effect size of biochar application on different soil properties and crop productivity.Application of biochar increased soil pH,cation exchange capacity,and organic carbon by 46%,20%,and 27%,respectively,with greater effects in coarse and fine-textured soils.Effects on chemical properties were variable among biochar prepared from different feedstocks.Among physical properties,biochar application reduced bulk densities by 29%and increased porosity by 59%.Biochar prepared at higher pyrolytic temperatures(>500℃)improved bulk density and porosity to greater extents(31%and 66%,respectively).Biochar prepared at lower pyrolytic temperatures(<500℃)had a greater effect on microbial diversity(both bacterial and fungal),with more diverse bacterial populations in medium and coarse textured soils,while fungal diversity increased in fine textured soils.Biochar applications increased crop productivity only in fine and coarse textured soil.The effect size of biochar application on crop productivity was correlated with responses to physical properties of soils.The meta-analysis highlighted the need to conduct long-term field experiments to provide better explanations for changes in biochar properties as it undergoes aging,its longer-term effects on soil properties,and timing of re-application of different biochars.
