Biotechnological frontiers in harnessing allelopathy for sustainable crop production.

Allelopathy, the phenomenon in which plants release biochemical compounds that influence the growth and development of neighbouring plants, presents promising opportunities for revolutionizing agriculture towards sustainability. This abstract explores the role of biotechnological advancements in unlocking the potential of allelopathy for sustainable crop production and its applications in agriculture, ecology, and natural resource management. By combining molecular, genetic, biochemical, and bioinformatic tools, researchers can unravel the complexities of allelopathic interactions and their potential for sustainable crop production and environmental stewardship. The development of novel management methods for weed control is getting a lot of attention with the introduction of new genetic technologies such as Gene drive, Transgene technologies, Gene silencing, Marker-assisted selection (MAS), and Clustered regularly interspaced short palindromic repeats (CRISPR-Cas9). By strengthening competitive characteristics these tools hold great promise for boosting crops' ability to compete with weeds. Considering recent literature, this review highlights the genetic, transcriptomics, and metabolomics approaches to allelopathy. Employing allelopathic properties in agriculture offer sustainable benefits like natural weed management, pest management, and reduced chemical pollution, but challenges include environmental factors, toxicity, regulatory hurdles, and limited resources. Effective integration requires continued research, regulatory support, and farmer education​. Also, we aimed to identify the biotechnological domains requiring more investigation and to provide the basis for future advances through this assessment.

Identification and expression analysis of Phosphate Transporter 1 (PHT1) genes in the highly phosphorus-use-efficient Hakea prostrata (Proteaceae).

Heavy and costly use of phosphorus (P) fertiliser is often needed to achieve high crop yields, but only a small amount of applied P fertiliser is available to most crop plants. Hakea prostrata (Proteaceae) is endemic to the P-impoverished landscape of southwest Australia and has several P-saving traits. We identified 16 members of the Phosphate Transporter 1 (PHT1) gene family (HpPHT1;1-HpPHT1;12d) in a long-read genome assembly of H. prostrata. Based on phylogenetics, sequence structure and expression patterns, we classified HpPHT1;1 as potentially involved in Pi uptake from soil and HpPHT1;8 and HpPHT1;9 as potentially involved in Pi uptake and root-to-shoot translocation. Three genes, HpPHT1;4, HpPHT1;6 and HpPHT1;8, lacked regulatory PHR1-binding sites (P1BS) in the promoter regions. Available expression data for HpPHT1;6 and HpPHT1;8 indicated they are not responsive to changes in P supply, potentially contributing to the high P sensitivity of H. prostrata. We also discovered a Proteaceae-specific clade of closely-spaced PHT1 genes that lacked conserved genetic architecture among genera, indicating an evolutionary hot spot within the genome. Overall, the genome assembly of H. prostrata provides a much-needed foundation for understanding the genetic mechanisms of novel adaptations to low P soils in southwest Australian plants.

Forecast of agri-residues generation from rice, wheat and oilseed crops in India using machine learning techniques: Exploring strategies for sustainable smart management.

Management of agri-residues generated in large quantities necessitates for its accurate estimation. Data analysis using machine learning methods can predict the agri-residues generation. The objective of the study was to forecast agri-residues generation from rice, wheat, and oilseed crops in India using ML methods and their sustainable uses. Prediction of agri-residues was done first by forecasting the crop production via the application of ML techniques for the period 2022 to 2030, and then the amount of crop residues generation calculated by multiplying the crop productions with the residues-to-product-ratio (RPR) values of the respective crops. RPR was estimated by using the gravimetric ratio of the residue to the actual crop production. The crop-specific RPR values were taken from various earlier studies in Indian context. The RPR values of 1.73 for the rice, 1.65 for wheat, and 2.6 for the oilseed crop were used as a conversion factor for residues calculation. Machine learning models linear regression, sequential minimal optimization regression (SMOreg), M5 Rule, and Gaussian process were used in the study. SMOreg performed better in models tested by coefficient of determination, root mean square error, and mean absolute error. The models predicted the generation of residues in 2030 as rice straw and husk 195.76 Mt to 277.68 Mt, wheat straw 188.62 Mt to 266.95 Mt, and oilseed stalk and oil cakes 55.61 Mt to 96.30 Mt in India. An overview of the management of agri-residues discussed. Estimation of agri-residues can provide an opportunity to utilize them with the best possible ways, lessen pollution and promote a zero-waste strategy.

How the Global Burden of Animal Diseases links to the Global Burden of Crop Loss: a food systems perspective.

Food systems comprise interconnected webs of processes that together transform inputs (land, labour, water, nutrients and genetics, to mention just a few) into outputs such as nutrition and revenue for human societies. Perfect systems do not exist; rather, global food systems operate in the presence of hazards, biotic and abiotic alike, and under the constraint of limited resources to mitigate these hazards. There are, therefore, inefficiencies in these systems, which lead to losses in terms of monetary, nutritional, health and environmental values and create additional negative externalities in the health, social and environmental spaces. Health hazards in the food system do not respect arbitrary distinctions between the crop and livestock sectors, which are highly interconnected. These linkages exist where one sector provides inputs to another or through substitution effects where supply in one sector influences demand in another. The One Health approach advocates investigating the intersectoral hazards in a highly interdisciplinary manner. This article provides a conceptual framework for integrating the methodologies developed by the Global Burden of Crop Loss and Global Burden of Animal Diseases initiatives to generate burden estimates for hazards in food systems that better account for interconnectivity and foster an improved understanding of food systems that is aligned with the interdisciplinary nature of the One Health approach. A case study related to maize and poultry sector linkages in the wider context of public and environmental health is presented.

Les systèmes alimentaires sont des réseaux de processus interconnectés qui concourent à transformer des intrants (terre, main-d'oeuvre, eau, nutriments et génétique, pour n'en mentionner que quelques-uns) en extrants tels que des aliments et des revenus pour les sociétés humaines. Il n'existe pas de système parfait ; les systèmes alimentaires mondiaux sont exposés en permanence à des dangers de nature tant biotique qu'abiotique et contraints par les ressources limitées consacrées à l'atténuation de ces dangers. Les problèmes d'efficacité sont donc inéluctables ; ils entraînent des pertes de valeur tant monétaire que nutritionnelle, sanitaire et environnementale, et génèrent de nouvelles externalités négatives dans le domaine de la santé ainsi que dans l'espace social et dans l'environnement. Les dangers sanitaires présents dans le système alimentaire ignorent les distinctions arbitraires entre les secteurs agricole et d'élevage, lesquels sont fortement interconnectés. Ces liens se manifestent lorsqu'un secteur fournit des intrants à l'autre et, par l'effet de substitutions, lorsque l'offre dans un secteur influence la demande dans l'autre. L'approche " Une seule santé " préconise d'adopter une méthode fondée sur l'interdisciplinarité pour enquêter sur les dangers intersectoriels. Les auteurs décrivent le cadre conceptuel de l'intégration des méthodes des initiatives " Fardeau mondial des pertes agricoles " et " Impact mondial des maladies animales " dans le but de produire des estimations de la charge induite par les dangers des systèmes alimentaires qui prennent davantage en compte leur inter-connectivité et donnent lieu à une meilleure compréhension des systèmes alimentaires, en cohérence avec le caractère interdisciplinaire de l'approche " Une seule santé ". Est également présentée une étude de cas portant sur les liens entre la culture du maïs et l'élevage de volailles dans le contexte plus large de la santé publique et environnementale.

Los sistemas alimentarios comprenden redes interconectadas de procesos que, conjuntamente, transforman insumos (tierra, mano de obra, agua, nutrientes y genética, por mencionar solo algunos) en productos como alimentos e ingresos para las sociedades humanas. No existen sistemas perfectos; más bien, los sistemas alimentarios mundiales funcionan en un entorno de peligros, tanto bióticos como abióticos, y con las restricciones impuestas por los limitados recursos disponibles para mitigarlos. En estos sistemas se observan, por tanto, ineficiencias, que provocan pérdidas en términos monetarios, nutricionales, sanitarios y ambientales y que crean externalidades negativas adicionales en los ámbitos sanitario, social y ambiental. Los peligros para la salud en los sistemas alimentarios no atienden a distinciones arbitrarias entre los sectores agrícola y ganadero, que están muy interconectados. Estos vínculos surgen cuando un sector proporciona insumos a otro o a través de efectos de sustitución en los que la oferta de un sector influye en la demanda de otro. El enfoque de "Una sola salud" aboga por investigar los peligros intersectoriales de manera eminentemente interdisciplinaria. En este artículo se ofrece un marco teórico para la integración de las metodologías desarrolladas por las iniciativas dedicadas al impacto global de las pérdidas de cosechas y al impacto global de las enfermedades animales a fin de obtener estimaciones de los peligros en los sistemas alimentarios que tengan más en cuenta la interconexión y fomenten una mejor comprensión de los sistemas alimentarios acorde con el carácter interdisciplinario del enfoque de "Una sola salud". En este sentido, se presenta un estudio de caso relacionado con los vínculos entre los sectores del maíz y las aves de corral en el contexto más amplio de la salud pública y ambiental.

Response of the TEROS 12 Soil Moisture Sensor under Different Soils and Variable Electrical Conductivity.

In this work, the performance of the TEROS 12 electromagnetic sensor, which measures volumetric soil water content (θ), bulk soil electrical conductivity (σb), and temperature, is examined for a number of different soils, different θ and different levels of the electrical conductivity of the soil solution (ECW) under laboratory conditions. For the above reason, a prototype device was developed including a low-cost microcontroller and suitable adaptation circuits for the aforementioned sensor. Six characteristic porous media were examined in a θ range from air drying to saturation, while four different solutions of increasing Electrical Conductivity (ECw) from 0.28 dS/m to approximately 10 dS/m were used in four of these porous media. It was found that TEROS 12 apparent dielectric permittivity (εa) readings were lower than that of Topp's permittivity-water content relationship, especially at higher soil water content values in the coarse porous bodies. The differences are observed in sand (S), sandy loam (SL) and loam (L), at this order. The results suggested that the relationship between experimentally measured soil water content (θm) and εa0.5 was strongly linear (0.869 < R2 < 0.989), but the linearity of the relation θm-εa0.5 decreases with the increase in bulk EC (σb) of the soil. The most accurate results were provided by the multipoint calibration method (CAL), as evaluated with the root mean square error (RMSE). Also, it was found that εa degrades substantially at values of σb less than 2.5 dS/m while εa returns to near 80 at higher values. Regarding the relation εa-σb, it seems that it is strongly linear and that its slope depends on the pore water electrical conductivity (σp) and the soil type.

Systematic Investigation of Aluminum Stress-Related Genes and Their Critical Roles in Plants.

Aluminum (Al) stress is a dominant obstacle for plant growth in acidic soil, which accounts for approximately 40-50% of the world's potential arable land. The identification and characterization of Al stress response (Al-SR) genes in Arabidopsis, rice, and other plants have deepened our understanding of Al's molecular mechanisms. However, as a crop sensitive to acidic soil, only eight Al-SR genes have been identified and functionally characterized in maize. In this review, we summarize the Al-SR genes in plants, including their classifications, subcellular localizations, expression organs, functions, and primarily molecular regulatory networks. Moreover, we predict 166 putative Al-SR genes in maize based on orthologue analyses, facilitating a comprehensive understanding of the impact of Al stress on maize growth and development. Finally, we highlight the potential applications of alleviating Al toxicity in crop production. This review deepens our understanding of the Al response in plants and provides a blueprint for alleviating Al toxicity in crop production.

Vermitechnology transforms hazardous red mud into benign organic input for agriculture: Insights on earthworm-microbe interaction, metal removal, and soil-crop improvement.

Bioremediation of hazardous bauxite residues, red mud (RM), through vermicomposting has yet to be attempted. Therefore, the valorization potential of Eisenia fetida in various RM and cow dung (CD) mixtures was compared to aerobic composting. Earthworm fecundity and biomass growth were hindered in RM + CD (1:1) feedstock but enhanced in RM + CD (1:3). The pH of highly alkaline RM-feedstocks sharply reduced (>17%) due to vermicomposting. N, P, and K availability increased dramatically with Ca and Na reduction under vermicomposting. Additionally, ∼40-60% bioavailable metal fractions were transformed to obstinate (organic matter and residual bound) forms upon vermicomposting. Consequently, the total metal concentrations were significantly reduced with considerably high earthworm bioaccumulation. Microbial growth and enzyme activity were more significant under vermicomposting than composting. Correlation statistics revealed that microbial augmentation significantly facilitated a metal reduction in RM-vermibeds. Eventually, RM-vermicompost stimulated sesame growth and improved soil health with the least heavy metal contamination to soil and crop.

Sugarcane bagasse biochar boosts maize growth and yield in salt-affected soil by improving soil enzymatic activities.

Salinization is a leading threat to soil degradation and sustainable crop production. The application of organic amendments could improve crop growth in saline soil. Thus, we assessed the impact of sugarcane bagasse (SB) and its biochar (SBB) on soil enzymatic activity and growth response of maize crop at three various percentages (0.5%, 1%, and 2% of soil) under three salinity levels (1.66, 4, and 8 dS m-1). Each treatment was replicated three times in a completely randomized block design with factorial settings. The results showed that SB and SBB can restore the impact of salinization, but the SBB at the 2% addition rate revealed promising results compared to SB. The 2% SBB significantly enhanced shoot length (23.4%, 26.1%, and 41.8%), root length (16.8%, 20.8%, and 39.0%), grain yield (17.6%, 25.1%, and 392.2%), relative water contents (11.2%, 13.1%, and 19.2%), protein (17.2%, 19.6%, and 34.9%), and carotenoid (16.3, 30.3, and 49.9%) under different salinity levels (1.66, 4, and 8 dS m-1, respectively). The 2% SBB substantially drop the Na+ in maize root (28.3%, 29.9%, and 22.4%) and shoot (36.1%, 37.2%, and 38.5%) at 1.66, 4, and 8 dS m-1. Moreover, 2% SBB is the best treatment to boost the urease by 110.1%, 71.7%, and 91.2%, alkaline phosphatase by 28.8%, 38.8%, and 57.6%, and acid phosphatase by 48.4%, 80.1%, and 68.2% than control treatment under 1.66, 4 and 8 dS m-1, respectively. Pearson analysis showed that all the growth and yield parameters were positively associated with the soil enzymatic activities and negatively correlated with electrolyte leakage and sodium. The structural equational model (SEM) showed that the different application percentage of amendments significantly influences the growth and physiological parameters at all salinity levels. SEM explained the 81%, 92%, and 95% changes in maize yield under 1.66, 4, and 8 dS m-1, respectively. So, it is concluded that the 2% SBB could be an efficient approach to enhance the maize yield by ameliorating the noxious effect of degraded saline soil.

Pathways to adoption and mitigation: A dynamic perspective on good agricultural practices in Rural Malawi.

Many researchers have noted the limited adoption of farming management practices that should increase the resilience of smallholder farmers to weather shocks and mitigate their impact on the changing climate in sub-Saharan Africa. In this paper, we evaluate the dynamics of adopting "good agricultural practices" in Malawi, using data from a three-wave panel collected as part of an impact assessment of the Sustainable Agricultural Production Programme, funded by the International Fund for Agricultural Development. In addition to project impacts, we also evaluate additional mechanisms though which farmers may learn about the costs and benefits of different practices. We also evaluate the extent to which climatic conditions - such as being located in drought-prone or heavy rainfall areas - drive adoption decisions. Given the three waves of data, we first look at the range of adoption pathways observed, through the use of an adoption pathway trees. We identify six pathways, noting that adoption is not continuous for a large percentage of households. We then run a multinomial logit to assess the factors that increase the likelihood of falling into different adoption categories vis-a-vis remaining a never adopter. Results suggest that learning through information dissemination, such as through the SAPP project, and wider learning opportunities significantly increased the likelihood of pursuing different adoption pathways, while climatic conditions and learning through observing have limited impacts. On the other hand, for land-intensive management practices, being located in drought-prone areas or being located in areas prone to heavy rainfall increased the likelihood of pursuing different adoption pathways, as did greater ability to learn by observing. Learning by information sharing had limited impacts for land-intensive adoption pathway decisions. Overall, results suggest that information dissemination is important, though the mechanism differs by type of practice promoted. Flexibility in adoption status is an attribute of this system and there is a need to identify and promote practices that are both flexible and increase resilience to climate change.

Estimation of wheat crop production using multispectral information fusion.

BACKGROUND: The present work estimates the area and corresponding wheat crop production in the study area, which comprises the Etah region of Uttar Pradesh, India. For this purpose, multispectral images of multiple sensors, namely Sentinel-2, Landsat-8 and Landsat-9 during the preharvest period, i.e. March for the years 2021 and 2022, were used. A multispectral information fusion approach was proposed, involving image classification as well as vegetation index-based information extraction. For imposing information fusion, appropriate image bands were identified with the help of separability analysis followed by land cover classification for wheat crop class extraction. Support vector machine (SVM), artificial neural network (ANN) and maximum likelihood (ML) were used for classification, whereas normalized difference vegetation index (NDVI) and fractional vegetation cover (FVC) were used for index-based crop area extraction. RESULTS: A maximum accuracy of 98.34% was achieved for Sentinel-2 data using ANN, whereas a minimum accuracy of 80.21% was achieved for Landsat-9 using the ML classifier. The estimated area for Sentinel-2 data for the year 2021 was 260 540 ha using ANN and 203 240 ha using ML, which is close to the reference data, i.e. 238 600 ha. SVM also showed good performance and calculated least error in estimated crop area for the year 2022 on Sentinel-2 data. It calculated 8 408 490 tons of wheat for the same year. CONCLUSION: The proposed method utilizes a single image per year for extraction of information supported by the ground truth data, which makes it a novel approach to information extraction for crop production monitoring. © 2023 Society of Chemical Industry.

Assessing the impacts of crop production on climate change: An in-depth analysis of long-term determinants and policy implications.

This research investigates the long-term determinants of carbon emissions in three diverse regions-Europe and Central Asia (ECA), Sub-Saharan Africa (SSA), and the Middle East and North Africa (MENA)-spanning 1990 to 2020. Utilizing advanced econometric models and analyses, including the Regularized Common Correlated Effects Estimator (rCCE), Common Correlated Effects Estimator (CCE), and Mean-Group (MG) approach, the study explores the intricate relationships between carbon emissions, crop production, emissions per agricultural production, energy consumption, renewable energy consumption, per capita GDP, and population. Region-specific nuances are uncovered, highlighting the varying dynamics: ECA exhibits intricate and non-significant relationships, SSA showcases significant effects of population dynamics and green technology adoption, and the MENA region reveals a nuanced interplay between emissions per agricultural production.The findings underscore the universal efficacy of green technology adoption for mitigation. Strategies for mitigating carbon emissions in the agricultural sector require diversified energy transition approaches, emphasizing efficiency enhancements, green technology adoption, and tailored population management strategies based on regional intricacies. Counterfactual simulations indicate the potential efficacy of strategic measures targeting crop production to reduce carbon emissions, while acknowledging the nuanced relationship between economic growth and emissions. Policymakers are urged to recognize the persistence in emission patterns, emphasizing the importance of targeted interventions to transition towards more sustainable trajectories. Overall, the research provides essential insights for crafting effective policies at both regional and global scales to address the complexities of climate change mitigation in the agricultural sector.

Machine learning-based potential loss assessment of maize and rice production due to flash flood in Himachal Pradesh, India.

Flash floods in mountainous regions like the Himalayas are considered to be common natural calamities. Their consequences often are more dangerous than any flood event in the plains. These hazards not only put human lives at threat but also cause economic deflation due to the loss of lands, properties, and agricultural production. Hence, assessing the impact of such hazards in the existing agricultural system is of utmost importance to understand the probable crop loss. In this paper, we studied the efficiency of the remotely sensed microwave data to map the croplands affected by the flash flood that occurred in July 2023 in Himachal Pradesh, a mountainous state in the Indian Himalayan Region. The Una, Hamirpur, Kangra, and Sirmaur districts were identified as the most affected areas, with about 9%, 6%, 5.74%, and 3.61% of the respective districts' total geographical area under flood. Further, four machine learning algorithms (random forest, support vector regressor, k-nearest neighbor, and extreme gradient boosting) were evaluated to forecast maize and rice crop production and potential loss during the Kharif season in 2023. A regression algorithm with ten predictor variables consisting of the cropland area, two vegetation indices, and seven climatic parameters was applied to forecast the maize and rice production in the state. Amongst the four algorithms, random forest showed outstanding performance compared to others. The random forest regressor estimated the production of maize and rice with R2 more than 0.8 in most districts. The mean absolute error and the root mean squared error obtained from the random forest regressor were also minimal compared to the others. The maximum production loss of maize is estimated for Solan (54.13%), followed by Una (11.06%), and of rice in Kangra (19.1%), Una (18.8%) and Kinnaur (18.5%) districts. This indicated the utility of the proposed approach for a quick in-season forecast on crop production loss due to climatic hazards.

Advancing irrigation management: integrating technology and sustainability to address global food security.

Irrigation management is essential for addressing global food security challenges under changing climate. This review discusses the integration of advanced irrigation technologies and their roles in enhancing water use efficiency and managing energy demands within agricultural systems. High-efficiency irrigation systems, such as drip and sprinkler systems, have significant potential to reduce water use and increase crop yields. However, their adoption varies worldwide, and the efficiency of existing irrigation practices often remains inadequate, resulting in substantial water losses due to outdated management practices. Emerging technologies and innovative irrigation strategies, including precision agriculture and advanced crop models, provide promising pathways for improving irrigation efficiency. Nonetheless, the widespread integration of these technologies is hindered by high costs, the need for technical expertise, and challenges in adapting existing agricultural systems to new methodologies. Irrigation systems can have substantial energy requirements, particularly those dependent on groundwater. The exploration of the water-environment-energy-food (WEEF) nexus illustrates the importance of a balanced approach to resource management, which is crucial for achieving sustainable agricultural outcomes. Future research should include lowering barriers to technology adoption, enhancing data utilization for precision irrigation, promoting integrated management strategies within the WEEF framework, and strengthening policy support for sustainable practices. This review proposes a multidisciplinary approach to irrigation management that includes technological innovation, strategic policy development, and global cooperation to secure sustainable agricultural practices and ensure global food supply resilience in the face of climate change.

A cross-scale analysis to understand and quantify the effects of photosynthetic enhancement on crop growth and yield across environments.

Photosynthetic manipulation provides new opportunities for enhancing crop yield. However, understanding and quantifying the importance of individual and multiple manipulations on the seasonal biomass growth and yield performance of target crops across variable production environments is limited. Using a state-of-the-art cross-scale model in the APSIM platform we predicted the impact of altering photosynthesis on the enzyme-limited (Ac ) and electron transport-limited (Aj ) rates, seasonal dynamics in canopy photosynthesis, biomass growth, and yield formation via large multiyear-by-location crop growth simulations. A broad list of promising strategies to improve photosynthesis for C3 wheat and C4 sorghum were simulated. In the top decile of seasonal outcomes, yield gains were predicted to be modest, ranging between 0% and 8%, depending on the manipulation and crop type. We report how photosynthetic enhancement can affect the timing and severity of water and nitrogen stress on the growing crop, resulting in nonintuitive seasonal crop dynamics and yield outcomes. We predicted that strategies enhancing Ac alone generate more consistent but smaller yield gains across all water and nitrogen environments, Aj enhancement alone generates larger gains but is undesirable in more marginal environments. Large increases in both Ac and Aj generate the highest gains across all environments. Yield outcomes of the tested manipulation strategies were predicted and compared for realistic Australian wheat and sorghum production. This study uniquely unpacks complex cross-scale interactions between photosynthesis and seasonal crop dynamics and improves understanding and quantification of the potential impact of photosynthesis traits (or lack of it) for crop improvement research.

Above- and belowground linkages during extreme moisture excess: leveraging knowledge from natural ecosystems to better understand implications for row-crop agroecosystems.

Above- and belowground linkages are responsible for some of the most important ecosystem processes in unmanaged terrestrial systems including net primary production, decomposition, and carbon sequestration. Global change biology is currently altering above- and belowground interactions, reducing ecosystem services provided by natural systems. Less is known regarding how above- and belowground linkages impact climate resilience, especially in intentionally managed cropping systems. Waterlogged or flooded conditions will continue to increase across the Midwestern USA due to climate change. The objective of this paper is to explore what is currently known regarding above- and belowground linkages and how they impact biological, biochemical, and physiological processes in systems experiencing waterlogged conditions. We also identify key above- and belowground processes that are critical for climate resilience in Midwestern cropping systems by exploring various interactions that occur within unmanaged landscapes. Above- and belowground interactions that support plant growth and development, foster multi-trophic-level interactions, and stimulate balanced nutrient cycling are critical for crops experiencing waterlogged conditions. Moreover, incorporating ecological principles such as increasing plant diversity by incorporating crop rotations and adaptive management via delayed planting dates and adjustments in nutrient management will be critical for fostering climate resilience in row-crop agriculture moving forward.

CH4 and N2 O emissions from smallholder agricultural systems on tropical peatlands in Southeast Asia.

There are limited data for greenhouse gas (GHG) emissions from smallholder agricultural systems in tropical peatlands, with data for non-CO2 emissions from human-influenced tropical peatlands particularly scarce. The aim of this study was to quantify soil CH4 and N2 O fluxes from smallholder agricultural systems on tropical peatlands in Southeast Asia and assess their environmental controls. The study was carried out in four regions in Malaysia and Indonesia. CH4 and N2 O fluxes and environmental parameters were measured in cropland, oil palm plantation, tree plantation and forest. Annual CH4 emissions (in kg CH4 ha-1 year-1 ) were: 70.7 ± 29.5, 2.1 ± 1.2, 2.1 ± 0.6 and 6.2 ± 1.9 at the forest, tree plantation, oil palm and cropland land-use classes, respectively. Annual N2 O emissions (in kg N2 O ha-1 year-1 ) were: 6.5 ± 2.8, 3.2 ± 1.2, 21.9 ± 11.4 and 33.6 ± 7.3 in the same order as above, respectively. Annual CH4 emissions were strongly determined by water table depth (WTD) and increased exponentially when annual WTD was above -25 cm. In contrast, annual N2 O emissions were strongly correlated with mean total dissolved nitrogen (TDN) in soil water, following a sigmoidal relationship, up to an apparent threshold of 10 mg N L-1 beyond which TDN seemingly ceased to be limiting for N2 O production. The new emissions data for CH4 and N2 O presented here should help to develop more robust country level 'emission factors' for the quantification of national GHG inventory reporting. The impact of TDN on N2 O emissions suggests that soil nutrient status strongly impacts emissions, and therefore, policies which reduce N-fertilisation inputs might contribute to emissions mitigation from agricultural peat landscapes. However, the most important policy intervention for reducing emissions is one that reduces the conversion of peat swamp forest to agriculture on peatlands in the first place.

Environmental and agronomic assessment of soil conditioners produced from bauxite residue and oil palm wastes.

Soil conditioner is class of products used to enhance physics, physicochemical or soil biological activities, being able to recover disturbed or nutritional unbalanced soils. The formulation of a soil conditioner composed by bauxite residue (BR), and organic oil palm wastes, as raw materials, was recently proposed as an innovative strategy for the Brazilian acid soils amendment. Here we show the results of soil conditioner amended soil leaching tests and agronomical performance. The soil conditioners were formulated by BR mixed with decomposed POC (palm oil compost) and non-decomposed POMW (palm oil mill waste) oil palm wastes, in the proportion of 25% BR + 75% POC (T1) and 50% BR + 50% POMW (T2), in addition to the treatment with 100% POMW without BR (T3) and limestone at a dose calculated to raise soil pH to 6.0 (T4). Except for T4, all conditioners were applied to the soil at doses of 40, 80, and 120 t ha-1 for leaching tests. The experimental plots were composed of polyvinyl chloride columns, filled with 5 kg of soil, with bottles adapted with hoses at the bottom to facilitate drainage of the leachate. After leaching tests, the respective columns were used as pots for the cultivation of Brachiaria grass, stage with addition of a control composed by undisturbed soil (T5). The pH of the leachates had changes, but the use of BR associated with POMW was similar to the use of limestone. Of the 65 chemical elements evaluated, only nine were identified in the leachate, being most of them considered as plant nutrients. As for soil pH, limestone was slightly higher (6.6) than treatments that had BR (5.5). Brachiaria grass cultivated in the soil amended with conditioners showed similar results of limestone treated soil for the parameters of plant development and showed fertility improvement.

Perceived risk of COVID-19 pandemic on crop production: an implication for food security in Ethiopia.

BACKGROUND: Risk perception is a subjective psychological construct that is influenced by cognitive, emotional, social, cultural, and individual differences, both within and between individuals and across countries. Although the impact of COVID-19 on short- and long-term food security is difficult to predict, some risk factors and lessons from previous pandemics can be identified. The goal of this study is to assess rural farmers' perceptions of the COVID-19 pandemic's impact on crop production and its implications for food security in West Arsi Zone, Oromia, Ethiopia. METHODS: A community-based cross-sectional study was conducted among 634 small-holder farmers in the west Arsi zone district. From November 1-30, 2020, data was gathered through interviews with local farmers. Data was gathered using a semi-structured questionnaire. Six trained expert agricultural workers were used as data collectors and supervisors, respectively, and both were trained. The questionnaire had been pre-tested. The Statistical Package for the Social Sciences (SPSS) software version 25 was used to analyze the data. To identify factors associated with risk perception of the COVID-19 pandemic on crop production, binary and multivariable logistic regression were used, with statistical significance determined at a p-value of 0.05. RESULTS: This study found that among farmers in West Arsi Zone, Oromia, Ethiopia, about 32.5% reported having perceived risk of COVID-19 pandemic on crop production, and that age greater than or equal to 57, female sex (AOR,1.48 95% CI (1.03-2.12)), primary Educational status (AOR,2.85(1.78-4.58)), and permanent employed occupation of the house head (AOR, 2.27(1.24-4.17) were found to be independent predictors of perceived risk of COVID-19 pandemic on crop production among farmers in West Arsi Zone, Oromia, Ethiopia. CONCLUSION: Perceived risk of COVID-19 on crop production was high and varied across age groups, sexes, educational attainment levels, and the occupation of the head of the household.

On the accuracy of official Chinese crop production data: Evidence from biophysical indexes of net primary production.

With rapid economic growth and urbanization, self-sufficiency in crop production has become central to China's agriculture policy. Accurate crop production statistics are essential for research, monitoring, and planning. Although researchers agree that China's statistical authority has considerably modernized over time, China's economic statistics have still been viewed as unreliable and often overstated to meet growth targets at different administrative levels. Recent increases in crop production reported by national statistics have also come under increasing scrutiny. This paper investigates crop production data quality from a planetary boundary perspective-comparing net primary production (NPP) harvested obtained from national statistics with satellite-driven NPP estimates that are supported by detailed observation of land cover, combined with observations on physical factors that limit plant growth. This approach provides a powerful means to check the plausibility of China's grain production statistics at different administrative levels that can generate insights about their discrepancies and can contribute to improved crop production measurements. We find some evidence of potential misreporting problems from the lower administration level where the risk of manipulation of statistics is higher. We also find problems from provincial-level major grain producers. These values can also affect the national totals. Although the numbers are affected by large uncertainties, we find that improving the spatial resolution of key agricultural parameters can greatly improve the reliability of the indicator that in turn can help improve data quality. More reliable production data will be vital for relevant research and provide better insights into food security problems, the carbon cycle, and sustainable development.

Peak grain forecasts for the US High Plains amid withering waters.

Irrigated agriculture contributes 40% of total global food production. In the US High Plains, which produces more than 50 million tons per year of grain, as much as 90% of irrigation originates from groundwater resources, including the Ogallala aquifer. In parts of the High Plains, groundwater resources are being depleted so rapidly that they are considered nonrenewable, compromising food security. When groundwater becomes scarce, groundwater withdrawals peak, causing a subsequent peak in crop production. Previous descriptions of finite natural resource depletion have utilized the Hubbert curve. By coupling the dynamics of groundwater pumping, recharge, and crop production, Hubbert-like curves emerge, responding to the linked variations in groundwater pumping and grain production. On a state level, this approach predicted when groundwater withdrawal and grain production peaked and the lag between them. The lags increased with the adoption of efficient irrigation practices and higher recharge rates. Results indicate that, in Texas, withdrawals peaked in 1966, followed by a peak in grain production 9 y later. After better irrigation technologies were adopted, the lag increased to 15 y from 1997 to 2012. In Kansas, where these technologies were employed concurrently with the rise of irrigated grain production, this lag was predicted to be 24 y starting in 1994. In Nebraska, grain production is projected to continue rising through 2050 because of high recharge rates. While Texas and Nebraska had equal irrigated output in 1975, by 2050, it is projected that Nebraska will have almost 10 times the groundwater-based production of Texas.