A Review of the Prospective Effects of Spacing and Varieties on Onion Yield and Yield Components (Allium cepa L.) in Ethiopia.

Onion (Allium cepa L.) is the most important commercial vegetable crop widely grown throughout the world. It is also an important bulb crop in Ethiopia. However, its production and productivity are restricted by different factors, including biotic and abiotic stresses. This review investigates the potential impacts of spacing and varieties on onion yield and yield components in Ethiopia. Countries around the world are producing onion for its nutritional value, medicinal properties, minerals, proteins, and carbohydrates. In terms of production, onion ranks second only after tomatoes. The average onion yield in Ethiopia is estimated to be 8.8 tons/ha, while in the world, it is approximately 19.7 tons/ha. Inappropriate spacing and inadequate onion varieties are some of the limitations widely described for yield variation in Ethiopia. Thus, to control the size, shape, and yield of onion bulbs, spacing determination and variety improvement are some of the techniques currently employed in Ethiopia. Adama red, Bombay red, and red creole are some of the known varieties in the country, and the intrarow spacings for Adama red and Bombay red are reported to be 4 cm and 6 cm, respectively. Different spacing between onion plants affects how much they produce and other factors such as size and quality, depending on the variety. It is important to assess whether changing spacing makes sense from both a farming and economic standpoint, alongside considering other agricultural methods.

Driver-response relationships in a large shallow lake since the Anthropocene: Short-term abrupt perturbations versus long-term sustainable.

Lakes, as integral social-ecological systems, are hotspots for exploring climatic and anthropogenic impacts, with crucial pathways revealed by continuous sediment records. However, the response of multi-proxies in large shallow lakes to typical abrupt events and sustained drivers since the Anthropocene remains unclear. Here, we explored the driver-identification relationships between multi-proxy peaks and natural and anthropogenic events as well as the attribution of short-term perturbations and long-term pressures. To this end, sediment core records, socio-ecological data, and documented events from official records were integrated into a large shallow lake (Dongting Lake, China). Significant causal cascades and path effects (goodness-of-fit: 0.488; total effect: -1.10; p < .001) were observed among catchment environmental proxies, lake biogenic proxies, and mixed-source proxies. The peak-event identification rate (PEIR) and event-peak driving rate were proposed, and values of 28.57%-46.43% and 50%-81.25% were obtained, respectively. The incomplete accuracy of depicting event perturbations using sediment proxies was caused by various information filters both inside and outside the lake. PEIRs for compound events were 1.41 (±0.72) and 1.09 (±0.46) times greater than those for anthropogenic-dominated and natural-dominated events, respectively. Furthermore, socio-economic activity, hydrologic dynamics, land-use changes, and agriculture exerted significant and persistent pressures, cumulatively contributing 55.3%-80.9% to alterations in sediment proxies. Relatively synergistic or antagonistic trends in temporal contributions of these forces were observed after 2000, which were primarily attributed to the "Grain for Green" project and the Three Gorges Dam. This study represents one of the few investigations to distinguish the driver-response relationship of multiple proxies in large shallow lakes under typical event perturbations and long-term sustained pressures since the Anthropocene. The findings will help policymakers and managers address ecological perturbations triggered by climate change and human activities over long-term periods.

Greenhouse gas emissions, carbon stocks and wheat productivity following biochar, compost and vermicompost amendments: comparison of non-saline and salt-affected soils.

Understanding the impact of greenhouse gas (GHG) emissions and carbon stock is crucial for effective climate change assessment and agroecosystem management. However, little is known about the effects of organic amendments on GHG emissions and dynamic changes in carbon stocks in salt-affected soils. We conducted a pot experiment with four treatments including control (only fertilizers addition), biochar, vermicompost, and compost on non-saline and salt-affected soils, with the application on a carbon equivalent basis under wheat crop production. Our results revealed that the addition of vermicompost significantly increased soil organic carbon content by 18% in non-saline soil and 52% in salt-affected soil compared to the control leading to improvements in crop productivity i.e., plant dry biomass production by 57% in non-saline soil with vermicompost, while 56% with the same treatment in salt-affected soil. The grain yield was also noted 44 and 50% more with vermicompost treatment in non-saline and salt-affected soil, respectively. Chlorophyll contents were observed maximum with vermicompost in non-saline (24%), and salt-affected soils (22%) with same treatments. Photosynthetic rate (47% and 53%), stomatal conductance (60% and 12%), and relative water contents (38% and 27%) were also noted maximum with the same treatment in non-saline and salt-affected soils, respectively. However, the highest carbon dioxide emissions were observed in vermicompost- and compost-treated soils, leading to an increase in emissions of 46% in non-saline soil and 74% in salt-affected soil compared to the control. The compost treatment resulted in the highest nitrous oxide emissions, with an increase of 57% in non-saline soil and 62% in salt-affected soil compared to the control. In saline and non-saline soils treated with vermicompost, the global warming potential was recorded as 267% and 81% more than the control, respectively. All treatments, except biochar in non-saline soil, showed increased net GHG emissions due to organic amendment application. However, biochar reduced net emissions by 12% in non-saline soil. The application of organic amendments increased soil organic carbon content and crop yield in both non-saline and salt-affected soils. In conclusion, biochar is most effective among all tested organic amendments at increasing soil organic carbon content in both non-saline and salt-affected soils, which could have potential benefits for soil health and crop production.

Prospects of probiotics in beekeeping: a review for sustainable approach to boost honeybee health.

Honeybees are vital for global crop pollination, making indispensable contributions to agricultural productivity. However, these vital insects are currently facing escalating colony losses on a global scale, primarily attributed to parasitic and pathogenic attacks. The prevalent response to combat these infections may involve the use of antibiotics. Nevertheless, the application of antibiotics raises concerns regarding potential adverse effects such as antibiotic resistance and imbalances in the gut microbiota of bees. In response to these challenges, this study reviews the utilization of a probiotic-supplemented pollen substitute diet to promote honeybee gut health, enhance immunity, and overall well-being. We systematically explore various probiotic strains and their impacts on critical parameters, including survival rate, colony strength, honey and royal jelly production, and the immune response of bees. By doing so, we emphasize the significance of maintaining a balanced gut microbial community in honeybees. The review also scrutinizes the factors influencing the gut microbial communities of bees, elucidates the consequences of dysbiosis, and evaluates the potential of probiotics to mitigate these challenges. Additionally, it delineates different delivery mechanisms for probiotic supplementation and elucidates their positive effects on diverse health parameters of honeybees. Given the alarming decline in honeybee populations and the consequential threat to global food security, this study provides valuable insights into sustainable practices aimed at supporting honeybee populations and enhancing agricultural productivity.

Predictive modeling of methylmercury in rice (Oryza sativa L.) and species-sensitivity-distribution-based derivation of the threshold of soil mercury in karst mountain areas.

The bioavailable mercury (Hg) in the soil is highly active and can affect the formulation of methyl-Hg (MeHg) in soil and its accumulation in rice. Herein, we predicted the concentration of MeHg in rice using bioavailable Hg extracted from soils; additionally, we determined the threshold value of soil Hg in karst mountain areas based on species sensitivity distribution. The bioavailable Hg was extracted using calcium chloride, hydrochloric acid (HCl), diethylenetriaminepentaacetic acid mixture, ammonium acetate, and thioglycolic acid. Results showed that HCl is the best extractant, and the prediction model demonstrated good predictability of the MeHg concentration in rice based on the HCl-extractable Hg, pH, and soil organic matter (SOM) data. Compared with the actual MeHg concentration in rice, approximately 99% of the predicted values (n = 103) were within the 95% prediction range, indicating the good performance of the rice MeHg prediction model based on soil pH, SOM, and bioavailable Hg in karst mountain areas. Based on this MeHg prediction model, the safety threshold of soil Hg was calculated to be 0.0936 mg/kg, which is much lower than the soil pollution risk screening value of agricultural land (0.5 mg/kg), suggesting that a stricter standard should be applied regarding soil Hg in karst mountain areas. This study presents the threshold of soil Hg pollution for rice safety in karst mountain areas, and future studies should target this threshold range.

Graphical, statistical and index-based techniques integrated for identifying the hydrochemical fingerprints and groundwater quality of In Salah, Algerian Sahara.

Groundwater, a predominant reservoir of freshwater, plays a critical role in providing a sustainable potable water and water for agricultural and industry uses in the In Salah desert region of Algeria. This research collected 82 underground water samples from Albian aquifers to assess water quality and identify hydrogeochemical processes influencing mineralization. To achieve this objective, various methods were employed to evaluate water quality based on its intended uses. The drinking water quality index utilized revealed the water potability status, while the indicators of irrigation potability were employed to evaluate its quality for agricultural purposes. Additionally, an assessment of groundwater susceptibility to corrosion and scaling in an industrial context was conducted using several indices, e.g., Langelier index, Larson-Skold index, Ryznar index, chloride-sulfate mass ratio, Puckorius index, aggressiveness index, and the Revelle index. The findings of this study revealed that the groundwater quality for consumption fell into four categories: good (2.44%), fair (29.27%), poor (65.85%), and non-potable (2.44%). Concerning agricultural irrigation, the indexical results indicated that 15.85% of the waters exhibited adequate quality, while 84.15% were questionable for irrigation. Calculations based on various corrosion and scaling evaluation indices showed that most wells were prone to corrosion, with a tendency for calcium bicarbonate deposit formation. Furthermore, the hydrochemical study identified three water types: Na-Cl (53.66%), Ca-Mg-Cl (37.80%), and Ca-Cl (8.54%) waters. Analyses of correlation matrices, R-type clustering, factor loadings, Gibbs diagrams, scatterplots, and chloro-alkaline indices highlighted that the chemistry of the Albian groundwater is fundamentally impacted by a number of processes such as silicate weathering, evaporite dissolution, ionic exchange, and anthropogenic inputs, that played impactful role in the aquifer's water chemistry.

Irrigation intelligence-enabling a cloud-based Internet of Things approach for enhanced water management in agriculture.

Advanced sensor technology, especially those that incorporate artificial intelligence (AI), has been recognized as increasingly important in various contemporary applications, including navigation, automation, water under imaging, environmental monitoring, and robotics. Data-driven decision-making and higher efficiency have enabled more excellent infrastructure thanks to integrating AI with sensors. The agricultural sector is one such area that has seen significant promise from this technology using the Internet of Things (IoT) capabilities. This paper describes an intelligent system for monitoring and analyzing agricultural environmental conditions, including weather, soil, and crop health, that uses internet-connected sensors and equipment. This work makes two significant contributions. It first makes it possible to use sensors linked to the IoT to accurately monitor the environment remotely. Gathering and analyzing data over time may give us valuable insights into daily fluctuations and long-term patterns. The second benefit of AI integration is the remote control; it provides for essential activities like irrigation, pest management, and disease detection. The technology can optimize water usage by tracking plant development and health and adjusting watering schedules accordingly. Intelligent Control Systems (Matlab/Simulink Ver. 2022b) use a hybrid controller that combines fuzzy logic with standard PID control to get high-efficiency performance from water pumps. In addition to monitoring crops, smart cameras allow farmers to make real-time adjustments based on soil moisture and plant needs. Potentially revolutionizing contemporary agriculture, this revolutionary approach might boost production, sustainability, and efficiency.

Up-bottom assessments of nutrient supply and gaseous pollutant from Chinese wheat straw field management.

To achieve resource efficiency, and carbon neutrality, it is vital to evaluate nutrient supply and gaseous pollutant emissions associated with field management of bio-straw resources. Previous straw yield estimates have typically relied on a constant grain-to-straw yield ratio without accounting for grain yield levels in a given region. Addressing this high-resolution data gap, our study introduces a novel empirical model for quantifying grain-to-straw yield, which has been used to gauge wheat straw field management practices at the city level during 2011-2015. Utilizing both statistical review and GIS-based methods, average nitrogen (N), phosphorus (P), and potassium (K) supplies from straw field management stood at 1510, 1229, and 61700 tons, respectively. Average emissions of PM2.5, SO2, NOx, NH3, CH4, and CO2 due to straw burning were 367, 41, 160, 18, 165, and 70,644 tons, respectively. We also reported uncertainty from Monte Carlo model as the 5th-95th percentiles of estimated nutrient supply and gaseous pollutant. These insights will provide foundational support for the sustainable and environmentally friendly management of wheat straw in China.

Ozone pollution contributes to the yield gap for beans in Uganda, East Africa, and is co-located with other agricultural stresses.

Air quality negatively impacts agriculture, reducing the yield of staple food crops. While measured data on African ground-level ozone levels are scarce, experimental studies demonstrate the damaging impact of ozone on crops. Common beans (Phaseolus vulgaris), an ozone-sensitive crop, are widely grown in Uganda. Using modelled ozone flux, agricultural surveys, and a flux-effect relationship, this study estimates yield and production losses due to ozone for Ugandan beans in 2015. Analysis at this scale allows the use of localised data, and results can be presented at a sub-regional level. Soil nutrient stress, drought, flood risk, temperature and deprivation were also mapped to investigate where stresses may coincide. Average bean yield losses due to ozone were 17% and 14% (first and second growing season respectively), equating to 184 thousand tonnes production loss. However, for some sub-regions, losses were up to 27.5% and other crop stresses also coincided in these areas. This methodology could be applied widely, allowing estimates of ozone impact for countries lacking air quality and/or experimental data. As crop productivity is below its potential in many areas of the world, changing agricultural practices to mitigate against losses due to ozone could help to reduce the crop yield gap.

Assessing scale-dependency of climate risks in coffee-based agroforestry systems.

Agroforestry is a management strategy for mitigating the negative impacts of climate and adapting to sustainable farming systems. The successful implementation of agroforestry strategies requires that climate risks are appropriately assessed. The spatial scale, a critical determinant influencing climate impact assessments and, subsequently, agroforestry strategies, has been an overlooked dimension in the literature. In this study, climate risk impacts on robusta coffee production were investigated at different spatial scales in coffee-based agroforestry systems across India. Data from 314 coffee farms distributed across the districts of Chikmagalur and Coorg (Karnataka state) and Wayanad (Kerala state) were collected during the 2015/2016 to 2017/2018 coffee seasons and were used to quantify the key climate drivers of coffee yield. Projected climate data for two scenarios of change in global climate corresponding to (1) current baseline conditions (1985-2015) and (2) global mean temperatures 2 °C above preindustrial levels were then used to assess impacts on robusta coffee yield. Results indicated that at the district scale rainfall variability predominantly constrained coffee productivity, while at a broader regional scale, maximum temperature was the most important factor. Under a 2 °C global warming scenario relative to the baseline (1985-2015) climatic conditions, the changes in coffee yield exhibited spatial-scale dependent disparities. Whilst modest increases in yield (up to 5%) were projected from district-scale models, at the regional scale, reductions in coffee yield by 10-20% on average were found. These divergent impacts of climate risks underscore the imperative for coffee-based agroforestry systems to develop strategies that operate effectively at various scales to ensure better resilience to the changing climate.

A dataset for fine-grained seed recognition.

The research of plant seeds has always been a focus of agricultural and forestry research, and seed identification is an indispensable part of it. With the continuous application of artificial intelligence technology in the field of agriculture, seed identification through computer vision can effectively promote the development of agricultural and forestry wisdom. Data is the foundation of computer vision, but there is a lack of suitable datasets in the agricultural field. In this paper, a seed dataset named LZUPSD is established. A device based on mobile phones and macro lenses was established to acquire images. The dataset contains 4496 images of 88 different seeds. This dataset can not only be used as data for training deep learning models in the computer field, but also provide important data support for agricultural and forestry research. As an important resource in this field, this dataset plays a positive role in modernizing agriculture and forestry.

Oriented feature pyramid network for small and dense wheat heads detection and counting.

Wheat head detection and counting using deep learning techniques has gained considerable attention in precision agriculture applications such as wheat growth monitoring, yield estimation, and resource allocation. However, the accurate detection of small and dense wheat heads remains challenging due to the inherent variations in their size, orientation, appearance, aspect ratios, density, and the complexity of imaging conditions. To address these challenges, we propose a novel approach called the Oriented Feature Pyramid Network (OFPN) that focuses on detecting rotated wheat heads by utilizing oriented bounding boxes. In order to facilitate the development and evaluation of our proposed method, we introduce a novel dataset named the Rotated Global Wheat Head Dataset (RGWHD). This dataset is constructed by manually annotating images from the Global Wheat Head Detection (GWHD) dataset with oriented bounding boxes. Furthermore, we incorporate a Path-aggregation and Balanced Feature Pyramid Network into our architecture to effectively extract both semantic and positional information from the input images. This is achieved by leveraging feature fusion techniques at multiple scales, enhancing the detection capabilities for small wheat heads. To improve the localization and detection accuracy of dense and overlapping wheat heads, we employ the Soft-NMS algorithm to filter the proposed bounding boxes. Experimental results indicate the superior performance of the OFPN model, achieving a remarkable mean average precision of 85.77% in oriented wheat head detection, surpassing six other state-of-the-art models. Moreover, we observe a substantial improvement in the accuracy of wheat head counting, with an accuracy of 93.97%. This represents an increase of 3.12% compared to the Faster R-CNN method. Both qualitative and quantitative results demonstrate the effectiveness of the proposed OFPN model in accurately localizing and counting wheat heads within various challenging scenarios.

Utilizing plasma-generated N2O5 gas from atmospheric air as a novel gaseous nitrogen source for plants.

Fixing atmospheric nitrogen for use as fertilizer is a crucial process in promoting plant growth and enhancing crop yields in agricultural production. Currently, the chemical production of nitrogen fertilizer from atmospheric N2 relies on the energy-intensive Haber-Bosch process. Therefore, developing a low-cost and easily applicable method for fixing nitrogen from the air would provide a beneficial alternative. In this study, we tested the utilization of dinitrogen pentoxide (N2O5) gas, generated from oxygen and nitrogen present in ambient air with the help of a portable plasma device, as a nitrogen source for the model plant Arabidopsis thaliana. Nitrogen-deficient plants supplied with medium treated with N2O5, were able to overcome nitrogen deficiency, similar to those provided with medium containing a conventional nitrogen source. However, prolonged direct exposure of plants to N2O5 gas adversely affected their growth. Short-time exposure of plants to N2O5 gas mitigated its toxicity and was able to support growth. Moreover, when the exposure of N2O5 and the contact with plants were physically separated, plants cultured under nitrogen deficiency were able to grow. This study shows that N2O5 gas generated from atmospheric nitrogen can be used as an effective nutrient for plants, indicating its potential to serve as an alternative nitrogen fertilization method for promoting plant growth.

Metagenomics reveals the variations in functional metabolism associated with greenhouse gas emissions during legume-vegetable rotation process.

Legume-based rotation is commonly recognized for its mitigation efficiency of greenhouse gas (GHG) emissions. However, variations in GHG emission-associated metabolic functions during the legume-vegetable rotation process remain largely uncharacterized. Accordingly, a soybean-radish rotation field experiment was designed to clarify the responses of microbial communities and their GHG emission-associated functional metabolism through metagenomics. The results showed that the contents of soil organic carbon and total phosphorus significantly decreased during the soybean-radish process (P < 0.05), while soil total potassium content and bacterial richness and diversity significantly increased (P < 0.05). Moreover, the predominant bacterial phyla varied, with a decrease in the relative abundance of Proteobacteria and an increase in the relative abundance of Acidobacteria, Gemmatimonadetes, and Chloroflexi. Metagenomics clarified that bacterial carbohydrate metabolism substantially increased during the rotation process, whereas formaldehyde assimilation, methanogenesis, nitrification, and dissimilatory nitrate reduction decreased (P < 0.05). Specifically, the expression of phosphate acetyltransferase (functional methanogenesis gene, pta) and nitrate reductase gamma subunit (functional dissimilatory nitrate reduction gene, narI) was inhibited, indicating of low methane production and nitrogen metabolism. Additionally, the partial least squares path model revealed that the Shannon diversity index was negatively correlated with methane and nitrogen metabolism (P < 0.01), further demonstrating that the response of the soil bacterial microbiome responses are closely linked with GHG-associated metabolism during the soybean-radish rotation process. Collectively, our findings shed light on the responses of soil microbial communities to functional metabolism associated with GHG emissions and provide important insights to mitigate GHG emissions during the rotational cropping of legumes and vegetables.

A typology of subseasonal rainfall evolution during the southern Niger monsoon.

Niger is highly vulnerable to rainfall variability, often with adverse socioeconomic consequences. This study examined observed subseasonal rainfall variability during Niger's monsoon season (May to September). Using k-means clustering of dekadal (ten-day) rainfall, a typology was developed for the annual evolution of the monsoon season. Year-to-year rainfall variability for each of the first few dekads of the season is modest, but the middle, or peak of the rainy season demonstrates large interannual variability. Clustering analysis of annual timeseries for each dekad of the season revealed two types of monsoon progression. The distinction between the two types is strongly dependent on differences during the latter half of the season. For the first and third ten-day periods in August, and the first ten days in September, the two groups of years are more distinct. These results imply that while reliable prediction of the timing of anomalous onsets will be challenging, due to the relatively narrow range of uncertainty historically, there are opportunities for further exploration of dynamic and or statistical predictors or precursors using this typology that could potentially provide better information for decision-makers, especially with respect to agriculture.

A profile of urban agricultural growers, organizations, their needs, and challenges in the Northeastern United States.

Urban agriculture is increasingly valued as a strategy for improving quality of life in cities, but urban growers face challenges and often lack coordinated support from governments and the agricultural industry. We surveyed urban growers through an online survey, primarily in the Northeastern United States, to develop a profile of growers and associated organizations, assess the current state of urban agriculture, and determine how universities could help meet their needs. A total of 394 respondents completed the survey and most urban growers were white (non-Hispanic) and younger than 45 years old. Women and men were in almost equal proportion. Urban growers were well-educated, but most did not receive a degree in agriculture. Urban agriculture in our study area was dominated by relatively small non-profit organizations and home and community gardens were the most common types of organizations. Urban agricultural organizations want to improve environmental sustainability and socio-cultural conditions through food access and security, regardless of their tax status. Urban growers face diverse barriers and challenges and the most ubiquitous barriers and challenges reported by respondents were related to availability of land and long-term access in urban areas. Many respondents received low revenue or were operating at a net loss even though they reported diverse income streams. Respondents need a wide range of training, including in traditional agricultural topics as well as financial management and business trainings. Universities can play a key role in promoting urban agriculture by offering training and research. Workforce development is a large priority among universities, so urban growers should regularly be consulted, and the results shared with career and workforce development professionals and researchers in urban areas to identify training and research that meets the needs of stakeholders.

The agricultural carbon reduction effect of digital rural construction under the dual carbon target.

The exploration of the agricultural carbon emission reduction effect of digital rural construction offers a promising path towards achieving dual carbon goals. This study establishes an evaluation system for digital rural construction and analyzes its impact on agricultural carbon emissions using various creative techniques including panel fixed effects, mediation effects, threshold effects, and spatial Durbin models based on provincial panel data from 2011 to 2021.It is found that: (1) The impact of digital rural construction on agricultural carbon emissions exhibits a "inverted U-shaped" pattern, with a nonlinear effect on emissions through promoting agricultural green total factor productivity and adjusting agricultural structure.(2) Digital rural construction has both promoting and inhibiting effects on agricultural carbon emissions, both locally and in adjacent areas. It also demonstrates a threshold effect, with rural human capital as the sole threshold. Once the threshold value 8.830 is surpassed, the agricultural carbon emission reduction effect becomes prominent.(3)Digital rural construction has a dual effect on local agricultural carbon emissions in terms of both promoting and then restraining the emissions, which has a spatial spill-over effect in the neighboring areas. This study contributes to our understanding of carbon reduction pathways by highlighting the comprehensive utilization of digital rural construction and expanding research on the dynamic context of its impact on carbon emissions.

Impact of regenerative farming practices on soil quality and yield of cotton-sorghum system in semi arid Indian conditions.

Regenerative agricultural practices, i.e. organic and natural farming, are rooted in India since ancient times. However, the high cost of production, lack of organic pest control measures and premium price of organic produces in chemical agriculture encourage natural farming. In the present study, the quality improvement of calcareous soils under organic (OGF) and natural (NTF) management was compared with integrated conventional (ICF) and non-invasive (NIF) farming practices with cotton-sorghum crops over three consecutive years. A total of 23 soil attributes were analyzed at the end of the third cropping cycle and subjected to principal component analysis (PCA) to select a minimum data set (MDS) and obtain a soil quality index (SQI). The attributes soil organic carbon (SOC), available Fe, pH, bulk density (BD) and alkaline phosphatase (APA) were selected as indicators based on correlations and expert opinions on the lime content of the experimental soil. The SQI was improved in the order of OGF (0.89) > NTF(0.69) > ICF(0.48) > NIF(0.05). The contribution of the indicators to SQI was in the order of available Fe (17-44%) > SOC (21-28%), APA (11-36%) > pH (0-22%), and BD (0-20%) regardless of the farming practices. These indicators contribute equally to soil quality under natural (17-22%) and organic (18-22%) farming. The benefit:cost ratio was calculated to show the advantage of natural farming and was in the order of NTF(1.95-2.29), ICF (1.34-1.47), OGF (1.13-1.20) and NIF (0.84-1.47). In overall, the natural farming significantly sustained the soil quality and cost benefit compared to integrated conventional farming practices.

CARM30: China annual rapeseed maps at 30 m spatial resolution from 2000 to 2022 using multi-source data.

Rapeseed is a critical cash crop globally, and understanding its distribution can assist in refined agricultural management, ensuring a sustainable vegetable oil supply, and informing government decisions. China is the leading consumer and third-largest producer of rapeseed. However, there is a lack of widely available, long-term, and large-scale remotely sensed maps on rapeseed cultivation in China. Here this study utilizes multi-source data such as satellite images, GLDAS environmental variables, land cover maps, and terrain data to create the China annual rapeseed maps at 30 m spatial resolution from 2000 to 2022 (CARM30). Our product was validated using independent samples and showed average F1 scores of 0.869 and 0.971 for winter and spring rapeseed. The CARM30 has high spatial consistency with existing 10 m and 20 m rapeseed maps. Additionally, the CARM30-derived rapeseed planted area was significantly correlated with agricultural statistics (R2 = 0.65-0.86; p < 0.001). The obtained rapeseed distribution information can serve as a reference for stakeholders such as farmers, scientific communities, and decision-makers.

Harnessing market based mechanisms to improve water quality: Water quality trading policies in the river Alde, UK.

Nitrogen fertiliser in agriculture continues to be one of the largest contributors to water pollution driven by the global food demand. Consequently, policies designed to tackle nitrogen pollution tend to be focused on the farm level. Applying mitigation measures requires knowledge, local labour and financial investment to achieve desired goals. Influencing farming activity comes with challenges as policies result in economic losses. We propose Water Quality Trading (WQT) to minimize the cost of controlling water pollution and develop it for policy recommendations in the River Alde catchment in Suffolk. We apply WQT to three scenarios named Reference Pollution Target, Livestock Target Plan and Variation of Farming. Our findings demonstrate that WQT can reduce farmers nitrogen load by 8%, 7% and 18% respectively from the baseline of 6 mg/L. The scenario simulations show a net revenue increase of 6%, 5% and 18% respectively. Our study demonstrates the effectiveness of the WQT approach in reducing water pollution, promoting sustainable agriculture and meeting water management goals.