Agricultural Resources Research Articles

Enhancing food crop classification in agriculture through dipper throat optimization and deep learning with remote sensing

Remote sensing images (RSIs), a keystone of modern agricultural technology, refer to spectral or visual data captured from drones, satellites, or aircraft without direct physical contact with the Earth's surface. These images provide a wide-ranging view of agricultural landscapes, providing valuable insights into land use, crop health, and environmental conditions. Agricultural food crop classification, a vital application within precision agriculture, includes the detection and classification of different crops cultivated in a certain region. Traditionally reliant on manual techniques, the development of technologies, particularly the incorporation of RSIs, has revolutionized this process. Agricultural food crop classification has become more sophisticated and automated by harnessing the wealth of data received from RS, which facilitates precise management and monitoring of crops on a large scale. Deep learning (DL), a branch of artificial intelligence, plays a more effective role in these synergies. The incorporation of DL into the RSI analysis enables high-precision and efficient detection of various crop types, assisting more informed decision-making in agriculture. This study proposes a new Dipper Throat Optimization Algorithm with Deep Learning based Food Crop Classification (DTOADL-FCC) algorithm using Remote Sensing Imaging for Agricultural Resource Management. The DTOADL-FCC method aims to apply DL algorithms for the classification of different crop types. In the DTOADL-FCC method, fully convolutional network (FCN) based segmentation process is performed. Next, the DTOADL-FCC method exploits the SE-ResNet model for learning intrinsic and complex features. The DTOADL-FCC method makes use of DTOA for the hyperparameter tuning process. Lastly, the classification of crop types takes place using the extreme learning machine (ELM) model. The study utilizes mathematical formulations including activation functions, loss functions, fitness calculations, and iterative update processes. A brief set of simulations showcases that the DTOADL-FCC method achieves remarkable performance over other techniques with much improved results.

Agroecosystem-based agricultural genetic resources for balanced and diversified food, nutrition, health, business, geographical indication and environment

ABSTRACT Nepal has 15 distinct agroecosystems spread across three major agroecozones: Tarai, Mid Hill, and High Hill. These regions support a rich variety of agrobiodiversity. This paper examines the significance of native agrobiodiversity within each of these agroecosystems for diversified food, nutrition, health, business and environment. The information presented is derived from various literature sources, discussions with key experts, and the authors’ own experiences. Each of these agroecosystems has cultivated and preserved numerous unique species, landraces, and genotypes of agrobiodiversity, including crops, forages, livestock, agro-insects, agro-microorganisms, and aquatic genetic resources. This agrobiodiversity is deeply connected with local cultures, traditional knowledge, and skills. The high diversity of agricultural genetic resources (AGRs) within Nepal’s relatively small area is a crucial factor for ensuring food security, nutrition, health, business opportunities, and environmental sustainability. Many niche products, prized for their purity, quality, taste, health benefits, and nutritional value, are sold at premium prices. However, the focus on production-centric agricultural programs over the years has led to the replacement of many niche-specific varieties by more uniform and limited ones. Introducing new genotypes into a particular agroecosystem may undermine the principles of ecological agriculture. Therefore, it is essential to enhance the competitiveness of native AGRs and associated indigenous technologies within each agroecosystem.

Water-land-energy efficiency and nexus within global agricultural trade during 1995–2019

Agricultural product demand driven by population and economic growth poses challenges to water, land, and energy utilization, and this increasing local demand is largely met through trade. However, the efficiency and nexus pattern of the water, land, and energy embodied in agricultural trade are not well understood. This study uses the multi-regional input–output framework to analyze agricultural water, land, and energy utilization efficiency of resource footprints per unit economic output as well as their transfer and nexus pattern in global agricultural trade for 1995–2019. The results show that many international agricultural trade paths are inefficient in the water, land, and energy resource use because the agricultural products in these paths are exported from relatively low- to high-efficiency economies/regions. However, these inefficient transfer paths show an increasing trend over the study period. Regarding the water-land-energy nexus, conflicts are prevalent in land-energy and water-energy couplings. Most trade paths are conducted to alleviate the pressure on a specific resource, inadvertently increasing the pressure on other resources. Although agricultural trade is important for meeting global food demands, it is not consistently beneficial to the local environment when considering agricultural resources use efficiency. This study is expected to improve our understanding of agricultural trade impacts to the agricultural resources and support the sustainable development of global agriculture.

A Novel Approach for Farmland Size Estimation in Small-Scale Agriculture Using Edge Counting and Remote Sensing

Accurate and timely information on farmland size is crucial for agricultural development, resource management, and other related fields. However, there is currently no mature method for estimating farmland size in smallholder farming areas. This is due to the small size of farmland plots in these areas, which have unclear boundaries in medium and high-resolution satellite imagery, and irregular shapes that make it difficult to extract complete boundaries using morphological rules. Automatic farmland mapping algorithms using remote sensing data also perform poorly in small-scale farming areas. To address this issue, this study proposes a farmland size evaluation index based on edge frequency (ECR). The algorithm utilizes the high temporal resolution of Sentinel-2 satellite imagery to compensate for its spatial resolution limitations. First, all Sentinel-2 images from one year are used to calculate edge frequencies, which can divide farmland areas into low-value farmland interior regions, medium-value non-permanent edges, and high-value permanent edges (PE). Next, the Otsu’s thresholding algorithm is iteratively applied twice to the edge frequencies to first extract edges and then permanent edges. The ratio of PE to cropland (ECR) is then calculated. Using the North China Plain and Northeast China Plain as study areas, and comparing with existing farmland size datasets, the appropriate estimation radius for ECR was determined to be 1600 m. The study found that the peak ECR value for the Northeast China Plain was 0.085, and the peak value for the North China Plain was 0.105. The overall distribution was consistent with the reference dataset.

Investigating agricultural water sustainability in arid regions with Bayesian network and water footprint theories

Water scarcity is a significant constraint in agricultural ecosystems of arid regions, necessitating sustainable development of agricultural water resources. This study innovatively combines Bayesian theory and Water Footprint (WF) to construct a Bayesian Network (BN). Water quantity and quality data were evaluated comprehensively by WF in agricultural production. This evaluation integrates WF and local water resources to establish a sustainability assessment framework. Selected nodes are incorporated into a BN and continuously updated through structural and parameter learning, resulting in a robust model. Results reveal a nearly threefold increase of WF in the arid regions of Northwest China from 1989 to 2019, averaging 189.95 × 108 m3 annually. The region's agricultural scale is expanding, and economic development is rapid, but the unsustainability of agricultural water use is increasing. Blue WF predominates in this region, with cotton having the highest WF among crops. The BN indicates a 70.1 % probability of unsustainable water use. Sensitivity analysis identifies anthropogenic factors as primary drivers influencing water resource sustainability. Scenario analysis underscores the need to reduce WF production and increase agricultural water supply for sustainable development in arid regions. Proposed strategies include improving irrigation methods, implementing integrated water-fertilizer management, and selecting drought-resistant, economically viable crops to optimize crop planting structures and enhance water use efficiency in current agricultural practices in arid regions. This study not only offers insights into water management in arid regions but also provides practical guidance for similar agricultural contexts. The BN model serves as a flexible tool for informed decision-making in dynamic environments.

Comparative Assessment of Water Productivity and Crop Performance of Cluster Bean, Cowpea and Pearl Millet in Arid Zones

Arid and semi-arid regions face mounting challenges due to water scarcity exacerbated by climate change, necessitating sustainable agricultural practices to ensure food security and economic stability. This study compares the water productivity and crop performance of Cluster Bean (Cyamopsis tetragonoloba L), Cowpea (Vigna unguiculata L.) and Pearl Millet (Pennisetum glaucum L.) under sprinkle irrigation in the Thar Desert, India. The experiment, conducted over two years (2022 and 2023) at ICAR – CSWRI – Arid Region Campus, Bikaner evaluated plant height, leaf number, branch development, fresh and dry biomass yields and water productivity. Pearl Millet exhibited the tallest plants (160.03 cm) and highest water productivity (1.57 kg/m³), emphasizing its drought resilience and efficient water use. Cowpea demonstrated substantial biomass production (average fresh weight of 16,348.75 kg/hac.) and moderate water productivity (1.16 kg/m³), suitable for forage and soil improvement. Cluster Bean, although less productive in biomass, showed adaptability with increased branching (from 4.65 to 7.35 branches/tiller/plant), indicative of its resilience in arid conditions. These findings underscore the importance of crop selection and management practices in maximizing agricultural productivity and resource use efficiency in water-limited environments.

Soil erosion risk assessment of the Lakhmess watershed (northwestern Tunisia) via the SEAGIS model: Inferred prioritization of risky sub-watersheds

Water erosion in Tunisian semi-arid regions causes harmful effects by silting reservoirs and reducing agricultural lands and soil fertility. Several factors are involved in the erosion process: rain erosivity, soil fragility, and degraded land cover on steep slopes associated with the intensification of inappropriate human practices. Thus, identifying erosion vulnerable sub-watersheds based on the assessed soil loss rate is very important to apply suitable conservation measures. The current research aimed to prioritize risky areas in the Lakhmess watershed, north-west Tunisia via the Soil Erosion Assessment using Geographical Information System (SEAGIS) model. To prioritize sub-watersheds vulnerable to soil erosion and sediment yield, the Lakhmess watershed, covering an area of 162 km2, was divided into 16 sub-watersheds (L1–L16), according to the hydrographic network. Then, the mean annual soil erosion rate and the mean annual sediment yield in the watershed were estimated by integrating the Revised Universal Soil Loss Equation (RUSLE) and the Sediment Delivery Ratio (SDR) in the SEAGIS model and their spatial distribution was determined. The obtained results indicate that the estimated average annual soil erosion rate is 4.2 t/ha/y and the annual sediment yield is 2.6 t/ha/y. Maner's SDR model was selected as the best model for estimating SY, with standard error, standard deviation, and coefficient of variation values of 0.75%, 0.01, and 0.45%, respectively. The prioritization of the Lakhmess sub-watersheds based on the estimated soil loss rate reveals that among the 16 sub-watersheds, three sub-watersheds (L10, L12, and L15) were identified as being in a very high priority soil erosion class. The high soil erosion rate and sediment yield in these sub-watersheds is explained by the steep slope and a high rainfall erosivity factor. Six sub-watersheds (L2, L4, L5, L6, L7, and L16) were found to belong to a very low priority soil erosion class, as they are characterized by a very gentle slope, which appears to be an extremely determining factor. These findings constitute a basis for decision makers to plan effective conservation measures to conserve agricultural lands, soil, and water resources in northwestern Tunisia.

Advanced Forecasting of Drought Zones in Canada Using Deep Learning and CMIP6 Projections

This study addresses the critical issue of drought zoning in Canada using advanced deep learning techniques. Drought, exacerbated by climate change, significantly affects ecosystems, agriculture, and water resources. Canadian Drought Monitor (CDM) data provided by the Canadian government and ERA5-Land daily data were utilized to generate a comprehensive time series of mean monthly precipitation and air temperature for 199 sample locations in Canada from 1979 to 2023. These data were processed in the Google Earth Engine (GEE) environment and used to develop a Convolutional Neural Network (CNN) model to estimate CDM values, thereby filling gaps in historical drought data. The CanESM5 climate model, as assessed in the IPCC Sixth Assessment Report, was employed under four climate change scenarios to predict future drought conditions. Our CNN model forecasts CDM values up to 2100, enabling accurate drought zoning. The results reveal significant trends in temperature changes, indicating areas most vulnerable to future droughts, while precipitation shows a slow increasing trend. Our analysis indicates that under extreme climate scenarios, certain regions may experience a significant increase in the frequency and severity of droughts, necessitating proactive planning and mitigation strategies. These findings are critical for policymakers and stakeholders in designing effective drought management and adaptation programs.

The Nexus of Climate Change, Food Insecurity, and Conflict in Somalia: A Comprehensive Analysis of Multifaceted Challenges and Resilience Strategies

Climate change poses a significant threat to Somalia’s food security and stability. This review examines the complex interplay between climate change impacts, food insecurity, and conflict in Somalia. Recent climate data and forecasts paint a concerning picture: the rainfall probabilistic forecast for April- June 2024 indicates a high likelihood of above-normal rainfall across much of Somalia, thus increasing the flood risks. Concurrently, temperature forecasts project above-normal temperatures, potentially exacerbating drought conditions between the rainy seasons. Historical climatology data reveal Somalia’s vulnerability, with average temperatures ranging from 25°C to 30°C and significant seasonal variations affecting agriculture and water resources. These climate trends, combined with ongoing political instability and weak governance, have created a storm of challenges. This perspective explores how recurrent droughts and floods disrupt agricultural production, decimate livestock, and displace vulnerable populations, particularly those affecting rural and nomadic communities. It also examines how environmental stress exacerbates poverty and conflict, thereby creating a vicious cycle of vulnerability. This analysis draws on recent data on poverty rates, food expenditure patterns, and climate-related displacement, to illustrate the multifaceted nature of the crisis. Finally, this perspective proposed recommendations for building climate resilience, enhancing food security, and promoting sustainable development in Somalia, emphasizing the need for integrated approaches that combine immediate humanitarian assistance with long-term adaptation strategies.

Downscaling soil moisture in regions with high soil heterogeneity: the solution based on ensemble learning with sequential and parallel learner

Soil moisture plays an important role in the water and heat exchanges between the land surface and atmosphere, and it has great importance for agricultural production, ecological planning, and water resources management. Although microwave remote sensing has been widely used in large-scale soil moisture monitoring, the accuracy of the downscaled retrieval results cannot be guaranteed for regions with high vegetation coverage and high soil heterogeneity. To address these challenges, this study built soil moisture indice set based on MODIS and elevation data by calculating the Pearson correlation coefficient (R) and Maximum Information Coefficient (MIC), then constructed decision tree models (Gradient Boosting Decision Tree and Random Forest) about the indice set and low-resolution Soil Moisture Active Passive (SMAP) by using two ensemble learning methods (Bagging and Boosting). The models were applied to the high-resolution soil moisture indices in Jilin Province for the years 2017 to 2020 to generate 1 km-resolution products. In the validation process, Triple Collocation Analysis (TCA), comparison of soil moisture maps with coarse and fine resolution, and in-situ measurements in Lishu County, Tongyu County, and Jilin City were used to evaluate the differences between downscaling soil moisture results and ground observations at network, seasonal and point scales. The results were as follows: (1) The correlation coefficient (R2) calculated by the TCA method was 0.733 (GBDT_36km) > 0.649 (RF_36km), and the error variance was 0.0004 (GBDT_36km) < 0.00058 (RF_36km). (2) R at network scale was 0.798 (GBDT_SM) > 0.662 (RF_SM), RMSE was 0.040 (GBDT_SM) < 0.044 (RF_SM), the point scale R was 0.864 (GBDT_SM) > 0.833 (RF_SM), RMSE was 0.029 (GBDT_SM) < 0.039 (RF_SM). The R in four stages of the growth period was GBDT_SM > RF_SM, RMSE was GBDT_SM < RF_SM. In conclusion, the GBDT and RF models can reliably downscale soil moisture in Jilin Province, and the Boosting ensemble learning method represented by GBDT had a better estimation performance.

Sharp decline in surface water resources for agriculture and fisheries in the Lower Mekong Basin over 2000-2020

Water resources play a crucial role in the global water cycle and are affected by human activities and climate change. However, the impacts of hydropower infrastructures on the surface water extent and volume cycle are not well known. We used a multi-satellite approach to quantify the surface water storage variations over the 2000–2020 period and relate these variations to climate-induced and anthropogenic factors over the whole basin. Our results highlight that dam operations have strongly modified the water regime of the Mekong River, exhibiting a 55 % decrease in the seasonal cycle amplitude of inundation extent (from 3178 km2 to 1414 km2) and a 70 % decrease in surface water volume (from 1109 km3 to 327 km3) over 2000–2020. In the floodplains of the Lower Mekong Basin, where rice is cultivated, there has been a decline in water residence time by 30 to 50 days. The recent commissioning of big dams (2010 and 2014) has allowed us to choose 2015 as a turning point year. Results show a trend inversion in rice production, from a rise of 40 % between 2000 and 2014 to a decline of 10 % between 2015 and 2020, and a strong reduction in aquaculture growth, from +730 % between 2000 and 2014, to +53 % between 2015 and 2020. All these results show the negative impact of dams on the Mekong basin, causing a 70 % decline in surface water volumes, with major repercussions for agriculture and fisheries over the period 2000–2020. Therefore, new future projects such as the Funan Techo canal in Cambodia, scheduled to start construction at the end of 2024, will particularly affect 1300 km2 of floodplains in the lower Mekong basin, with a reduction in the amount of water received, and other areas will be subjected to flooding. The human, material and economic damage could be catastrophic.

Factors Affecting Socio-Economic Status of the Residents of Mbeere North; Embu County Kenya

Mbeere North, a rural constituency in Embu County, Kenya, faces significant socio-economic challenges despite its potential for agricultural productivity and resource utilization. The region, inhabited predominantly by the Mbeere community and characterized by diverse ethnic groups, suffers from inadequate infrastructure, limited access to quality healthcare and education, and a reliance on subsistence farming. These issues are exacerbated by environmental degradation and climate change, leading to high poverty rates and economic instability. This study assessed the socio-economic status of Mbeere North's residents by identify the factors influencing their well-being. The research employed a descriptive design, targeting a sample of 150 residents, including households, community leaders, business owners, and providers of education and healthcare. Data collection methods included questionnaires and interviews, with a focus on key areas such as infrastructure, health, education, and economic activities. The findings revealed a population with low levels of education, limited healthcare access, and average monthly household incomes of KES 15,000. Infrastructure development is rated moderately, with significant variability in perceptions among respondents. The theoretical framework integrated Gary Becker's Human Capital Theory and Structural Functionalism. The paper presents analysis of descriptive statistics on socio-economic indicators, alongside the results of regression analysis. This analysis sheds light on the relationships between various independent variables, including education, health, economy, infrastructure, social services, and welfare, and the dependent variable, socio-economic status. Regression analysis revealed a strong positive correlation between the independent variables (education, healthcare, and infrastructure) and socio-economic status, with an R value of 0.75 and an R Square of 0.5625. In conclusion, Mbeere North requires targeted interventions to improve infrastructure, diversify agriculture, and enhance healthcare and education services. Recommendations include promoting agro-processing industries, investing in modern farming techniques, and developing eco-tourism. These strategies are essential for fostering economic growth, improving living standards, and achieving sustainable development in the region. Keywords: Effects of Socio-Economic Status on the Residents of Mbeere North; Embu County Kenya.

Integrated machine learning and geospatial analysis enhanced gully erosion susceptibility modeling in the Erer watershed in Eastern Ethiopia

Land degradation from gully erosion poses a significant threat to the Erer watershed in Eastern Ethiopia, particularly due to agricultural activities and resource exploitation. Identifying erosion-prone areas and underlying factors using advanced machine learning algorithms (MLAs) and geospatial analysis is crucial for addressing this problem and prioritizing adaptive and mitigating strategies. However, previous studies have not leveraged machine learning (ML) and GIS-based approaches to generate susceptibility maps identifying these areas and conditioning factors, hindering sustainable watershed management solutions. This study aimed to predict gully erosion susceptibility (GES) and identify underlying areas and factors in the Erer watershed. Four ML models, namely, XGBoost, random forest (RF), support vector machine (SVM), and artificial neural network (ANN), were integrated with geospatial analysis using 22 geoenvironmental predictors and 1,200 inventory points (70% used for training and 30% for testing). Model performance and robustness were validated through the area under the curve (AUC), accuracy, precision, sensitivity, specificity, kappa coefficient, F1 score, and logarithmic loss. The relative slope position is most influential, with 100% importance in SVM and RF and 95% importance in XGBoost, while annual rainfall (AR) dominated ANN (100% importance). Notably, XGBoost demonstrated robustness and superior prediction/mapping, achieving an AUC of 0.97, 91% accuracy, 92% precision, and 81% kappa while maintaining a low logloss (0.0394). However, SVM excelled in classifying gully resistant/susceptible areas (97% sensitivity, 98% specificity, and 91% F1 score). The ANN model predicted the most areas with very high gully susceptibility (13.74%), followed by the SVM (11.69%), XGBoost (10.65%), and RF (7.85%) models, while XGBoost identified the most areas with very low susceptibility (70.19%). The ensemble technique was employed to further enhance GES modeling, and it outperformed the individual models, achieving an AUC of 0.99, 93.5% accuracy, 92.5% precision, 97.5% sensitivity, 95.4% specificity, 85.8% kappa, and 94.9% F1 score. This technique also classified the GES of the watershed as 36.48% very low, 26.51% low, 16.24% moderate, 11.55% high, and 9.22% very high. Furthermore, district-level analyses revealed the most susceptible areas, including the Babile, Fedis, Harar, and Meyumuluke districts, with high GES areas of 32.4%, 21.3%, 14.3%, and 13.6%, respectively. This study offers robust and flexible ML models with comprehensive validation metrics to enhance GES modeling and identify gully prone areas and factors, thereby supporting decision-making for sustainable watershed conservation and land degradation prevention.

The impact of labor force aging on agricultural total factor productivity of farmers in China: implications for food sustainability

IntroductionThe rapid aging of the labor force has serious implications for socioeconomic development and poses challenges to food sustainability. Existing studies have focused on the impact of labor force aging on land productivity, but little attention has been paid to the comprehensive capacity of agricultural production, namely the total factor productivity of agriculture.MethodsWe estimate the impact of labor force aging on total factor productivity in agriculture using data from 170,506 sample farm households from the National Fixed Point Survey data from 2003 to 2020. We estimate the results using a panel fixed effects approach and mitigate the endogeneity problem using an instrumental variables approach to ensure the robustness of the estimates.Results and discussionThe results show that labor force aging has an adverse effect on the agricultural total factor productivity of farm households and remains robust after addressing endogeneity and substituting explanatory variables. The aging effect shows significant heterogeneity across different food functional areas with different types of farm households. The negative impact of labor force aging on total factor productivity in agriculture is mainly concentrated in the main grain-production and grain-producing and marketing-balanced areas. We also find that the negative impact of labor force aging is more significant among large-scale farmers. The mechanism analysis finds that the aging of the labor force adversely affects the agricultural total factor productivity by inhibiting technological progress and reducing the efficiency of agricultural resource allocation. These insights, when considered in the light of global trends towards agricultural labor aging, suggest that policy interventions aimed at promoting technological adoption, enhancing resource allocation efficiency, and supporting the transitioning of older farmers may hold promise for maintaining food sustainability and addressing the challenges posed by an aging agricultural workforce in numerous developing nations.

The ratio of transpiration to evapotranspiration and water use efficiency in an irrigated oasis agroecosystem: Different temporal-scale effects

Agroecosystems play an important role in carbon sequestration and water consumption of global terrestrial ecosystems. However, the magnitude, pattern, and regulation of the ratio of transpiration to evapotranspiration (T/ET), and also water use efficiency at ecosystem (WUEe) and canopy (WUEc) scales, remain unclear in agroecosystems in arid areas. In this study, a six-year synchronous observation of water-carbon fluxes and sap flow by the eddy covariance and sap flow techniques was conducted in an irrigated vineyard in northwest China. We found that T/ET, WUEe, and WUEc changed dynamically at the daily (1.0–98.6%, 0.3–4.9 g C kg–1 H2O, 0.4–5.9 g C kg–1 H2O) and monthly (26.0–71.0%, 0.8–1.8 g C kg–1 H2O, 2.3–3.2 g C kg–1 H2O) scales. At the annual scale, the range was relatively narrow, with mean values of 61.7% ± 3.7%, 1.5 ± 0.1 g C kg–1 H2O, and 2.4 ± 0.3 g C kg–1 H2O, respectively (mean ± standard deviation). Biological and environmental factors regulated their dynamics on different temporal scales. Specifically, leaf area index (LAI) and canopy conductance (gc) mainly determined the seasonal variation of T/ET, WUEe, and WUEc at the daily and monthly scales. However, at the annual scale, vapor pressure deficit (VPD) and air temperature (Ta) became the main influencing factors. These results highlight the complexity of water-carbon coupling in agroecosystems and the necessity of considering specific factors at different temporal scales when modeling and managing agricultural water resources. In addition, there is still great water-saving potential from the perspective of WUE in the cultivation of vines in Northwest China.

Environmental Factors Affecting Spatio-Temporal Distribution of Crop-Exploiting Species: Implications for Coexistence Between Agricultural Production and Avifauna Conservation in Wetlands

Bird communities in agroecosystems bring both ecosystem services (e.g., pollination) and disservices (e.g., crop exploitation) to farmers. However, in the proximity of wetland reserves, farmers disproportionately experience harvest yield loss due to large aggregation of bird species that can utilize various agricultural resources. This often results in negative human–wildlife interactions which lower conservation support among farmers. Knowledge about the distribution of avian species that negatively influence yields, and its environmental drivers is thus fundamental to reconcile crop production and bird conservation. This study aims to examine the spatio-temporal patterns in richness and abundance of bird species known to cause agricultural yield loss as well as species-specific distribution patterns for the six bird species that are most challenging for local farmers. In combination with interview surveys of local farmers (n = 367) and seasonal bird surveys (n = 720), we investigated distribution of crop-exploiting avian species in the Indawgyi wetland ecosystem in Myanmar. Our results showed high richness and abundance of crop-exploiting species in the water habitat across all seasons, with most challenging species exhibiting higher presence closer to these water sources. The crop phenology had positive effect on species richness and abundance during the growing season. The agricultural use of crop-exploiting species was season- and species-specific, where the presence probability in the agricultural habitat was higher in habitat generalists than wetland specialists. Therefore, we suggest improved management of natural wetland habitats (e.g., habitat restoration), sustainable coexistence mechanisms in farms close to water (e.g., bird-friendly rice farming and Ecolabel certification) to reduce avian impacts on the farming communities and, at the same time, to promote bird conservation in wetlands of international importance.

Protection of Agricultural Genetic Resources in China

The protection of agricultural genetic resources, which represents a core element in the agricultural intellectual property (IP) system, will play an important role in realizing China's Rural Revitalization Strategy. In order to substantiate the need for improved agricultural property protection in China, the article examines existing issues in this area, including a weak understanding of IP protection on the part of rural people, imperfections in the existing legal framework, a lack of creative personnel working in the IP, inadequate support from government and other departments, as well as a lacking innovation mindset on the part of entrepreneurs. Proposed approaches for improving the legal awareness of agricultural genetic resource protection include increasing professional competencies in the aforementioned area and ensuring the participation of rural people in the protection of their agricultural genetic resources.

Hyperthermophilic composting of livestock waste drastically reduces antimicrobial resistance

Composting is the most common method for managing livestock waste. However, it often fails to effectively eliminate antimicrobial resistance, resulting in potential antimicrobial resistance in livestock waste. Hence, a more effective livestock waste management strategy is warranted. The hyperthermophilic composting method uses recycled compost containing hyperthermophilic microbes to raise the compost temperature up to approximately 100 °C (compost temperature in normal composting: 50–70 °C). This study clarifies the effectiveness of hyperthermophilic composting method in attenuating antimicrobial resistance during livestock waste composting in both a composting simulator and on a field dairy farm. We analyzed bacterial abundance and community composition, performed PCR analysis, and evaluated the concentration of residual antimicrobials in the compost. Hyperthermophilic composting significantly reduced the abundance of culturable bacteria and Escherichia coli including that of antimicrobial-resistant culturable bacteria and E. coli in both the simulated and field dairy farms. The copy numbers of the tested antimicrobial resistance genes (tetA, tetB, blaTEM, and blaCTX-M) decreased substantially. Residual antimicrobials (tetracyclines and β-lactams) were not detected in the field dairy farms, and network analysis showed that potential hosts of antimicrobial-resistance genes were eliminated. These results indicate that hyperthermophilic composting significantly reduces the abundance of antimicrobial-resistant bacteria, antimicrobial-resistance genes, and residual antimicrobials at low cost and easy operation while generating valuable agricultural resource as the final output.

Modeling Gender Gap in Access to Empowerment and Agricultural Productive Resources: Evidence from Farming Households in Kaduna State

This research examined gender access to empowerment and agricultural productive resources with a gender dimension. Respondents were selected with random and stratified sampling techniques. Primary data were generated through the administration of well-structured questionnaire. Generated data were analyzed with descriptive statistics, Women Empowerment Index (WEI), Global Gender Parity Index (GGPI) and Gini Inequality Index (GII). The result shows that 37% and 60.20% of women and men had access to empowerment programmes respectively in the study area. WEI result showed that women had low access to empowerment variable (WEI = 0.67), the GGPI revealed that women (GGPI = 0.63) had low parity as against the men (GGPI = 0.77) with higher parity index. Furthermore, the Gini Inequality Index that there is inequality in access to agricultural productive resources between men (GII = 0.286) and women (GII = 0.383). The research concluded women had low empowerment and low access to agricultural productive resources as against the men. The research recommended that government programmes should be mainstreamed to ensure that more women have access to them and the extension department should design their programmes to ensure that more women have access to extension services in the study area.

Automatic mapping of winter wheat planting structure and phenological phases using time-series sentinel data

The precise extraction of winter wheat planting structure holds significant importance for food security risk assessment, agricultural resource management, and governmental decision-making. This study proposed a method for extracting the winter wheat planting structure by taking into account the growth phenology of winter wheat. Utilizing the fitting effect index, the optimal Savitzky–Golay (S–G) filtering parameter combination was determined automatically to achieve automated filtering and reconstruction of NDVI time series data. The phenological phases of winter wheat growth was identified automatically using a threshold method, and subsequently, a model for extracting the winter wheat planting structure was constructed based on three key phenological stages, including seeding, heading, and harvesting, with the combination of hierarchical classification principles. A priori sample library was constructed using historical data on winter wheat distribution to verify the accuracy of the extracted results. The validation of fitting effect on different surfaces demonstrated that the optimal filtering parameters for S–G filtering could be obtained automatically by using the fitting effect index. The extracted winter wheat phenological phases showed good consistency with ground-based observational results and MOD12Q2 phenological products. Validation against statistical yearbook data and the proposed priori knowledge base exhibited high statistical accuracy and spatial precision, with an extracting accuracy of 94.92%, a spatial positioning accuracy of 93.26%, and a kappa coefficient of 0.9228. The results indicated that the proposed method for winter wheat planting structure extracting can identify winter wheat areas rapidly and significantly. Furthermore, this method does not require training samples or manual experience, and exhibits strong transferability.