Agricultural Management Research Articles

Integrated Subsurface Hydrologic Modeling for Agricultural Management Using HYDRUS and UZF Package Coupled with MODFLOW

The present work aims to compare two different subsurface hydrological models, namely HYDRUS and MODFLOW UZF package, in terms of groundwater recharge; thus, both models were coupled with MODFLOW. The study area is an experimental kiwifruit orchard located in the Arta plain in the Epirus region of Greece. A novel conceptual framework is introduced in order to (i) use in situ and laboratory measurements to estimate parameter values for both sub-surface flow models; (ii) couple the developed models with MODFLOW to estimate groundwater recharge; and (iii) compare and evaluate the performance of both approaches, with differences stemming from the distinctive equations describing the flow in the unsaturated zone. Detailed soil investigation was conducted in two soil horizons in the research field to identify soil texture zones, along with infiltration experiments implementing both double-ring and single-ring infiltrometers. The results of the field measurements indicate that fine-textured soils are predominant within the field, affecting several hydrological processes, such as infiltration, drainage, and root water uptake. Field measurements were incorporated in unsaturated zone flow modeling and the infiltration fluxes were simulated with the application of both the UZF package of MODFLOW and the HYDRUS code. The two codes presented acceptable agreement between the simulated and observed hydraulic head values with a similar performance in terms of statistics; however, they produced different results regarding recharge rates in the aquifer as simulated by MODFLOW. HYDRUS produced higher hydraulic head values in the aquifer throughout the simulation, related to higher recharge rates arising from the root water uptake and the capillary effects that are computed by HYDRUS but neglected by the UZF package of MODFLOW.

A First Assessment of Greenhouse Gas Emissions From Agricultural Peatlands in Canada: Evaluation of Climate Change Mitigation Potential

ABSTRACTCanada has a quarter of the world's peatlands accounting for an estimated 150 Gt of stored carbon. While over 98% of Canadian peatlands are intact, agriculture has been estimated as accounting for the greatest peatland disturbance by area. Greenhouse gas (GHG) emissions from peatland agriculture can contribute a large proportion of national anthropogenic emissions for some countries. In Canada, estimates of GHG emissions from cultivated peat soils are incomplete. Improved accounting of these GHG emissions is required to inform decisions about where to deploy ecological restoration projects and where to allow future agricultural expansion as climate warms. Compiled studies that measured GHG fluxes from agricultural peat fields in Canada resulted in mean emissions factors of 5.1 t CO2e ha−1 year−1, −0.12 kg CH4 ha−1 year−1, and 14.3 kg N2O‐N ha−1 year−1 for carbon dioxide, methane, and nitrous oxide, respectively. Combining these values with a compilation of estimates of agricultural peatland disturbance area in Canada, GHG emissions estimates in Canada arising from peatland converted to agriculture remain highly uncertain, ranging from 1.4 to 35 Mt CO2e year−1, with a median value near 18 Mt CO2e year−1. The largest contributor to this wide range of estimates is uncertainty peatland area affected, indicating an urgent need to improving mapping of organic soils under agriculture in Canada. To help guide decision‐making in Canada, we recommend a network of research stations across a range of agricultural management intensities and climate regions for monitoring hydrological conditions and GHG exchange on organic soils affected by agriculture.

Ontology-based Data Integration Framework for Smart Pig Farms

Semantic web technology is known to enhance data for the connection of information using a network that can be easily read by machines. This work applies an ontology to integrate data involved in swine farm management towards the development of smart farm for swine. To realize environmental data, IoT devices including sensors, camera, and RFID readers were deployed in a pig farm. Despite differences in data format from different sources, an ontology was crafted to provide semantic annotations that could unify the data and enhance them with tangible relationships based on the domain knowledge. The core concepts of this ontology were sensing data with temporal information and identifiable data to represent relationship between real-time environmental features and the pigs within the pen to improve pig management. The semantically enhanced data can thus be applied with semantic-based queries and inference reasoner. Smart services including monitoring, decision support, alerting, and automatic executing based on the deployed IoT devices are thus enabled to support farm managements towards smart farm for swine. From the results, we found that the integrated sensing information from sensors and knowledge given in the developed ontology with inference rules assists the task of interpretation of monitored information and decision-making to improve swine farm management. The data gathered could be extended by combining two or more factors such as temperature and humidity into heat index using knowledge inference to precisely understand environmental information and activate the relevant machine automatically.

Seasonal forecasts have sufficient skill to inform some agricultural decisions

Abstract Seasonal forecasts, which look several months into the future, are currently underutilized in active decision-making, particularly for agricultural and natural resource management. This underutilization can be attributed to the absence of forecasts for decision-relevant variables at the required spatiotemporal resolution and at the time when the decisions are made and a perception of poor skill by decision-makers. Addressing these constraints, we quantified the skill of seasonal forecasts in informing two agricultural decisions with differing decision timeframes and influencer variables: (a) whether to apply fertilizer in fall or wait until spring based on expected winter temperatures, and (b) drought response, such as whether to lease water based on expectations of drought. We also looked into how early the forecast can be provided without significant degradation in skill. Currently, drought response decisions are typically formulated in April, utilizing drought forecasts issued in the same month, while fall fertilization decisions are generally made between August and September. There is growing interest among stakeholders in the availability of earlier forecasts to inform these critical choices. We utilized the North American Multi-Model Ensemble (NMME) hindcasts for the time period 1982 - 2020 over the Pacific Northwest US to obtain meteorological variables. Runoff was estimated via simulations of the coupled crop-hydrology VIC-CropSyst model. The skill assessment with the Heidke Skill Score (HSS) yielded promising outcomes in both decisions for the entire Pacific Northwest region. Notably, NMME’s positive skill (median HSS of 30%) in predicting warmer winters identifies years when fertilizer application should be avoided to prevent fertilizer loss through mineralization (and associated costs). Similarly, there is skill in forecasting drought conditions in most irrigated watersheds for up to two months in advance of April, the current decision time. In conclusion, our findings affirm that contrary to the perception of low skill and resulting underutilization, current seasonal forecasts hold the potential to inform at least some key agricultural decisions.

Tropical Interbasin Interaction as Effective Predictors of Late-Spring Precipitation Variability in the Southern Great Plains

Abstract The southern Great Plains experience fluctuating precipitation extremes that significantly impact agriculture and water management. Despite ongoing efforts to enhance forecast accuracy, the underlying causes of these climatic phenomena remain inadequately understood. This study elucidates the relative influence of the tropical Pacific and Atlantic basins on April–May–June precipitation variability in this region. Our partial ocean assimilation experiments using the Community Earth System Model unveil the prominent role of interbasin interaction, with the Pacific and Atlantic contributing approximately 70% and 30%, respectively, to these interbasin contrasts. Our statistical analyses suggest that these tropical interbasin contrasts could serve as a more reliable indicator for late-spring precipitation anomalies than El Niño–Southern Oscillation. The conclusions are reinforced by analyses of seven climate forecasting systems within the North American Multi-Model Ensemble, offering an optimistic outlook for enhancing real-time forecasting of late-spring precipitation in the southern plains. However, the current predictive skills of the interbasin contrasts across the prediction systems are hindered by the lower predictability of the tropical Atlantic Ocean, pointing to the need for future research to refine climate prediction models further. Significance Statement Agriculture and infrastructure in the southern plains face challenges from severe late-spring precipitation extremes. Traditional predictors like El Niño–Southern Oscillation (ENSO) lose effectiveness during the critical spring-to-summer transition, creating a forecasting gap. This study introduces the concept of tropical interbasin interactions, known to enhance seasonal predictability for late-spring precipitation in the southern plains. Novel climate model experiments highlight contributions from the tropical Pacific and Atlantic, offering a promising predictability that potentially surpasses the limitations of ENSO-based predictions. These outcomes hold the potential for developing operational forecasts of late-spring precipitation anomalies in the southern plains, enabling proactive risk management.

Predicting Wheat Potential Yield in China Based on Eco-Evolutionary Optimality Principles

Accurately predicting the wheat potential yield (PY) is crucial for enhancing agricultural management and improving resilience to climate change. However, most existing crop models for wheat PY rely on type-specific parameters that describe wheat traits, which often require calibration and, in turn, reduce prediction confidence when applied across different spatial or temporal scales. In this study, we integrated eco-evolutionary optimality (EEO) principles with a universal productivity model, the Pmodel, to propose a comprehensive full-chain method for predicting wheat PY. Using this approach, we forecasted wheat PY across China under typical shared socioeconomic pathways (SSPs). Our findings highlight the following: (1) Incorporating EEO theory improves PY prediction performance compared to current parameter-based crop models. (2) In the absence of phenological responses, rising atmospheric CO2 concentrations universally benefit wheat growth and PY, while increasing temperatures have predominantly negative effects across most regions. (3) Warmer temperatures expand the window for selecting sowing dates, leading to a national trend toward earlier sowing. (4) By simultaneously considering climate impacts on wheat growth and sowing dates, we predict that PY in China’s main producing regions will significantly increase from 2020 to 2060 and remain stable under SSP126. However, under SSP370, while there is no significant trend in PY during 2020–2060, increases are expected thereafter. These results provide valuable insights for policymakers navigating the complexities of climate change and optimizing wheat production to ensure food security.

Forecasting Grain Production in Hubei Province Based on Markov Modeling

Abstract. Grain production plays a crucial role in economic and social development. Accurate prediction of grain production can help agricultural decision-makers allocate resources rationally and do a good job at the next stage of the arrangements for the deployment of the work.This article used the production data, which was from 2023 Hubei Provincial Statistical Yearbook,to simulate and predict the grain production for several years,with the method of Markov prediction. It can be seen from the results that the average fitting error of the model is 3.47%. Using the text dataset of grain production from 2020 to 2022, the results show that the model can effectively improve the prediction accuracy of agricultural grain procuction. Finally, the model is used to predict the agricultural output value structure of China from 2023 to 2027. Markov prediction can improve the predictability and initiative of the work and provide scientific basis for the future decision-making of agricultural managers, and has guiding significance for the work of grain storage and distribution.

Genetic-Based Neural Network for Enhanced Soil Texture Analysis: Integrating Soil Sensor Data for Optimized Agricultural Management

Soil texture analysis is vital in agricultural management due to its influence on crop growth and yield. Defined by the proportions of clay, sand, and silt particles, soil texture affects properties like aeration, water-holding capacity, and nutrient retention, all crucial for plant development. The OBJECTIVES: This study aims to design a Genetic-Based Neural Network (GBNN) for accurate soil texture analysis, particularly for soils with similar structures but different compositions. It also aims to collect environmental impact data through soil sensors to enhance the understanding of soil texture.METHODS: The methodology involves developing a GBNN, leveraging genetic algorithms to group homogeneous particles, thus improving texture classification. This approach addresses the shortcomings of previous deep learning models. Additionally, soil sensor data will be collected to study environmental factors affecting soil texture.RESULTS: The GBNN showed improved accuracy in texture classification compared to previous models. Genetic algorithms effectively grouped similar particles, and soil sensor data provided insights into environmental impacts on soil texture.CONCLUSION: The GBNN for soil texture analysis overcame previous models' challenges, improving classification accuracy. The integration of soil sensor data provided valuable environmental insights, aiding farmers in optimizing crop selection, fertilizer application, and soil management for better yields and sustainability.

Insights into pupal development of Bactrocera dorsalis: factors influencing eclosion.

In the life cycle of the oriental fruit fly where larvae reside within fruits and adults exhibit high activity, the pupal stage occurs in the soil, closely tied to agricultural soil management. This study investigates the impact of four variables (body orientation, burial depth, soil particle size, and pH) on Bactrocera dorsalis' physiological preferences, eclosion rates, and pupal stage duration. Notably, body orientation, burial depth, soil texture (particle size), and pH affect eclosion rates. The fruit fly demonstrates a preference for a supine position during eclosion, with average eclosion rates of 97.33%, contrasting with decreases to 78.00% in a head-down position. Soil with mixed particle sizes is detrimental to pupal eclosion, reducing the eclosion rate to 56.67%. And at a pH of 9.98, eclosion rates decrease to 16.67%. These factors significantly influence eclosion rates However, these factors do not significantly alter the duration of the pupal stage, with mean changes not exceeding one day when experimental conditions are modified. These findings suggests that soil manipulations affecting these variables could reduce eclosion rates without compromising the uniformity of adult emergence. This study provides a foundation for environmentally friendly pest control practices and addresses gaps in the pupal stage research of the oriental fruit fly.

Enhancing sustainable Chinese cabbage production: a comparative analysis of multispectral image instance segmentation techniques

Accurate instance segmentation of individual crops is crucial for field management and crop monitoring in smart agriculture. To address the limitations of traditional remote sensing methods in individual crop analysis, this study proposes a novel instance segmentation approach combining UAVs with the YOLOv8-Seg model. The YOLOv8-Seg model supports independent segmentation masks and detection at different scales, utilizing Path Aggregation Feature Pyramid Networks (PAFPN) for multi-scale feature integration and optimizing sample matching through the Task-Aligned Assigner. We collected multispectral data of Chinese cabbage using UAVs and constructed a high-quality dataset via semi-automatic annotation with the Segment Anything Model (SAM). Using mAP as the evaluation metric, we compared YOLO series algorithms with other mainstream instance segmentation methods and analyzed model performance under different spectral band combinations and spatial resolutions. The results show that YOLOv8-Seg achieved 86.3% mAP under the RGB band and maintained high segmentation accuracy at lower spatial resolutions (1.33 ~ 1.14 cm/pixel), successfully extracting key metrics such as cabbage count and average leaf area. These findings highlight the potential of integrating UAV technology with advanced segmentation models for individual crop monitoring, supporting precision agriculture applications.

Precision Agriculture and Water Conservation Strategies for Sustainable Crop Production in Arid Regions

The intensifying challenges posed by global climate change and water scarcity necessitate enhancements in agricultural productivity and sustainability within arid regions. This review synthesizes recent advancements in genetic engineering, molecular breeding, precision agriculture, and innovative water management techniques aimed at improving crop drought resistance, soil health, and overall agricultural efficiency. By examining cutting-edge methodologies, such as CRISPR/Cas9 gene editing, marker-assisted selection (MAS), and omics technologies, we highlight efforts to manipulate drought-responsive genes and consolidate favorable agronomic traits through interdisciplinary innovations. Furthermore, we explore the potential of precision farming technologies, including the Internet of Things (IoT), remote sensing, and smart irrigation systems, to optimize water utilization and facilitate real-time environmental monitoring. The integration of genetic, biotechnological, and agronomic approaches demonstrates a significant potential to enhance crop resilience against abiotic and biotic stressors while improving resource efficiency. Additionally, advanced irrigation systems, along with soil conservation techniques, show promise for maximizing water efficiency and sustaining soil fertility under saline–alkali conditions. This review concludes with recommendations for a further multidisciplinary exploration of genomics, sustainable water management practices, and precision agriculture to ensure long-term food security and sustainable agricultural development in water-limited environments. By providing a comprehensive framework for addressing agricultural challenges in arid regions, we emphasize the urgent need for continued innovation in response to escalating global environmental pressures.

Enzymatic Biosensors: Advanced Tools for Soil and Plant Health Monitoring

Soil health is the potential of the soil to support the productivity of organisms, preserve the environment, and improve the health of plants and animals. Healthy soil is vital for agricultural sustainability and environmental resilience. The presence of high concentrations of toxic chemicals (pollutants and contaminants) in the soil poses a significant risk to human health and the ecosystem. Plant health is influenced by various factors, including the surrounding environment. To minimize constraints such as low nutrient levels, ecosystem contamination/pollution, and pest and disease incidences in crops, and to maximize productivity and ensure agricultural sustainability, advanced detection and management strategies are urgently needed. Diagnosis, prediction, and monitoring are essential for managing agricultural practices to mitigate these harmful impacts. The development of improved biosensing devices has been driven by the demand for quick, accurate, sensitive, real-time, and fast instruments for screening and identifying contaminants. Specific enzymes are used as the biological sensing element in an enzyme-based biosensor, which is combined with a transducer to transform the signal from the enzymatic reaction into a quantifiable response proportionate to the analyte concentration. Enzymatic biosensors need to possess high specificity, selectivity, reproducibility, stability, a low limit of detection, a low limit of quantification, and a rapid response time. They have a diverse array of uses, such as soil quality monitoring, water monitoring, food quality monitoring, pathogen detection, drug discovery, disease identification, and environmental research.

A mixed method approach to analysing patterns and drivers of antibiotic use and resistance in beef farms in Argentina

IntroductionAntimicrobial resistance is a challenge to be faced by all livestock sectors; within beef farming, antibiotic use patterns vary by country and management practices. Argentina is a country with high beef production & consumption but limited information surrounding antibiotic use. The aims of this project was to understand how antibiotics are being used across the beef industry in Argentina and exploring drivers of usage.MethodsQuantitative and qualitative data was collected by: A survey of breeding and feedlot farms including antibiotic use (from purchase data); a detailed analysis of two feedlot farms’ therapeutic antibiotic use records; a survey of vets’ views on certain antibiotic practices; and a focus group of farmers and vets focusing on wider influences affecting decision making. Antibiotic use data was calculated using mg/population corrected unit (PCU) (ESVAC) and thematic analysis was used to identify drivers of antibiotic use among participants.ResultsThe median use across 17 farms that supplied purchase data was 76.52 mg/kg PCU (ESVAC; IQR = 36.81 mg/kg PCU [ESVAC]). The detailed farm records showed that the largest reason for treatment was group treatments (72.92% of treatments) followed by treatment for respiratory disease (12.75% of treatments). Macrolides accounted for 76.37% of treatments. Nearly half of farms used routine prophylactic treatment for arriving animals (n = 7/18). The use of quarantine and ‘sick pens’ were seen as important by surveyed vets with antibiotic prophylaxis and in-feed antibiotics seen as contributors to antibiotic resistance. The focus group highlighted the influence of the economic and political landscape on husbandry practices and the responsibility the farming sector had towards antibiotic stewardship.DiscussionOverall, Argentine beef feedlots resemble North American beef feedlots in terms of antibiotic practices but with considerably lower usage, with in-feed monensin representing a large proportion of total ABU. The adaptation period presents a challenge to animal health; antibiotics are administered a prophylaxis, metaphylactic and individual treatments depending on farm management practices. Further research into internationally comparable measures of ABU and detailed cost-benefit analysis of practical, on-farm interventions are needed to aid improved antimicrobial stewardship in livestock systems globally.

Macro- and micro-plastic accumulation in soils under different intensive farming systems: a case study in Quzhou county, the North China Plain.

The macroplastics (MaPs) and microplastics (MiPs) polluting agricultural soils raise great concerns. Unfortunately, scientists know little about the occurrence of MaPs/MiPs in soil among different farming systems. In this study, we analyzed MaPs/MiPs in soils (0‒30 cm) collected from six different farming systems (wheat-maize rotations, cotton, vegetables, permanent orchards, greenhouses with and without mulching) in Quzhou county, the North China Plain, by using fluorescence microscope and micro-Fourier transform infrared spectroscopy. The results showed that the abundance of MaPs and MiPs ranged from 0.2 to 46.8 kg ha‒1, and 4.1×103‒3.7×104 items kg‒1, respectively. The prominent colors of the MaPs were white and black. The predominant shape, size and chemical composition of soil MiPs were fragments (45‒62%), < 1 mm (98‒99%), and polyethylene (38‒43%), respectively. MaPs were mainly detected in the 0‒10 cm soil layer. MiP abundance in the 0‒10 cm soil layer was significantly higher than that in the 20‒30 cm soil layers among different farming systems, except for the fields with wheat-maize rotations and permanent orchards (p < 0.05). Overall, cotton fields showed the highest MaP and MiP abundance, followed by vegetable fields and orchards. Redundancy analysis revealed that tillage practices and plastic film management greatly influence the size distribution of MiPs. A strong negative correlation between large-sized plastic fractions (0.2‒1 mm) and tillage frequency was tested while the years of application of plastic films and the abundance of plastic residues showed a strong positive correlation with small-sized plastic fractions (< 0.2 mm). Our findings conclude that agricultural mulch films are an important source of MaPs and MiPs in agricultural soil and distributions are strongly influenced by agricultural management practices and farming systems. Further studies should take farming systems and farming practices into account, thereby exploring the potential mechanisms of plastic fragmentation and granularization in agricultural soil.

Influence of Farmer’s Income Level on Adoption of Agricultural Water Management Practices among Smallholder Farmers in Rongai Sub-county, Kenya

Water is a resource that cannot be replaced and can only be renewed if it is well managed. It is basic for all forms of life, for every aspects of socio-economic development, and for the maintenance of a healthy ecosystem. Agriculture is the biggest water user globally, accounting for 70 percent of total water withdrawals on average. There are different practices that can aid in managing water used for agriculture. Some of these agricultural water management practices are; rainwater harvesting, irrigation, organic farming, and use of drought resistant crops. Farmer’s decision to either adopt or reject these practices can be affected by several factors, some of which are socio-economic. Some of the socio-economic factors that may influence adoption of these practices include farmer’s educational level, income level and farm size. This study examined the influence of farmer’s income level on adoption of agricultural water management practices (Rainwater harvesting, Irrigation Drought resistant crops). Cross-sectional survey design was adopted, while proportionate and simple random sampling technique was used to obtain the respondents. The accessible population was 6,230 smallholder farmers from the target population of 26,804 smallholder farmers in Rongai sub county Kenya. The study was done in August 2023 to November 2023.The study included 120 smallholder farmers in Rongai Sub County. The study used questionnaire to collect data while binary logistic regression was used to analyze the data. The results indicated that adoption of agricultural water management practices is low, only 36% of smallholder farmers had adopted agricultural water management practices. The P value calculated for the 120 smallholder farmers was P=.033 which is &lt; than .05 and therefore the null hypothesis was rejected concluding that in this study income level had statistically significant influence on adoption of agricultural water management practices among smallholder farmers in Rongai sub-county in Nakuru ,Kenya. The findings may help in emphasizing the necessity of assisting farmers in removing financial obstacles that may hinder adoption of agricultural water management practices.

Assessing Post-Vaccination Seroprevalence and Enhancing Strategies for Lumpy Skin Disease Vaccination in Korean Cattle

Lumpy skin disease (LSD), caused by the LSD virus (LSDV), a dsDNA virus of the genus Capripoxvirus, represents a significant cross-border infectious threat, particularly impacting cattle and water buffaloes through transmission by blood-feeding insects. Traditionally endemic to Southern Africa, LSD has rapidly spread over the past decade through the Middle East to Eastern Europe and China, reaching Korea in October 2023. This outbreak prompted a nationwide vaccination campaign, addressing both the disease’s severe economic impact and its status as a notifiable disease under the World Organisation for Animal Health. This study assesses the seropositivity of the LSD vaccine in cattle across four Korean provinces 2–3 months post-vaccination, aiming to inform improvements in biosecurity and vaccination strategies. Overall, 30.59% of the cattle tested (1196 out of 3910) exhibited positive antibody responses, comparable to international post-vaccination findings. Analysis further revealed differences in the antibody positivity between farm types and management practices. Specifically, farms where vaccines were administered by veterinarians showed no significant difference in antibody positivity between Korean native cattle and dairy cattle, regardless of the presence of restraint facilities. However, on farms where vaccinations were conducted by the owners, dairy cattle demonstrated a higher seropositivity (43.30 ± 33.39%) compared to Korean native cattle (21.97 ± 20.79%) in the absence of restraint facilities. Further comparisons underscored the impact of restraint facilities on vaccination efficacy, with dairy farms generally achieving higher antibody positivity (29.43 ± 30.61%) than farms with Korean native cattle (23.02 ± 23.33%) (p &lt; 0.05), suggesting that consistent vaccine delivery methods enhance immunogenic responses. Contrarily, no significant difference was noted in antibody positivity between large- and small-scale farms, indicating that farm size did not notably impact the effectiveness of the vaccinator. These findings emphasize that while current vaccines are sufficiently inducing immunity, enhancing vaccination strategies, particularly through trained personnel and improved restraint facilities, is crucial. This study’s insights into the impact of vaccination and farm management practices provide valuable guidance for refining LSD control measures in Korea and potentially other affected regions.

Machine learning-based crop type detection and vegetation mon-itoring using Sentinel-1 SAR and Landsat 8

This study explores the use of machine learning for crop type detection and vegetation monitoring in arid and semi-arid regions, specifically in Biskra and Khenchela, Algeria. Traditional field-based agricultural monitoring is labor-intensive and limited in spatial coverage, prompting the need for more efficient and scalable methods. The research integrates Synthetic Aperture Radar (SAR) data from Sentinel-1 and optical imagery from Landsat 8 to enhance the classification of different crop types and track vegetation dynamics. Key metrics such as radar backscatter and Normalized Difference Vegetation Index (NDVI) are employed to distinguish between irrigated and rainfed crops. The Random Forest algorithm is utilized for classification, with training data derived from field surveys, high-resolution satellite imagery, and existing land cover maps. The Google Earth Engine (GEE) platform facilitates large-scale data processing, providing real-time agricultural analysis. Over the 12-month study period (January to December 2020), the integrated approach demonstrated high accuracy, achieving over 85% precision in classifying key crops such as date palms, cereals, and vegetables. Results indicate that SAR and NDVI data provide complementary insights into vegetation health, phenology, and agricultural practices in different climatic zones. This methodology offers a promising solution for improving agricultural monitoring and resource management in arid and semi-arid regions, enhancing food security and optimizing water usage. The study’s findings also underscore the value of satellite-based remote sensing for large-scale, real-time agricultural analysis.

The economic, social, and environmental impact of ecologically centered integrated pest management practices in East Africa

Agricultural pest management faces mounting challenges with increasing pressure to reduce chemical pesticide use while ensuring food security, and environmental sustainability. Ecologically centered approaches, such as integrated pest management (IPM), offer promising sustainable agroecological crop protection alternative solutions to pesticides. This study assesses the investment viability and environmental sustainability of two IPM interventions—mango fruit fly IPM (FF-IPM) and push-pull technology (PPT) in Kenya and Uganda, using project investment and adoption data from 2007 to 2021. The study also evaluates these technologies contribution to food security and poverty reduction. FF-IPM integrates biological control, cultural practices, and targeted bait traps to manage fruit flies in mango production, while PPT employs Desmodium and Brachiaria grass to control stemborers and striga weed. The findings highlight that these IPM interventions achieved a combined net present value of $500 million, with a benefit-cost ratio of about 8:1, and an internal rate of return of 21%, comparable to returns from improved crop varieties. These technologies also improved food security for 641,000 people, lifted 445,349 people above the poverty line, representing 2% of the poor population in both countries, and generated an average income increase of $5 per capita in Kenya and Uganda annually. Environmentally, they sequestered 2.7 million tons of CO2 equivalent, valued at $12.2 million, and are reducing pesticide use by 86,000 to 204,000 L per year. These results demonstrate the potential of IPM approaches to simultaneously enhance livelihoods, local economies, and generate global environmental public goods, suggesting that addressing adoption barriers could further amplify these positive outcomes.

Interannual variations of normalized difference vegetation index and potential evapotranspiration and their relationship in the Baghdad area

Abstract A monthly correlation between urban vegetation growth and potential evapotranspiration (PET) is needed for better knowledge of controlling water resources and organized irrigation processes. This study aims to explore their relationship within an urban area like Baghdad, using a linear regression model to derive a best-fit line drawn in a scatterplot on a monthly time scale. Based on two different monthly data sources: weather variables (e.g., air temperature, solar radiation, and relative humidity) and Sentinel-2 satellite imagery of 2 years, 2018 and 2021, this study presented the interannual variations of PET and normalized difference vegetation index (NDVI). The choice of these years has a significant feature of climatic differences, which are arid and semi-arid, respectively. PET values were estimated by the Truc method, while the areas of vegetation (represented by NDVI) were calculated using the Geographic Information Sensing program. The results show that the maximum PET in both years was found in the summer months (June and July) with mean values of about 8.8 mm/day, while their minimum mean values of about 1.5 mm/day occurred in winter months (January and December). From the spatial distribution of NDVI, it was found that at positive pixels when NDVI &gt;0.2, vegetation cover in March, April, and December 2018 had large areas with more than 200 km2 in 2018, while they were largest only in May 2021 with 197.8 km2. There was a linear correlation with slope (0.03) and intercept (= 1.8) and a strong correlation, R 2 = 0.72. The practical implications of the findings contribute to enhancing a solid scientific basis for improving agricultural water management, especially under dry conditions.

Agricultural Water Conservation Methods: Application, Difficulties, and Innovation

The largest growth in water usage among water-using industries has been in agriculture due to the growing need for agricultural goods to feed the world's expanding population. Presently, agricultural water conservation encompasses a range of technologies that are mostly grounded in human understanding, but also include a number of complex strategies that significantly rely on cutting-edge mechanical and electrical techniques. Modern agronomic practices like precision agriculture may conserve water. Precision agriculture is a modern agronomic strategy that may preserve water.Modern agricultural investments, land transfers, and urbanisation have left these irregular and traditionally built villages with an ambiguous and ever-changing land use pattern. This may hinder water-saving adoption. In addition to taking into account the growth of the agricultural sector, the agricultural water management policy prioritises the long-term ecological economy and the sustainability of the water resource supply network. Water needs are predicted to rise significantly by the end of the twenty-first century, particularly for irrigation. Reducing the amount of water used in agriculture is crucial. With temperatures rising and droughts occurring more frequently in many nations, water is a vital component of agricultural output. Certain technology-based practices coupled with efficient farmers and humans management of water resources will be the key features for improving water conservation.