Agricultural Model Research Articles

Assessing the pig microbial health impacts of smallholder farming

The livestock industry has long been a hotspot environment for antibiotic resistance genes, with smallholder farming still holding a significant position in pig farming. However, the microbial antibiotic resistance and pathogen risks in pigs under the smallholder farming model remain unclear. We systematically analyzed the antibiotic resistance and microbial composition of pig feces from smallholder and large-scale farming models in Sichuan. The results indicated a lower abundance of antibiotic resistance genes (ARGs) and similar microbial composition in smallholder farming compared to large-scale farming. Beneficial bacteria were more abundant in small-scale farming, whereas large-scale farming exhibited more ARGs, virulence genes, and human pathogenic bacteria (HPBs), including ESBL Escherichia coli strains closely related to human strains, indicating higher zoonotic risk. The findings suggest that smallholder farming presents a relatively better microbial composition and resistance profile, highlighting its advantages over large-scale farming in terms of pig and human health. It is noteworthy that a considerable proportion of HPBs carrying ARGs still exist in the feces from smallholder farming, and given the openness of fecal handling, there remains a high risk of transmitting ARGs and pathogens to humans.

Avoiding siloed approaches: Integrating psychological insights into sustainable farming.

This study enhances our understanding of the psychological factors influencing farmers' adoption of sustainable farming practices, specifically those related to achieving NetZero emissions. It achieves this by integrating various psychological theories with practical farming methods within the context of Behavioral-Adoption and Purpose-Driven contexts. The research consisted of two studies. Study 1 employed Exploratory Factor Analysis (EFA) to analyze responses from 438 UK farmers regarding their attitudes toward a series of Net Zero policy commitments, drawing on psychological theories including the Unified Theory of Acceptance and Use of Technology, the Theory of Planned Behavior, the Prototype Willingness Model, Implementation Intentions, Self-Determination Theory, Eudaimonia, and the Integrated Model of Health Literacy. The findings revealed a new Integrated Motivation Model for Sustainable Farming that comprises seven factors: Agricultural Commitment and Stewardship, Sustainable Farming Readiness and Confidence, Sustainable Incentive Engagement and Acceptance, Climate Adaptation Competence and Confidence, Net Zero Accountability and Reporting Commitment, Community Influence and Commitment in Sustainable Farming, and Innovation and Technological Competence. Study 2 validated these factors through the development of a 21-item Integrated Motivation Model for Sustainable Farming scale and use of Confirmatory Factor Analysis (CFA) to confirm the 7-factor structure using a subsample of 418 UK farmers from Study 1 and an additional 230 US farmers. Furthermore, Study 2 tested the concurrent validity of the new scale by demonstrating significant associations with reported sustainable farming behaviors. These findings underscore the complex interplay of motivational, cognitive, and social processes influencing sustainable farming practices. The integrated psychological model developed through this research provides parsimonious and valuable insights for policymakers to foster sustainable practices in farming effectively. The confirmation of this model across farming populations enhances its generalizability and potential to guide targeted interventions aimed at achieving behavioral change in pursuit of Net Zero targets in agriculture.

Strategic Categorization of Dairy Cow Farms in Croatia using Cluster Analysis

Abstract Background The milk processing sector in the Republic of Croatia faces numerous challenges. It is a distinctly bipolar structure, with some entities resembling the largest milk producers in the EU, while many small and medium-sized dairy farms struggle to remain competitive and achieve further progress. To formulate effective policy, it is important to differentiate between these types and address their key challenges. Objectives The aim is to find the most representative solution that will help us define typical dairy farms and upgrade a SiTFarm tool (Slovenian Typical Farm Model), enabling us to assess the situation in Croatia comprehensively. Methods/Approach Cluster analysis was conducted using empirical data obtained from the Croatian Agency for Agriculture and Food. The analysis involved applying both hierarchical and non-hierarchical clustering techniques. Results Two cluster analysis scenarios are presented, differing in the variables used. In each scenario, 16 relatively homogeneous clusters of farms were obtained. Diversity was minimized within these clusters, and they effectively explain the dairy business in Croatia. Conclusions The results of this analysis thus represent an important starting point for further analysis of the dairy sector in Croatia. These findings could help policymakers identify the types of farms that would benefit most from targeted investments to enhance efficiency, economic viability, and environmental sustainability.

Contract Farming & Cost of Funding: Evidence from Madurase Solar Salt Small Businesses

This study aimed to estimate the indirect expenses borne by tenant farmers due to credits they receive in community salt farming operations managed through contract farming systems in Pamekasan Regency. Credits constitute a crucial aspect of tenant farmers’ rights within the community salt farming contract farming framework. This study employed a comparative analysis of the cost of funds based on the adopted contract farming models, specifically the two-way and three-way contract farming structures. Moreover, it investigated the correlation between the cultivated salt field’s size and the magnitude of the cost of funds endured by tenant farmers. The results of the analysis reveal that the average costs of funds borne by tenant farmers are notably high, ranging from 5.24% to 6.71% per month. Surprisingly, these figures exceed the loan interest rates offered by two formal financial institutions: BRI and Bank JATIM. Another significant finding is the positive correlation between the size of cultivated salt fields and the magnitude of the cost of funds borne by tenant farmers. Lastly, there is a clear positive relationship between the credit amount received and the corresponding cost of funds endured by tenant farmers. The substantial costs of funds shouldered by tenant farmers involved in the community salt farming, operated through contract farming systems in Pamekasan Regency, signify inefficiencies within the contract farming structure. It is expected that the government can aid these tenant farmers by disbursing subsidized credits through collaborations with relevant ministries and local formal financial institutions.

Impacts of aquaculture practices on the sustainability of social-ecological systems in coastal zones of the Mekong Delta

Coastal and deltaic regions face increasing natural and human-induced hazards, such as cyclones, storm surges, erosion, and saline intrusion, exacerbated by climate change. In tropical zones, mangrove ecosystems provide vital protection against these threats, yet the conversion of mangroves to shrimp aquaculture undermines these benefits, even in protected areas.The Mekong Delta in Vietnam, historically rich in biodiversity and diverse habitats like mangroves and mudflats, has seen local communities thrive. However, rising global market demands have led to more intensive aquaculture models, posing new risks.This research examines the impact of shrimp farming models, from extensive to hyper-intensive, under multiple natural hazards and transformative processes like land conversion and mangrove restoration. Using risk assessment tools and data from local community interviews and focus groups, we present a model showcasing the influence of aquaculture on socio-ecological systems.Key findings reveal the interplay between livelihoods and landscape conversion, highlighting the risks and benefits of various shrimp farming models. The study stresses the need for stakeholder collaboration and improved wastewater treatment to enhance sustainability and environmental health in the Mekong Delta.

A Dataset of Visible Light and Thermal Infrared Images for Health Monitoring of Caged Laying Hens in Large-Scale Farming.

Considering animal welfare, the free-range laying hen farming model is increasingly gaining attention. However, in some countries, large-scale farming still relies on the cage-rearing model, making the focus on the welfare of caged laying hens equally important. To evaluate the health status of caged laying hens, a dataset comprising visible light and thermal infrared images was established for analyses, including morphological, thermographic, comb, and behavioral assessments, enabling a comprehensive evaluation of the hens' health, behavior, and population counts. To address the issue of insufficient data samples in the health detection process for individual and group hens, a dataset named BClayinghens was constructed containing 61,133 images of visible light and thermal infrared images. The BClayinghens dataset was completed using three types of devices: smartphones, visible light cameras, and infrared thermal cameras. All thermal infrared images correspond to visible light images and have achieved positional alignment through coordinate correction. Additionally, the visible light images were annotated with chicken head labels, obtaining 63,693 chicken head labels, which can be directly used for training deep learning models for chicken head object detection and combined with corresponding thermal infrared data to analyze the temperature of the chicken heads. To enable the constructed deep-learning object detection and recognition models to adapt to different breeding environments, various data enhancement methods such as rotation, shearing, color enhancement, and noise addition were used for image processing. The BClayinghens dataset is important for applying visible light images and corresponding thermal infrared images in the health detection, behavioral analysis, and counting of caged laying hens under large-scale farming.

Wind power prediction based on deep learning models: The case of Adama Wind farm

Wind is a renewable energy source that is used to generate electricity. Wind power is one of the suitable solutions for global warming since it is free from pollution, doesn’t cause greenhouse effects, and it is a natural source of energy. However, Wind power generation highly depends on weather conditions. It is very difficult to easily predict the amount of power generated from wind at a particular instant in time. Adama wind power farm is one of the wind farms in Ethiopia. There is no accurate and reliable forecasting model for the Adama wind farm that enables the forecasting of the power generated from the farm. The main objective of this research is to develop a wind power forecasting model for the Adama wind farm using deep learning techniques. Forecasting of wind power generation capacity involves appropriate modeling techniques that use past wind power generation data. The experiments have been conducted using Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (Bi-LSTM) and Gated Recurrent Unit (GRU). To achieve the highest forecasting accuracy, four years of data (from 2016-2019), with five-minute intervals, have been collected with a total of 163,802 rows. For hyperparameter optimization grid search and random search techniques have been utilized. The performances of the proposed deep learning models were investigated error metrics, including Mean Absolute Errors (MAE) and the Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE) and R squared (R2). Bi-LSTM outperforms the other two algorithms, scoring 0.644, 0.388, 0.769 and 0.978 MAE, MAPE, RMSE and R2 values respectively. Such wind power forecasting helps energy planners and regional power providers to compute power production and energy generated from other sources.

Corporate farming model for sustainable supply chain crude palm oil of independent smallholder farmers

Independent oil palm smallholders in Indonesia contributed significantly to the sustainable of Crude Palm Oil (CPO) supply chain. On the other hand, sustainability in the process of implementing the supply chain system for independent smallholders is an issue that is extremely important for the success of the supply chain system for the CPO company. This study’s objective is to develop an alternative business model with the goal of strengthening the interaction that already exists between independent farmers and cooperatives in ensuring the existence of a supply chain for raw materials and having access to financial resources, knowledge, and technology. The selection of the research location considered several factors, including the size of the area’s oil palm plantations, the number of oil palm farmers who are self-sufficient, and the possibility of locating CPO plant. Study findings that, the new model by business canvas analysis as the upgraded model of existing model business recommended that it should be built based on the cooperative palm oil family which provides farmers with access to financial, replanting, intensification, and extensification and infrastructure by APKASINDO, BPDPKS, Bank, and other stakeholders to sustain the FFB supply chain in cooperation through a partnership program in a long-term scheme. This whole system was developed by corporate farming system based on the cooperative as the off taker of FFB farmers and develop mini plant CPO to produce cooking oil and biofuels and by product as the waste management system as the value added for farmers.

Analysis of co-operative irrigation farming and household food security in Africa: A PRISMA model approach

This study provides a comprehensive bibliometric analysis of the relationship between co-operative irrigation farming and household food security in Africa. The research aims to identify key studies, authors, and thematic clusters, analyse the geographical distribution of research efforts, and evaluate the impact of co-operative irrigation farming on food security indicators. Utilizing the PRISMA model, data were systematically gathered from peer-reviewed publications indexed in the Dimensions database, focusing on materials published between 2019 and 2023. The study employed both bibliometric and descriptive-quantitative methodologies to analyse the data. The results reveal a significant positive impact of co-operative irrigation farming on improving household food security across various regions in Africa. The findings highlight the need for targeted policy interventions and sustainable agricultural practices that support co-operative irrigation efforts. A growing scholarly attention toward co-operative irrigation farming and household food security in recent years reveals a potentiality in finding a sustainable solution to food insecurity challenge. These insights are essential for shaping future research and informing policies aimed at enhancing food security through co-operative farming models in the African context.

Study on the Design of Series-Type All-DC Wind Farms Based on Half-Bridge Voltage Balancing Circuits

Offshore wind farms connected in series, with each wind turbine connected in series with one another, enhance the coupling between them. Significant differences in wind speeds between neighboring DC wind turbines (DCWTs) might result in a substantial disparity in the output voltage, hence posing a risk of overvoltage. Nevertheless, implementing voltage-limiting configurations for DCWTs might lead to the dissipation of wind energy, thereby diminishing the wind farm’s capacity to deliver electricity. This work introduces a half-bridge voltage balancing circuit (HVBC) topology as a solution to the issue of DCWT output voltage changes affecting the stable operation of wind farms. The proposed HVBC topology is designed specifically for large-capacity series-connected all-DC wind farms where wind speed variations occur. This design achieves power decoupling for series-connected all-DC wind farms by providing current compensation to the series-connected DCWTs. A control strategy is devised by examining the decoupling principle and operational characteristics of the HVBC. A 60 kV/48 MW tandem-type all-DC wind farm model consisting of six DCWTs in series is built in Matlab/Simulink. The model is then simulated to evaluate its performance under conditions of unequal wind speed, rapid changes in wind speed, and wind turbine failure shutdown. This research verifies the feasibility of the HVBC topology and improves the stability of the series-type all-DC wind farm.

Carbon emission measurement and regional decomposition analysis of China’s beef cattle farming industry

IntroductionWarming caused by greenhouse gas (GHG) emissions has become a global environmental issue of widespread concern, and China, as a responsible power, has the pressing task of reducing carbon emissions. China is one of the world’s major beef producers and consumers, and at the same time, beef cattle, as a large livestock, is the largest source of GHG emissions in the livestock industry.MethodsThis study considered the panel data of 31 provinces in China from 2008 to 2022. The kernel density estimation and Dagum Gini coefficient were used to analyze the spatiotemporal dynamic evolution patterns and influencing factors of carbon emissions from China’s beef cattle farming industry.Results(1) The carbon emission trajectory of beef cattle production follows a distinctive “ascend-descend-ascend” three-phase pattern. By 2022, the sector’s cumulative carbon emissions had burgeoned by 37.62% relative to 2008, reflecting an average annual escalation of 2.31%. Despite the overall upward trend in carbon emissions, significant regional differences were observed. The Central Plains region has witnessed a consistent decline, in stark contrast to the Southwest and Northeast regions, which have emerged as hotspots for heightened carbon emissions and intensified emission densities within China’s beef cattle production landscape, underscoring the intensifying significance of carbon mitigation measures. (2) The kernel density curve shows an overall rightward shift with a specific gradient effect on carbon emissions. In addition, the range of the right drag of the curve in 2022 was significantly reduced, which laterally reflects the narrowing of the difference between the provinces with the highest and lowest carbon emissions from beef cattle farming. The principal source of variance in the overall carbon emissions from beef cattle production is the disparities between regions, which accounts for an average annual contribution rate of 52.52%. Conversely, the within-region contribution rates have remained relatively stable, while those for the intensity of transvariation have witnessed a substantial rise, with annual averages of 18.31 and 28.96%, respectively. (3) Regarding the factors influencing carbon emissions reduction, environmental regulations and production efficiency significantly drive carbon emissions reduction in beef cattle farming.DiscussionRelevant government departments should actively guide farmers toward green production, establish perfect policies and regulations for low-carbon beef cattle farming, and promote low-carbon beef cattle farming models based on local conditions.

Sustainable and Low-Input Techniques in Mediterranean Greenhouse Vegetable Production

In the modern agricultural landscape, numerous challenges, such as climate change, diminishing arable lands, and the reduction of water resources, represent significant threats. The Mediterranean greenhouse farming model relies on low-input strategies to maximize both yield and quality. Its protected horticulture is essential for the year-round cultivation of high-value crops, ensuring efficient and sustainable production. In the realm of future agricultural strategies, leveraging internet-based approaches emerges as a pivotal factor for real-time and remote control of various agricultural parameters crucial for crop growth and development. This approach has the potential to significantly optimize agronomic inputs, thereby enhancing the efficiency of targeted vegetable production. The aim of the present review is to underscore the challenges related to the intensive greenhouse production systems emphasizing various strategies leading to low-input greenhouse vegetable production. The goal is to promote more sustainable and resource-efficient approaches in the cultivation of greenhouse vegetables. This review highlights several key strategies for optimizing the greenhouse environment, including efficient water management through conservation tillage, drainage water reuse, and selecting the most appropriate irrigation systems and timing. Additionally, light modulation and temperature control—using solar energy for heating and pad-and-fan systems for cooling—are crucial for enhancing both crop performance and resource efficiency. The review also explores low-input agronomical strategies, such as pest and disease control—including solarization and optimized integrated pest management (IPM)—as well as fertilization and advanced growing techniques. These approaches are essential for sustainable greenhouse farming.

69 The Holos model for estimating greenhouse gases and soil carbon: Characterizing regionalized beef farm model systems

Abstract The objective of this work is to characterize regionally representative beef farm systems that represent dominant or typical surveyed management practices for 11 beef-producing regions across Canada. This work fulfills two further purposes 1) to improve and expand the Holos model interface; and 2) to facilitate the estimation of greenhouse gas (GHG) emissions and soil carbon (C) changes on beef farms in different regions of Canada. Holos version 4 is the whole-farm model of Agriculture and Agri-Food Canada’s to estimate GHG emissions and changes in soil C on Canadian farms in response to shifts in management practices. Holos can be implemented in all 10 Canadian provinces and accounts for GHG emissions from crop and livestock production [enteric and manure methane (CH4), manure and soil N2O emissions], farm machines and infrastructure [on-farm energy carbon dioxide (CO2) emissions], as well as from the upstream production of some farm inputs (synthetic fertilizer and pesticides). The model is designed to utilize data readily available on the farm to answer, ‘What if?’ scenarios, whereby the user can test the effect of changing management practices on their whole-farm GHG budget. To reduce the data input burden on the user, Holos V4 has built-in model livestock systems for beef, dairy, swine and poultry production that characterize the dominant features of these operations in Canada at the national scale based on relevant literature/data and expert opinion. Regarding beef production, we have characterized regionally specific model beef farms for incorporation into Holos, one for each of 11 Canadian beef-producing regions. General characteristics and management practices for each farm were based on the 2011 Beef Farm Survey (Sheppard et al., 2015), which summarizes management information from 1,009 Canadian beef farms, combined with data from the Canadian Cow-Calf Cost of Production Network (Canfax 2023). Each regional farm includes cow-calf, backgrounding in confinement, backgrounding on pasture and finishing components, and considers all the specific feed (e.g., forage, grains, by-products) required for each stage of the beef cycle. These 11 model farms are simulated within the current Holos V4 model to explore the impacts of variation in beef management practices on farm GHG emissions across Canada and on soil C stocks on lands used to produce feed and graze cattle. An overview of the national-level dairy, swine and poultry components in Holos will be presented along with a more detailed perspective of whole-farm GHG budget and multi-decadal soil C dynamics in regionalized beef farms. The impact of management and environmental factors that lead to differences in GHG emissions and soil C stocks in beef farms will also be explored.

Applying equineRSU and seasonal livestock correction to wider equine stud farm types

ABSTRACT To adjust for potential overestimations in the nitrogen excretion by horses within Overseer®, a revised stock unit system has been proposed (equineRSU). These equineRSU were generated and validated using a medium-sized equine farm as a model farm. The aim of this study was to test the application of the equineRSU and seasonal stock number adjustment on a more complex farming model. Livestock numbers and management data were captured prospectively for the base property (128.8 ha) of a large multi-property commercial breeding stud (3 support properties, 556 ha total area) between June 2022 and May 2023. The monthly on-farm metabolisable energy (ME) requirement and pasture demand were deterministically modelled with both a customised feed budget using livestock class and weight or using the equineRSU and monthly adjusted stock numbers. There were multiple complex movements of horses on and off the property in the different stock classes across the year, with stock management reflecting seasonal periods of high stocking density. There was good agreement (6% variance) between actual ME demand (complex feed budget) and estimated feed demand (simplistic equineRSU model) of 4,387,187 MJ vs 4,102,770 MJ. This suggests that the equineRSU could be used on complex equine farm systems within Overseer®.

Multi-objective optimal coordination of electric vehicle charging, power grid, energy storages and renewables

Considering that the grid connection of variable renewable energies (VREs) and the disorderly charging loads of large-scale electric vehicles (EVs) will adversely affect the power grid stability, the optimization strategy of EV charging and grid-connected scheduling are investigated, in which energy storage system is added to balance the demand and supply of the power grid. First of all, considering the profit of EV charging station, the charging cost of EV users and power loss, a multi-objective optimal scheduling model of EV charging, power grid, pumped hydro-storage (PHS) and wind farm is constructed, which is improved from the aspect of wind farm wake. Then, the multi-objective optimization algorithm based on genetic algorithm is used to solve the model to realize the optimal charging of EVs. Finally, the IEEE-13 power grid is used to verify the effectiveness of the proposed multi-objective optimal scheduling model. Combined with a specific example for simulation analysis, the results show that the model can achieve orderly charging of EVs, ensuring the safety of the power grid and promote the use of VREs. In addition, the optimization algorithm can further improve the profit, and reduce the charging cost and power loss.

Wind farm power optimization using system identification

The wake effect reduces the total power production of wind farms. This paper presents a method for wind farm power optimization through wake effect reduction. The proposed method optimizes the yaw angle offsets and de-rating settings of all turbines to maximize total power generation. The optimization approach is gradient-based, with gradients at each iteration obtained through system identification using field test data, eliminating the need for physical models. In system identification, test signal design, model estimation and model validation problems are solved in a systematic manner; in the gradient-based optimization, in order to achieve fast convergence, methods for initial value and initial step-size determination, variable step-size iteration and iteration termination are developed. The method is verified using the FLORIS wind farm model developed by National Renewable Energy Laboratory (NREL), USA. The studied wind farm consists of 80 wind turbines configured similarly to the Horns Rev I offshore wind farm in Denmark. The result of the developed optimization method is highly consistent with those obtained using FLORIS's built-in optimization tool.

Effects of genetic and environmental trends from 1970 to 2020 on farm efficiency estimated with a whole farm modeling system

The objectives of this study were to evaluate associations of genetic, cow management and nutrition, inbreeding, and crop yields from 1970 to 2020 with measures of production and economic efficiency according to a whole farm model, and to evaluate effects of genetic change in individual traits on economic efficiency in comparison to expectations from economic selection indexes. Genetic and phenotypic performance metrics for Holsteins from 1970 and 2020 were retrieved and input into the Integrated Farm System Model (IFSM) for a 7000 cow TX herd and a 50 cow PA grazing herd. Crop yields estimates from 1970 and 2020 were retrieved and farm hectarage was altered so that forage and energy concentrate requirements were met through farm production; likewise, scenarios evaluating effects of atmospheric CO2 fertilization (CO2F) on crop yield were evaluated by altering farm hectarage. For single traits that could be dynamically modeled by IFSM, performance shifts and resulting change in product prices or management expenses were added to 1970 base models. Economic efficiency was evaluated as the per cow return to management and unpaid factors as compared with 1970 base models. As averaged across state scenarios, gains in economic efficiency were +$945 and -$76 for additive genetic and inbreeding effects, respectively, for a total gain from genetic change of +$869. Genetic gain in fat yield (+$549) and protein yield (+$524) were responsible for most of the genetic gain, whereas milk yield (-$128) and increased cow BW (-$129) depressed economic efficiency. Genetic change in productive life had a smaller effect (+$44) than predicted unless heifers were purchased and at double the default value. Gains due to cow management and nutrition increased efficiency by +$666 and crop yield increased efficiency by +$711, of which +$371 was attributed to CO2F across scenarios. Whole farm dry matter efficiencies derived as the ratio of fat and protein corrected milk yield to dry matter intake increased from 0.82 (PA) and 0.97 (TX) in 1970 to 1.20 in 2020 and could be higher if farms reduce the size of their replacement herd by producing beef calves. The landmass required in 2020 was 63% and 78% of the 1970 requirement for TX and PA, respectively. Changes in cow genetic merit, management and nutrition, and crop yields have all increased the economic and environmental sustainability of milk production, and systems such as IFSM could be a useful tool to help inform economic selection indices.

Predicting dryland winter wheat yield in cold regions of Iran

AbstractWheat is a crucial staple worldwide, serving both human and animal needs. In Iran, where climate conditions vary widely, wheat farming faces significant challenges, especially in areas facing freezing winters and unfavorable temperatures during reproductive stages. Unfortunately, existing models often fail to account these extreme and specific climate conditions, leading to inaccurate predictions, notably in cold areas. To address this issue, wheat dryland farming (WDF) model was evaluated in predicting dryland winter wheat yields in five distinct areas including Shahrekord, Borujen, Koohrang, Farsan, Lordegan, and Ardal in Chahar‐Mahal and Bakhtiari province, Iran. The results showed that changes in precipitation and temperature significantly impacted dryland wheat production. While higher precipitation generally associates with higher yields, this relationship is not always straightforward due to factors like unfavorable precipitation patterns and types (i.e., rainfall or snow). Likewise, unfavorable temperatures, particularly during crucial growth stages and winter freezes, pose significant challenges to wheat growth and yield modeling. The WDF model's performance was evaluated across various temperature conditions in the study area, and it was more accurate in regions with certain minimum and maximum temperature values above thresholds. However, the model performance was poor in colder areas, where freezing temperatures were occurred in winter duration (Shahrekord, Borujen, Koohrang, and Farsan). In order to improve the model's accuracy, a correction factor based on the minimum and maximum air temperatures was incorporated in the model. The findings emphasized the importance of considering both precipitation and temperature dynamics when modeling winter wheat yields, especially in regions with diverse climates. By refining models like WDF, agricultural planners can better forecast the yield fluctuations and address the impacts of climate variability on food security in Iran and similar regions worldwide.

Smart agriculture model in detecting oil palm plantation diseases using a convolution neural network

Planning models for sustainable crop care in the context of smart agriculture are complex issues as they involve many factors such as productivity, quality, growth sustainability, workforce use, and information technology use. In this study, we will create an optimized model using a convolution neural network (CNN) that can classify and monitor plant diseases. Part of the plant care system is to be aware of plant diseases and to be able to deal with them immediately. This study aims to acquire a new smart farming model for integrated crop care. The results of this research are findings in the form of a CNN model for classifying plant diseases detected from the leaves of the plants studied in oil palm. Testing using Google Colab obtains 100% accuracy and 99% accuracy using a teachable machine. The contributions of this paper create a new model in the field of informatics, especially in the field of intelligent agriculture based on information technology.

End-users’ acceptance of ’X as a Service’: Evidence from agriculture 4.0

With the rapid advancement of information technology, a new service model known as X as a Service has emerged. X can represent diverse resources such as platforms, infrastructure, farming, mobility, security, and more, allowing users to meet their needs flexibly and cost-effectively. The successful application of X as a Service heavily depends on end-users’ acceptance. This study explored the motivation factors for X as a Service based on the evidence from adopting Farming as a Service in Agriculture 4.0 among farmers in Northeastern China. We provided a theoretical framework for Farming as a Service adoption behavior, covering factors including personalization, perceived enjoyment, functionality, perceived risk, financial consequences, and perceived network externality. The effectiveness of the research model was assessed and validated through a two-stage procedural approach, utilizing partial least squares structural equation modeling. Results revealed that our proposed acceptance model for Farming as a Service exhibited a good model fit, accounting for 84.4 % of the variance in adoption intentions. Research findings highlighted that perceived network externality and functionality were the most influential factors in determining users’ adoption intentions for Farming as a Service. Conversely, perceived risk emerged as a significant negative factor influencing adoption. Furthermore, financial consequences, perceived enjoyment, and personalization also played crucial roles as determinants of user adoption. These findings offered valuable insights for service providers to improve their products, services, and marketing strategies.