Agricultural Planning Research Articles

Advanced reference crop evapotranspiration prediction: a novel framework combining neural nets, bee optimization algorithm, and mode decomposition

AbstractVarious critical applications, spanning from watershed management to agricultural planning and ecological sustainability, hinge upon the accurate prediction of reference evapotranspiration (ETo). In this context, our study aimed to enhance the accuracy of ETo prediction models by combining a variety of signal decomposition techniques with an Artificial Bee Colony (ABC)–artificial neural network (ANN) (codename: ABC–ANN). To this end, historical (1979–2014) daily climate variables, including maximum temperature, minimum temperature, mean temperature, wind speed, relative humidity, solar radiation, and precipitation from four arid and semi-arid regions in Egypt: Al-Qalyubiyah, Cairo, Damietta, and Port Said, were used. Six techniques, namely, Empirical Mode Decomposition, Variational Mode Decomposition, Ensemble Empirical Mode Decomposition, Local Mean Decomposition, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, and Empirical Wavelet Transform were used to evaluate signal decomposition efficiency in ETo prediction. Our results showed that the highest ETo prediction accuracy was obtained with ABC-ANN (Train R2: 0.990 and Test R2: 0.989), (Train R2: 0.986 and Test R2: 0.986), (Train R2: 0.991 and Test R2: 0.989) and (Train R2: 0.988 and Test R2: 0.987) for Al-Qalyubiyah, Cairo, Damietta, and Port Said, respectively. The impressive results of our hybrid model attest to its importance as a powerful tool for tackling the problems associated with ETo prediction.

Improving Rainfall Prediction Accuracy in the USA Using Advanced Machine Learning Techniques

The key aim of this research project is to design and evaluate advanced machine learning models for increasing accuracy in rainfall forecasting over the USA. We intended to investigate nonlinear relationships typical of the atmospheric variables using state-of-the-art ML methods for more accurate rainfall predictions. For this research project on rainfall forecasting in the USA, we utilized an extensive dataset that comprises historical rainfall data collected from the National Oceanic and Atmospheric Administration (NOAA) and other meteorological agencies. The main dataset we use in this paper consists of daily rainfall measurements across various geographical locations of the USA, thus capturing the wide-ranging historical data necessary for both training and validation of the model. Besides measuring rainfall, we included meteorological data from sources such as NOAA's Global Historical Climatology Network and NASA's Modern-Era Retrospective Analysis for Research and Applications. These datasets further provided key variables that are known to affect rain, including temperature, humidity, wind speed, and atmospheric pressure. The performance metrics used in this research work for the models considered include accuracy, precision, recall, and F1 score. The above table shows that the Random Forest Classifier outperformed the other models, achieving perfect accuracy. That indicated that it rightly classified all the instances in the test set. The Logistic Regression and Support Vector Machine models gave a quite good performance by giving above average accuracy but had lower precision and recall for the rainfall prediction. Accurate rainfall forecasting has direct consequences on agriculture, greatly empowering farmers and agricultural planners to make more effective decisions regarding planting, harvesting, and crop management. The forecasts of rainfall are also of critical importance in disaster management regarding planning for flood emergencies. Moreover, precise forecasting of rainfall, particularly in sustainable water resources management, presents the most important data in planning for and conserving these resources.

Assessing the Spatial Distribution, Condition, and Intensity of Soil Acidity for Informed Management Decisions in the Semen Aari District of Ari Zone, Southwestern Ethiopia

Soil acidity poses a significant challenge to agricultural productivity in Ethiopia's highlands, particularly affecting the Semen Ari district in the Ari zone. The common practice of applying agricultural lime to mitigate soil acidity is hampered by a lack of detailed information on the extent, severity, and spatial distribution of acidic soils. This study aims to determine how soil acidity varies spatially by identifying and mapping the specific geographic patterns of soil acidity levels in the Semen Ari district. Seventy-one composite soil samples from the 0–20 cm layer were geo-referenced and analyzed. Using statistical analysis and ArcGIS software for spatial interpolation through ordinary kriging, soil pH ranged from 3.29 to 5.68, classifying 99% of the soils as strongly acidic. The root mean squared error (RMSE) of the interpolation was 0.30. Soil pH showed a significant negative correlation with exchangeable acidity but a non-significant negative correlation with organic carbon and total nitrogen. The results highlight the need for targeted soil management strategies, such as appropriate lime application rates and the cultivation of acid-tolerant crops, to enhance crop yields. Further research is recommended to include comprehensive soil property datasets to better understand the factors influencing soil pH variability, thus supporting more precise management of acidic soils in the region. The generated high-resolution soil acidity map serves as a valuable tool for agricultural planning and decision-making.

Topography Dominates the Spatial and Temporal Variability of Soil Bulk Density in Typical Arid Zones

Soil bulk density is a crucial indicator for assessing soil matter storage and soil quality. Due to the complexity of sampling soil bulk density, particularly in deeper layers, it is essential to study the spatial distribution patterns of soil bulk density and their influencing factors. To address the gap in large-scale studies of vertical (from surface to deeper layers) and horizontal (across a broad area) variations in soil bulk density in arid regions, this study focuses on Changji Prefecture, located in the central northern slope of the Tianshan Mountains and characterized by typical vertical zonation. By integrating classical statistics, geostatistics, and geographic information systems (GISs), this study investigates the spatial distribution patterns and driving factors of soil bulk density. The results indicate that soil bulk density in Changji Prefecture increases with soil depth, with significantly lower values in the surface layer than in deeper layers. Spatially, despite minimal variation in latitude, there is considerable elevation difference within the study area, with the lowest elevations in the central region. Soil bulk density exhibits a spatial distribution pattern of higher values in the northeast (desert areas) and lower values in the southwest (forest areas). The nugget effect in the surface layer (0–20 cm) is substantial at 44.9%, while the deeper layers (20–100 cm) show nugget effects below 25%, suggesting that the influence of both natural and anthropogenic factors on deep soil bulk density is limited and mainly affects the surface layer. Stepwise regression analysis indicates that among topographic factors, slope and elevation are the primary controls of spatial variability in soil bulk density across layers. This research demonstrates that, in arid regions, soil bulk density is influenced primarily by natural factors, with limited impact from human activities. These findings provide valuable data support and theoretical guidance for soil management, agricultural planning, and sustainable ecosystem development in arid regions.

Changing Rainfall Patterns in the Northeastern South Kivu Region, Democratic Republic of the Congo: A Detailed Analysis Using CHIRPS Rainfall Data (1981–2023)

AbstractUnderstanding changes in rainfall patterns is vital for effective water resource management and agricultural planning in climate-sensitive regions. In Northeastern South Kivu, Democratic Republic of the Congo, limited knowledge of rainfall dynamics poses challenges for local climate assessments. This study analyzed rainfall patterns using the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) dataset from 1981 to 2023 to fill this knowledge gap. The findings revealed significant spatial and temporal changes in rainfall across the regions of Kabare, Bukavu, Kalehe, and Idjwi. Mean annual rainfall ranged from 1136.92 mm to 2048.85 mm, with coefficients of variation between 8.23% and 8.45%. Monthly rainfall fluctuations were substantial, ranging from 16 to 56.35%. Standardized rainfall anomalies indicated a shift from predominantly wet conditions in the 1980s to drier conditions in later decades, with over half the years from 1990 to 2023 recording below-average rainfall. Mann-Kendall trend analysis, supported by Innovative Trend Analysis (ITA), confirmed significant decreases in annual rainfall, with rates between − 3.53 to -5.72 mm/year. Additionally, rainfall intensity indices highlighted a decline in total annual precipitation, suggested fewer extreme events. Spatial variability was observed, with regions like Kalehe and Bukavu showed higher values for indices such as R95P and RX1day compared to Kabare and Idjwi. These results underscored the urgent need for adaptive strategies to mitigate water scarcity and manage changing rainfall patterns in the region.

Investigation into the Geological Origin and Influencing Factors of Selenium-Enriched Soil in Licheng, Jinan, Shandong Province

The geochemical classification of soils is crucial for the scientific development and utilization of land, particularly in the investigation of selenium-rich soils, which plays a guiding role in the cultivation layout of local selenium-rich agricultural products. This study involves the collection and analysis of over 6000 samples of soil, water quality, and crops from the entire area of Licheng District in Jinan City, Shandong Province. By analyzing the data in conjunction with the geochemical classification standards for soil nutrient levels, soil environmental geochemical levels, and comprehensive soil quality geochemical levels, we delineated a distribution area of selenium-rich soils covering 192.26 km2 in Licheng District, providing a bibliographic basis for the scientific planning of agriculture and forestry in the region.

Urban agriculture and urban planning: a case of Bangalore between the 1950s and the 1970s

With urbanisation moving at a faster pace than ever before, discussions on Urban Agriculture (UA) and its role in sustainable development have taken centre stage. The implementation of UA has its challenges, one of them being how urban development frameworks view it. This paper examines the disappearance of a historic UA landscape in Bangalore, India between the 1950s and the 1970s, a period of post-independence urban planning. Using historical records including urban planning documents, government files, and oral histories that I recorded with a traditional community of UA practitioners—the Vahnikula Kshatriya—in Bangalore on their memories of the 1950s–1970s, I argue that colonial frameworks in post-independence urban development plans not only displaced UA but also continues to affect it. This paper highlights the urgent need to change how urban planning in formerly colonised places approaches UA.

Study of Machine Learning Techniques for the Estimation of Soil Moisture in Agriculture

Soil moisture is crucial in various fields and monitoring it to guide irrigation is challenging. Machine learning has emerged as a promising tool to predict soil moisture levels accurately. This study evaluates machine learning techniques for this task, training models with meteorological variables and direct soil moisture measurements. Four machine learning algorithms were implemented, highlighting the Gradient Boosting Regressor as the most effective. In addition, a processed data set that combines meteorological and soil moisture measurements is presented, hoping it will be helpful for future research. This approach seeks to improve the compression and predictability of soil moisture, which is crucial for agricultural planning and water management in agriculture

The LTAR Cropland Common Experiment at Lower Chesapeake Bay.

The Lower Chesapeake Bay (LCB) Long-Term Agroecosystem Research (LTAR) Common Experiment (CE) located in Beltsville, MD, focuses on research of concern to producers of the major regional crops, which are corn (Zea mays L.), soybean [Glycine max (L.) Merr.], wheat (Triticum aestivum L.), and various forage species. Livestock production in the region includes broiler and laying chickens (Gallus gallus domesticus L.) and dairy and beef cattle (Bos taurus L.). The LCB region is among the most heavily populated in the United States. Urban development pressure is high for both farms and natural areas. The need to restore Chesapeake Bay water quality is a major influence on regional agricultural practices. Conservation practices such as cover cropping, no-till agriculture, and nutrient management planning are more common in the region compared to nationally. However, farmers still face management challenges implementing practices that address water quality and the rise of herbicide-resistant weeds. Researchers at the LCB site recognize the need to protect the Chesapeake and Delaware Bays and maintain farmer profitability. The LCB CE compares a 3-year crop rotation system featuring alternative crop management (cover crop intensification, crop rotation diversification, and integrated weed management [IWM]) with a prevailing 2-year system (no cover crops and no IWM), both under continuous no-tillage, to identify the optimal balance to promote the sustainability of regional cropping systems. The LTAR LCB site provides data-driven tools and solutions to support farmers in the mid-Atlantic region.

Assessment of Soil Moisture through Climatic Variables Using Remote Sensing Data in Semi-Arid Sokoto State, Nigeria

Soil moisture availability is an indispensable requirement for not only the growth of crops but also other living organisms in any ecological settings This study determined soil moisture condition in Sokoto State using climate-based water index. Satellite-based estimations of mean monthly rainfall data was obtained from the USGS/FEWS database for each of the local government areas. Also, approximations of monthly values of minimum and maximum air temperatures (AIRS) were downloaded from the NASA website. The latter variables were utilised in the computation of potential evapotranspiration using Hargreaves equation. It was very clear from the results that water surplus occurred in the months of July, August and September across almost all the locations. In addition, places lying in the southern part of the State such as Kebbe, Yabo, Tambuwal and Shagari recorded higher positive values of moisture availability in contrast to their counter parts in the extreme north bordering Niger republic. Thus, this study revealed slight spatio-temporal variation in soil moisture condition across the entire study area decreasing from the southern locations towards the north. Hence, the research could help improve agricultural planning and suggest irrigation during dry periods. Consequently, the findings could offer useful insights for managing water resources and improving farming in dry areas.

Integrated Assessment and Agricultural Planning in Selenium-Rich Hilly Soils: A Case Study on Land Use, Heavy Metal Contamination, and Nutrient Element Distribution

The strategic development of selenium-enriched soil cultivation is essential for effective agricultural land management. This research explores the sustainable utilization of selenium-rich soils in Qiongzhong County, Hainan Island. An extensive evaluation was conducted on 7266 surface soil samples, assessing the selenium concentrations, nutrient levels, heavy metals, pH values, and soil organic matter (SOM). In addition, analyses of 70 samples for organochlorine compounds and PAHs were performed. The results indicated average selenium content of 0.46 mg/kg, with 55.2% of the samples surpassing the selenium enrichment threshold of 0.4 mg/kg. According to the GB15618-2018 standards, 127 samples (1.75%) showed medium or high chromium-associated risks. No contamination from organochlorine compounds or PAHs was found, including the 16 priority-controlled PAHs with an average concentration of 30.3 µg/kg, confirming the soil’s high quality. The correlation and factor analysis identified surface enrichment as the main factor influencing selenium accumulation, presenting minimal environmental risks. Consequently, three categories of selenium-rich soil were defined: selenium-rich, pollution-free, and high-nutrient selenium-rich soil. Recommendations based on the soil characteristics and existing agricultural practices were made for the cultivation of selenium-rich vegetables, rubber, and nuts. This study lays a foundation for the sustainable management of selenium-rich soils, providing insights for further research and decision-making to optimize these resources, thus promoting environmental protection and agricultural sustainability.

PENGARUH MODAL SOSIAL TERHADAP KAPASITAS MANAJERIAL PETANI JAGUNG DI DESA UMBA KECAMATAN NAPANO KUSAMBI KABUPATEN MUNA BARAT

This study aims to ascertain the social capital and managerial capacity of farmers, as well as to determine the influence of the social model on the managerial capacity of corn farmers in Umba Village, Napano Kusambi Subdistrict, West Muna Regency. The research population comprises all individuals engaged in the cultivation of corn in Umba Village, amounting to a total of 32 individuals. The sample was selected using the census method, resulting in a sample size of 32 respondents for this study. Data were collected through observation, surveys, and structured interviews, with the use of a questionnaire. This study employed a quantitative approach. The variables under investigation are divided into two categories: social capital and managerial capacity among farmers. The data were subjected to two distinct forms of analysis. Initially, a quantitative descriptive analysis was conducted utilising the class interval formula to delineate the prevailing conditions pertaining to social capital and managerial capacity among maize farmers. Subsequently, a multiple linear regression analysis was employed to ascertain the influence of social capital on the managerial capacity of maize farmers. The multiple linear regression analysis is comprised of three tests: the T-test, F-test, and determination test. The findings indicate that the utilization of social capital among corn farmers in Umba Village is classified as favorable. This is evidenced by the presence of a robust level of trust, a commendable application of norms, and a relatively strong network of social connections. The managerial capacity of maize farmers is categorized as good, as evidenced by their effective maize farm planning and organization. However, there are areas for improvement, particularly in the dimensions of maize farming implementation and supervision, which remain in the poor category. Social capital has a significant positive effect on the managerial capacity of maize farmers, amounting to 29.1%, with 70.9% influenced by other factors.

Comparative Analysis of Machine Learning Models for Weather Forecasting: A Heathrow Case Study

Accurate weather forecasting, especially temperature prediction, is fundamental for various segments within the UK, including agriculture, energy, and policy planning, as the nation adapts to the effects of climate change. This study addresses the limitations of conventional linear models in capturing the complex, non-linear relationships within meteorological data by comparing the effectiveness of different Machine Learning (ML) strategies. This study evaluates the performance of baseline ML models, such as Linear Regression (LR), Support Vector Regression (SVR), and K-Nearest Neighbors (KNN). It also examines advanced ensemble and boosting models such as Decision Tree (DT), Random Forest (RF), XGBoost (XGB), LightGBM (LGBM), and CatBoost, using a comprehensive dataset from Heathrow Airport. Detailed preprocessing, model training, and optimization through cross-validation were conducted, with performance assessed using Mean Squared Error (MSE) and Coefficient of Determination (R²) metrics. The results demonstrate that ensemble methods, particularly XGB and LGBM, offer superior predictive accuracy for weather forecasting tasks, highlighting their potential to enhance predictive models in meteorological applications.

A robust multi-model framework for groundwater level prediction: The BFSA-MVMD-GRU-RVM model

Groundwater level prediction is critical for environmental protection and agricultural planning. Accurate predictions help manage risks associated with excessive groundwater extraction and land subsidence. This study introduces a novel model combining multivariate variational mode decomposition (MVMD), gated recurrent unit (GRU), and relevance vector machine (RVM), along with the Boruta feature selection algorithm (BFSA), for precise groundwater level forecasting. The model operates in several stages. First, BFSA selects the most informative features as input data. Next, the MVMD method decomposes the time series of these features. The GRU model then extracts crucial information from these decompositions. Finally, the extracted data is fed into the RVM model to predict groundwater levels one month ahead in Iran's Bastam Plain. To assess the model's accuracy, multiple error indices were employed. The MVMD-GRU-RVM model outperformed other models, improving the testing Nash–Sutcliffe Efficiency and mean absolute error by 24–31 % and 6–61 %, respectively. Additionally, it enhanced R2 values of the MVMD-CNN, MVMD-RNN, MVMD-MLP, and MVMD-RBFNN models by 1.0–3.33 %. These results demonstrate that the MVMD-GRU-RVM model significantly improves groundwater level prediction accuracy. The key points of the paper are the successful effectiveness of MVMD in processing non-stationary time series and improving the predictive performance of the models, as well as the high ability of the GRU model in data feature extraction. The study also shows that the novel model can provide outputs with lower uncertainty.

Enhancing spatial resolution of satellite soil moisture data through stacking ensemble learning techniques

Soil moisture (SM) is a critical variable influencing various environmental processes, but traditional microwave sensors often lack the spatial resolution needed for local-scale studies. This study develops a novel stacking ensemble learning framework to enhance the spatial resolution of satellite-derived SM data to 1 km in the Urmia basin, a region facing significant water scarcity. We integrated in-situ SM measurements (obtained using time-domain reflectometry [TDR]), Soil Moisture Active Passive (SMAP) and Advanced Microwave Scanning Radiometer 2 (AMSR2) SM products, Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature and vegetation indices, precipitation records, and topography data. Ten base machine-learning models were evaluated using the Complex Proportional Assessment (COPRAS) method, and the top-performing models were selected as base learners for the stacking ensemble. The ensemble model, incorporating Random Forest, Gradient Boosting, and XGBoost, significantly improved SM estimation accuracy and resolution compared to individual models. The XGBoost and Gradient Boosting meta-models achieved the highest accuracy, with an unbiased root mean square error (ubRMSE) of 1.23% m3/m3 and a coefficient of determination (R2) of 0.97 during testing, demonstrating the exceptional predictive capabilities of our approach. SHapley Additive exPlanations (SHAP) analysis revealed the influence of each base model on the ensemble’s predictions, highlighting the synergistic benefits of combining diverse models. This study establishes new benchmarks for soil moisture monitoring by showcasing the potential of ensemble learning to improve the spatial resolution and accuracy of satellite-derived SM data, providing crucial insights for environmental science and agricultural planning, particularly in water-stressed regions.

Estimation of Rice Crop Acreage in Kuttanad Region, Kerala Using Landsat 8 OLI IMAGES and GIS Techniques

Aims: The study aimed to delineate rice accurately (Oryza sativa L.) cultivation areas in the Kuttanad region, Kerala, during the Puncha season of 2023-24 using medium- to high-resolution optical satellite data, particularly Landsat 8 Operational Land Imager (OLI), to aid in preharvest prediction of agricultural production and policy making. Study Design: This study used a remote sensing-based approach for rice area estimation, focusing on supervised classification methods. Place and Duration of Study: The study was conducted in Kuttanad, Kerala, a low-lying agroecosystem, during the Puncha season of 2023-24. Methodology: The study utilised two cloud-free Landsat 8 OLI images to delineate rice-growing areas. The images were pre-processed, mosaicked, and analysed using ArcGIS software. A supervised classification approach was employed using the Maximum Likelihood Classification algorithm. The study area was classified into five categories: rice fields, other crops, low vegetation, built-up areas, and water bodies. Ground-truth data was used to validate the classification accuracy. Results: The total rice area delineated during the Puncha season was 43,550.28 hectares. The classification achieved an accuracy of 93.33%, with a kappa coefficient of 0.87, indicating high reliability. Conclusion: The accurate delineation of rice-growing areas using satellite imagery provides valuable information for assessing production levels and planning for food security. This methodology can aid in agricultural planning and contingency strategies, particularly in regions like Kuttanad, which face challenges such as flooding and soil toxicity.

Application of meta-heuristic hybrid models in estimating the average air temperature of Caspian sea coast of Iran

The rise of industrial societies leads to higher greenhouse gas emissions, profoundly affecting the climate in coastal regions. Consequently, air temperature readings from standard meteorological stations are key indicators of the Earth's environmental condition. Therefore, accurate estimation of daily temperature in each region is one of the important prerequisites for agricultural planning as well as water resources management and drought prevention, which can be done in different ways such as experimental, semi-experimental and intelligent models. In this research, WSVR, AIG-SVR, GWO-SVR and BAT-SVR hybrid models were investigated and evaluated in order to estimate the average daily air temperature on the shores of the Caspian Sea located in the north of Iran. For modeling, weather station data from Babolsar meteorological station located in Mazandaran province were used. During the water year from 2012 to 2022, daily parameters including relative humidity, maximum temperature, minimum temperature, wind speed, and evaporation were selected as network inputs, with the average daily air temperature as the network output. To assess and compare model performances, several criteria were employed including correlation coefficient, root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe efficiency (NSE), and percentage bias. Comparative analysis revealed that the WSVR model surpassed other models, demonstrating the highest correlation coefficient (0.992), lowest RMSE (0.096), and lowest MAE (0.042). The highest Nash Sutcliffe criterion (0.996) and bias percentage (0.001) were prioritized in the validation stage.

Preference for water sources and agricultural utilisation among rural households: a case of Akoko district of Ondo State, Nigeria

The increasing challenge of freshwater scarcity amid rising demand and diminishing availability has led to a critical issue threatening sustainable development, especially in rural areas of developing countries like Nigeria. Despite the abundance of studies on rural water use, gaps remain in understanding the interplay between socio-economic factors, and agricultural practices in shaping these preferences and utilisation. Thus, the study investigates the preference for water sources and their utilisation in agricultural activities among rural households in the Akoko district of Ondo State, Nigeria. Cross-sectional data from randomly sampled 240 rural households were collected with the aid of a questionnaire, and employed for the data analysis. Descriptive statistics, logit, and multinomial regressions were used for the data analysis. The findings revealed a dominant preference for rainwater and well water. Factors such as access to safe water sources, time spent, extension service, technology adoption, soil type, experience, and proximity to water sources significantly influence the choice of water source utilization in the area using multinomial logit regression. Again, the results from logit regression revealed that water source, technology adoption, soil type, fish farming, crop farming, farming experience, and distance to water sources significantly affect the use of water for agricultural purposes. Therefore, the policy should enhance water access, consider soil characteristics in agricultural planning, and facilitate knowledge transfer among farmers for optimal water use, recognizing the symbiotic relationship between water access and agricultural sustainability.

Harnessing artificial intelligence for sustainable development in emerging markets: Exploring opportunities and challenges in Thailand

The current study investigates how artificial intelligence (AI) impacts sustainable development in emerging markets, with a focus on Thailand. Following a systematic review approach, research designs are configured for reviewing empirical evidence within peer-reviewed papers and reports. It also presents an overview of the adoption of AI in agriculture, health, and urban planning. The key findings lend credence to the potential use of AI in achieving resource optimality, reducing environmental damage, and urging social equity in its use. But these very initiatives are hampered by the digital divide, concerns about data privacy, and bias in algorithms. The way forward should be to establish solid, regulatory frameworks geared towards more investment in infrastructure with ethical AI practices that will lead to optimal gains. The huge potentials for AI to enable sustainability in Thailand are there; hence, it is very important to reduce the associated risks that require equitably distributed results in all sectors.

Decision-Making Model to Support Agricultural Policies in Realizing Economic and Social Sustainability

Abstract Background Achieving economic and social sustainability is the goal of any policy when defining measures. We focus on the beef sector, where many challenges have arisen due to its structural characteristics, such as an unfavourable scale structure, high costs, low efficiency, and a low environmental footprint. This paper presents an example of the support provided by a mathematical programming model in the development of a Common Agricultural Policy Strategic Plan for the period 2023-2027. Methods/approach It is a model based on linear programming that allows such an ex-ante analysis by calculating production plans at the farm level and aggregating the results at the sector level. Objectives When defining the interventions, the question arose as to what the reform of the Common Agricultural Policy will bring and to what extent the sector should be supported in meeting these challenges. These were the concerns of agricultural policy that we sought to support by modelling different scenarios. Results The results show that the situation of the sector will worsen, especially for larger farms, but they also show the great importance of production-related payments to mitigate the negative trend. Conclusions The applied approach proves to be suitable for supporting the design of agricultural policy and achieving greater economic and social sustainability in the sector.