Agricultural Planning Research Articles

Geo-Spatial Investigation of Climate Change Impacts on Irrigation Demand of Dry Season Boro Rice in Northwest Bangladesh

The present study is an attempt to assess the irrigation demand of the dry season Boro rice in the northwest regions of Bangladesh using climatic, soil and crop data. The FAO-56 Penman–Monteith method was applied to calculate reference evapotranspiration and irrigation demand of Boro rice has been estimated using CROPWAT model. Both the observed and downscaled Representative Concentration Pathway (RCP) climatic data were used in the study. The result reveals that there is no significant change in irrigation demand of Boro rice from 1975 to present. However, the demand will increase significantly in future. The study projects that future irrigation demand of Boro rice in climate change situation for RCP 8.5 will be increased by 180-220 mm in 2075. It is expected that the findings will contribute in devising appropriate agricultural and irrigation plans for the region. The Chittagong Univ. J. B. Sci.,Vol. 10(1 &2):217-235, 2020

Geospatial insights into Alphonso mango cultivation: a comprehensive land suitability study in the coastal belt of Maharashtra, India.

Land suitability assessment is integral to the advancement of precision agriculture. This inquiry is focused on identifying optimal regions for cultivating Alphonso mango in the coastal belt of Maharashtra, spanning across Palghar, Raigad, Thane, Ratnagiri, and Sindhudurg districts. Employing a GIS-based Analytic Hierarchy Process (AHP) methodology, 10 crucial parameters have been considered, encompassing climatic, physical, and chemical soil characteristics: cation exchange capacity, organic carbon, slope, rainfall, soil pH, soil texture, mean annual soil temperature, base saturation, soil drainage, and soil depth. Weights are assigned to these parameters based on expert opinions and existing literature to determine their significance in developing a soil suitability map. The study reveals distinct land suitability zones for Alphonso mango cultivation. The land suitability map designates 25.78% of the study area as highly suitable, while 9.18% is considered unsuitable for Alphonso mango cultivation. To validate the study, the Receiver Operating Characteristic (ROC) curve has been employed, indicating an 83% approval rate for the reliability and performance of the soil suitability. The results categorise soil suitability classes, providing valuable insights for farmers and agricultural planners to make informed decisions regarding Alphonso mango cultivation in similar geoenvironmental regions.

Improved remote sensing reference evapotranspiration estimation using simple satellite data and machine learning

Reference evapotranspiration (ET0) estimation is crucial for efficient irrigation planning, optimized water management and ecosystem modeling, yet it presents significant challenges, particularly when meteorological data availability is limited. This study utilized remote sensing data of land surface temperature (LST), day of year, and latitude, and employed a machine learning approach (i.e., random forest) to develop an improved remote sensing ET0 model. The model performed excellently in 567 meteorological stations in China with an R2 of 0.97, RMSE of 0.40, MBE of 0.00, and MAPE of 0.11 compared to the FAO-PM ET0; it also performed well globally, yielding an average R2 of 0.97 and RMSE of 0.43 across 120 sites in mid-latitude (20°-50°) regions. This model demonstrates simplicity, accuracy, robust and generalization, holding great potential for widespread application, especially in the large-scale, high-resolution estimation of ET0. This study will contribute to advancements in water resources management, agricultural planning, and climate change studies

Blackfeet innovation pathways to food sovereignty: sustainability through indigenous-led research partnerships

The Blackfeet Nation in northwestern Montana, United States, is implementing its Agriculture Resource Management Plan (ARMP), an Indigenous-led, sustainable agriculture plan prioritizing economic development for Indigenous producers, intergenerational health and well-being of Amskapi Piikani Blackfeet people, and ecological and cultural sensitivities within this sovereign nation and its traditional territories. Since the passage of the American Indian Agricultural Resource Management Act of 1993, only three Tribes have drafted and finalized Agricultural Resource Management Plans (ARMPs). The Blackfeet ARMP is now being held up as a national model of Tribal sovereignty. “Blackfeet Innovation Pathways to Food Sovereignty,” an Indigenous-led research project, emerged from the Blackfeet Nation’s community-based strategic planning process identifying gaps, systemic barriers and impactful solutions for achieving Blackfeet food sovereignty through the implementation of the Blackfeet Nation ARMP, along with research influenced by the ARMP. This paper provides a community case study of the ongoing process and offers a translational model of sustainable agriculture and food sovereignty within Indigenous lands to improve the economic futures of producers and their families, as well as health outcomes for Native communities.

Multi-step-ahead prediction of water levels using machine learning: A comparative analysis in the Vietnamese Mekong Delta

This study evaluates the efficacy of five machine learning algorithms Support Vector Regression (SVR), Decision Tree (DT), Random Forest (RF), Light Gradient Boosting Machine Regressor (LGBM), and Linear Regression (LR) in predicting water levels in the Vietnamese Mekong Delta's tidal river system, a complex nonlinear hydrological phenomenon. Using daily maximum, minimum, and mean water level data from the Cao Lanh gauging station on the Tien River (2000-2020), models were developed to forecast water levels one, three, five, and seven days in advance. Performance was assessed using Nash-Sutcliffe Efficiency, coefficient of determination, Root Mean Square Error, and Mean Absolute Error. Results indicate that all models performed well, with SVR consistently outperforming others, followed by RF, DT, and LGBM. The study demonstrates the viability of machine learning in water level prediction using solely historical water level data, potentially enhancing flood warning systems, water resource management, and agricultural planning. These findings contribute to the growing knowledge of machine learning applications in hydrology and can inform sustainable water resource management strategies in delta regions.

Red-Wattled Lapwing (Vanellus indicus): A Traditional Rain Forecaster for Tribal Farmers of Southwestern Rajasthan

Abstract An intensive survey was carried out over the past 8 years in tribal-dominated areas of southwestern Rajasthan to document red-wattled lapwing (Vanellus indicus) behavior as a rain forecaster, relevant to farm planning and better crop production. The present study included tribal farmers, agriculture scientists, extension workers, ornithologists, and farm workers. The precision of their observations was compared with a contemporaneous study of 10–15 red-wattled lapwing nests across the 46-ha operational area of the Agricultural Research Sub-Station (ARSS) and Adaptive Trial Center (ATC), Sumerpur, Pali, Rajasthan (25.1526°N latitude, 73.0823°E longitude). Local knowledge was gathered through surveys and focus group discussions with the respondents to discuss lapwing behavior as a rain prediction indicator. Meteorological data were gathered from the ATC farm observatory. The findings revealed that the older tribal farmers have an assortment of knowledge about the behavior of red-wattled lapwing as rain prediction indicators such as nesting sites, egg position, egg arrangement, nesting material, numbers of eggs, and egg-laying patterns. These predictions have a direct correlation with modern meteorological observations. It was also noted that the elder tribal farmers planned their farms using lapwing indicators in order to increase crop production, whereas younger generations overlook the traditional rain prediction indicators and have significant knowledge about meteorological data-based weather forecasting. Both the elder and younger tribal farmers were equally interested in meteorological weather forecast announcements, but due to network connectivity challenges, residents in some remote places were unable to access modern meteorological information. Long-term accurate rain forecasting is needed for better agricultural planning and to secure crop production. Rain forecasting can be improved by blending modern meteorological information with traditional proven indications. Farmers who have considerable traditional knowledge along with modern technological updates are capable of using both sources of information in suitable and successful farm planning for better crop production. Significance Statement The purpose of this study is to examine red-wattled lapwing behavior as rain measurements and compare its reliability with modern rain forecasting methods. We also develop a theoretical framework for understanding traditional and modern rain forecasting methods for disaster management in remote and accessible areas, to assist resource management and to minimize costs of cultivation. Our results provide a guideline for blending both types of knowledge and for providing relevant information to remote tribal areas at appropriate times for better farm planning and crop production.

Enhanced Spatio-Temporal Modeling for Rainfall Forecasting: A High-Resolution Grid Analysis

Rainfall serves as a lifeline for crop cultivation in many agriculture-dependent countries including India. Being spatio-temporal data, the forecasting of rainfall becomes a more complex and tedious process. Application of conventional time series models and machine learning techniques will not be a suitable choice as they may not adequately account for the complex spatial and temporal dependencies integrated within the data. This demands some data-driven techniques that can handle the intrinsic patterns such as non-linearity, non-stationarity, and non-normality. Space–Time Autoregressive Moving Average (STARMA) models were highly known for its ability to capture both spatial and temporal dependencies, offering a comprehensive framework for analyzing complex datasets. Spatial Weight Matrix (SWM) developed by the STARMA model helps in integrating the spatial effects of the neighboring sites. The study employed a novel dataset consisting of annual rainfall measurements spanning over 50 (1970–2019) years from 119 different locations (grid of 0.25 × 0.25 degree resolution) of West Bengal, a state of India. These extensive datasets were split into testing and training groups that enable the better understanding of the rainfall patterns at a granular level. The study findings demonstrated a notable improvement in forecasting accuracy by the STARMA model that can exhibit promising implications for agricultural management and planning, particularly in regions vulnerable to climate variability.

Clustering approach to group similar soils for efficient agricultural land use planning

Clustering approach to group similar soils for efficient agricultural land use planning

Impact assessment of agricultural droughts on water use efficiency in different climatic regions of Punjab Province Pakistan using MODIS time series imagery

AbstractDrought is the most destructive phenomenon that distresses the terrestrial carbon cycle balance and crop production. The variation in evapotranspiration (ET) and gross primary productivity (GPP) is a significant cause of agricultural drought effects on water use efficiency. This study aims to evaluate the impact of agricultural drought on WUE and it's anomalies in different climate regions. The standard vegetation index was used to measure the extent of agricultural drought. WUE was calculated using the ratio of ET, GPP, and climate classification using the De Martonne method. This study was conducted over the last 22 years, from 2001 to 2022. Meanwhile, 2001, 2002, 2014, and 2018 were considered high drought years based on a 22‐year analysis. According to remote sensing analysis of ET and GPP, the WUE increased throughout drought in all climate regions and more strongly in the arid zone than in humid regions. Humid areas were more vital due to drought than arid ones. WUE anomalies badge with increased drought severity across all climates except the very humid zone. Humid climates saw faster recovery times than arid ones, and areas experienced severe droughts. The findings of this research are essential for understanding the carbon and water cycles for agriculture management. The analysis of drought severity and WUE across arid climate regions helped analyse the varying impact of drought on WUE due to climate change. The significance of this study includes informing agricultural, water resource, and drought management planning in the Punjab Province, an agricultural region vulnerable to climate change. This study holds important learnings for agricultural regions worldwide. It has practical and scientific importance regarding agricultural systems' specific stresses and responses to droughts.

Comparative evaluation of machine learning algorithms for rainfall prediction to improve rice crops production

Rainfall has a huge impact on agriculture because it's one of the key causes of crops devastation. Farmers face a slew of issues when unexpected heavy rains fall, as their planted crops are washed away or damaged. Pakistan is an agricultural country where new methods and techniques are needed to improve the traditional farming methods. This research intends to provide aid in the protection of crops from severe rains using machine learning to accurately anticipate the possibility of rainfall, which is a well-known agricultural problem. Various weather factors such as temperature, humidity, and atmospheric pressure can be used to predict rainfall patterns. Rainfall prediction can be used to identify and furnish future rainfall descriptions for agricultural planning for food security, allowing farmers to take precautionary measures to safeguard rice fields. Naïve Bayes, LogitBoost, RIPPER, Decision Stump, AdaBoost, Random Forest, Artificial Neural network, and K* were evaluated for rainfall prediction based on accuracy, precision, recall, F1-measure, Root Mean Squared Error, area under receiver operating characteristic curve, elapsed training time and elapsed testing time. The results obtained indicate that the best performance is achieved by Random forest with maximum accuracy of 83.2%, followed by ANN (82.5%), LogitBoost (82.2%), RIPPER (82%), naïve Bayes (80.3%), AdaBoost (80.2%), and K*(79.2%) respectively. K* The lazy approach involved a minimum of training time and a maximum of test time. Maximum training time was consumed by Random Forest and minimum testing time was taken by Decision Stump.

KARAKTER SPASIAL DAN TEMPORAL CURAH HUJAN BULANAN KABUPATEN JEMBER BERDASARKAN DATA CHIRPS

Jember Regency is one of the national food storage areas. One of the most important resources in cultivating food crops is water which comes from rainfall. The existence of rainfall data is very important in planning and implementing agricultural activities. Limited rainfall data is the main problem for agricultural planning. One solution to this problem is to utilize rainfall data from CHIRPS (Climate Hazards Group Infrared Precipitation Station). The use of CHIRPS data is still biased data, so bias correction is needed to measure the level of accuracy of CHIRPS data for estimating rainfall in an area. Bias correction is carried out using two methods, namely multiple linear regression and power regression. The correction results show that the multiple linear regression method is able to produce monthly rainfall that is close to observations with an RMSE value of 3.9 and a coefficient of determination of 0.99. Temporally, the peak of the rainy season in Jember Regency occurs in December while the dry season is in August. In the rainy season (December-January and February) Jember Regency's rainfall is around 300-500 mm/month and in the dry season (June-July-August) it is in the range of 50-200 mm/month. Spatially, the northern part of Jember, especially Sumberbaru, Bangsalsari and Tanggul subdistricts, is wetter than the Jember Regency area as a whole. The southern part of Jember, especially those close to the coast, has lower rainfall than other regions. In the dry season, rainfall in this region is less than 20 mm/month.

A dataset of land use/land cover fusion in Guangxi, China in 2000, 2010 and 2020

This study is based on six major land use/land cover remote sensing data products that are widely used in China and the world, namely ESA Global Land Cover Product (ESAGC), ESRI Global 10-meter Land Use/Land Cover Data (ESRI-LULC), Tsinghua University high-resolution global land cover product (FROM-GLC), Wuhan University China Land Cover Dataset (CLCD), global 30-meter high-resolution system land cover product (GLC_FCS30) and GlobeLand30 (GLC30). By reclassifying multiple data products, we developed a multi-source data product fusion method based on consistency assessment, and compiled a new Guangxi region land use/land cover data product in 2020 with a correlation coefficient greater than 0.94 with local statistical yearbook data. The study further used three sets of long sequence data (CLCD, GLC_FCS30 and GLC30) to create three land use/land cover fusion data products in Guangxi in 2000, 2010 and 2020. This study provides a methodological basis for the application of regional land use/land cover data products. The fusion of data can better support related research on regional environmental protection, agricultural production planning, and urban development planning in Guangxi.

Long-term drought prediction using deep neural networks based on geospatial weather data

The problem of high-quality drought forecasting up to a year in advance is critical for agriculture planning and insurance. Yet, it is still unsolved with reasonable accuracy due to data complexity and aridity stochasticity. We tackle drought data by introducing an end-to-end approach that adopts a spatio-temporal neural network model with accessible open monthly climate data as the input. Our systematic research employs diverse proposed models and five distinct environmental regions as a testbed to evaluate the efficacy of the Palmer Drought Severity Index (PDSI) prediction. Key aggregated findings are the exceptional performance of a Transformer model, EarthFormer, in making accurate short-term (up to six months) forecasts. At the same time, the Convolutional LSTM excels in longer-term forecasting. Both models achieved high ROC AUC scores: 0.948 for one month ahead and 0.617 for twelve months ahead forecasts, becoming closer to perfect ROC-AUC by 54% and 16%, respectively, c.t. classic approaches.

Characterization and Mapping of the Potential Area of Oil Palm Using Multi-Criteria Decision Analysis in a Geographic Information Systems Environment

This study presents a GIS-based Multi-Criteria Decision Analysis (MCDA) spatial model to assess land suitability for oil palm (OP) cultivation in rainfed conditions. Initially, twelve parameters, viz., rainfall, number of rainy days, mean temperature, RH, ground water level, soil pH, salinity, soil depth, surface texture, stoniness, slope, and drainage, were selected for assessing OP suitability in one of the states (Kerala). However, subsequent ground verification revealed significant discrepancies, which prompted refining the model by focusing on key parameters with greater accuracy and relevance. Accordingly, only five the most critical parameters affecting OP cultivation under rainfed conditions were selected through the rank sum method, and weights were assigned ac-cording to their significance. This study was aimed at creating a comprehensive tool for informed decision making in agricultural planning. District-level spatial data from reliable sources were utilized for Multi-Criteria Decision Analysis. Thematic rasters, representing key factors influencing land suitability, were created in a GIS. Utilizing MCDA techniques, a digital suitability map was generated in ArcGIS 10.3, delineating three distinct classes over an extensive area of 10.5 million hectares. Further, with an aim to focus on actual locations that can be readily planted with oil palm, the suitable locations identified were restricted to eight selected land use/land cover (LULC) classes. This strategic limitation aimed to facilitate the expansion of OP cultivation exclusively to areas deemed most suitable based on the identified criteria. The validation of this developed model involved comparing the suitability map generated with the performance of existing oil palm plantations across diverse locations. The reasonable similarity between the model’s predictions and real-world plantation outcomes validated the effectiveness of this MCDA spatial model. This model not only helps identify suitable locations for rainfed oil palm cultivation but also serves as a valuable tool for strategic decision making in agricultural land use planning.

Assessing the impact of climate variability on maize yields in the different regions of Ghana-A machine learning perspective.

Climate variability has become one of the most pressing issues of our time, affecting various aspects of the environment, including the agriculture sector. This study examines the impact of climate variability on Ghana's maize yield for all agro-ecological zones and administrative regions in Ghana using annual data from 1992 to 2019. The study also employs the stacking ensemble learning model (SELM) in predicting the maize yield in the different regions taking random forest (RF), support vector machine (SVM), gradient boosting (GB), decision tree (DT), and linear regression (LR) as base models. The findings of the study reveal that maize production in the regions of Ghana is inconsistent, with some regions having high variability. All the climate variables considered have positive impact on maize yield, with a lesser variability of temperature in the Guinea savanna zones and a higher temperature variability in the Volta Region. Carbon dioxide (CO2) also plays a significant role in predicting maize yield across all regions of Ghana. Among the machine learning models utilized, the stacking ensemble model consistently performed better in many regions such as in the Western, Upper East, Upper West, and Greater Accra regions. These findings are important in understanding the impact of climate variability on the yield of maize in Ghana, highlighting regional disparities in maize yield in the country, and highlighting the need for advanced techniques for forecasting, which are important for further investigation and interventions for agricultural planning and decision-making on food security in Ghana.

Planowanie terenów rolniczych w Bułgarii - obecna funkcjonalność prawodawstwa i perspektywy

Development Planning for the Agricultural Territory is a problematic activity in Bulgaria in terms of the existing legal requirements. Bulgarian legislation contains certain gaps in regulating and technically ensuring the agricultural land planning. This is well settled in relation to the other types of territories like the Urban, Forest, and Protected ones, which planning rules and activities are regulated in details. The purpose of the paper is firstly to justify the necessity of improving and detailing the legal regulations for the agricultural land development planning, and secondly to suggest some expert approaches for improving and harmonization of legislation in connection with registration of the agricultural property by Agricultural Territory Development Plan (ATDP), Restored Property Map (RPM) and Cadastral Map (CM). The current legal grounds for planning development of the agricultural land in Bulgaria are presented and analysed. Lack of detailed regulations on their scope, content and operation have been found. It is also a matter of applicability of the newly created municipal CMs in settling land relations such as purchases and sales of land, inheritances, divisions, and changing the primary purpose of use of Agricultural Territory for construction. The completeness of CM with information about the quality of agricultural lands is still not regulated by Cadaster and Property Register Act. The paper contains specific proposals for amendments in Spatial Planning Act, Cadastral and Property Registry Act and Agricultural Land Ownership and Use Act.

Spatiotemporal Analysis of Food Production-Demand Mismatch in China and Implications for Agricultural Structural Adjustment.

Based on the food equivalent unit (FEU), this article analyzed Chinese food consumption patterns, spatial mismatch, and production potential to explore agricultural reform strategies. Assessing production-demand mismatch involved the spatial mismatch model, drawing data from statistical yearbooks. Calculations of food production potential utilized the CASA model and the Thornthwaite Memorial model, with net primary productivity (NPP) derived from remote sensing data as indicators. The results showed that livestock product consumption is on the rise, and the spatial mismatch index for herbivorous livestock products was the largest, ranging from 22.81 to 98.12 in 2019. The mismatched degree distribution of rations and food-consuming livestock products showed a trend of increasing on both sides, with the Hu Huanyong line as the center line. Production factors played a predominant role in food production-to-demand mismatch. Climatic productivity and actual productivity decreased from the southeast to northwest in space in 2019, and human activities significantly impacted productivity. When grassland agriculture is pursued as the adjustment orientation, the production potential can reach up to 4540.76 × 107 kg·FEU. Moreover, a grassland agriculture plan was devised, prioritizing its development in the developed southern regions.

Innovative Applications of Artificial Intelligence in Agricultural Land Planning

This paper reviews the diverse applications of Artificial Intelligence (AI) in agricultural land planning, highlighting how AI enhances agricultural production efficiency, optimizes resource allocation, and strengthens decision-making quality to promote sustainable agriculture. The article discusses AI's role in land suitability analysis, precision agriculture, integration into decision support systems, and the challenges and limitations of technology, emphasizing AI's significant role in advancing sustainable agricultural development and future research directions. Despite challenges such as data quality, model transparency, and ethical issues, AI's application prospects remain broad, potentially becoming a significant driving force in agricultural land planning and global food security.

An optimized network for drought prediction using satellite images

The change in climate and the hot temperature environment increased the risk of drought around the workplace. Predicting and forecasting the drought occurrence is essential for managing water resources and agricultural plans. Therefore, in this study, a novel Chimp-based Wide ResNet Prediction Framework (CWRPF) is designed to predict the drought. The key motive of the presented research is to predict the drought and no drought conditions derived from the satellite images. The satellite images are collected from the Bhuvan site. Initially, the satellite images are noise-filtered. The filtered images are then injected into the feature analysis phase to compute the drought indices of a specific area by the fitness function activated in the framework. After estimating the drought indices, the drought condition was categorized. Finally, the designed system is tested in the MATLAB platform and has gained more significant results by providing a 97.68% accuracy rate, R2 as 0.998, and lower RMSE and MAE values of 0.223 and 0.193. The accumulated results are compared with existing techniques to validate the improvement score. The accuracy of the CWRPF is more remarkable than that of other prediction models. Therefore, the system is efficient for drought prediction in satellite images.

Analysis Feasibility Of Farming Ground Water Spinach (Ipomoea Reptans Poir)

Purpose: This study aims to analyze the feasibility of land spinach farming in Sukarukun Village, Sukatani District, Bekasi Regency, to understand the profitability for farming families. Methodology: The study employed a saturated sample method with a population of 30 individuals. Data analysis included total cost analysis, revenue, income, R/C ratio, and Break Even Point (BEP). Results: The results showed that the average total cost was IDR 52,448,625 per year, revenue was IDR 75,600,000 per year, and income was IDR 23,151,375 per year. The R/C ratio was 1.4, indicating that the farming business is feasible. The BEP for production was 3,108 kg, BEP for price was IDR 4,162 per kg, and BEP for revenue was IDR 18,653,718. Findings: The study found that land spinach farming in the study area is profitable and feasible, as indicated by the R/C ratio and BEP calculations. Novelty: This research provides new insights into the economic feasibility of land spinach farming, contributing valuable information for farmers and stakeholders in agricultural planning. Originality: The study offers a detailed economic analysis of land spinach farming, highlighting key metrics such as total cost, revenue, income, R/C ratio, and BEP, which are essential for assessing farming viability. Conclusions: Land spinach farming in Sukarukun Village is economically feasible with a favorable R/C ratio and manageable BEP, making it a viable agricultural activity for local farmers. Type of Paper: Empirical Research Artikel Keywords: Farming Cost; Land Spinach; Feasibility; Revenue; Income