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In both capitalist and socialist economies, agricultural planning has been understood as the allocation of areas for planting of certain products; the production potential and ability to act rationally of farmers, the main productive actors, have been neglected. In addition, in both economic structures, the desired results have only been partially achieved by determining prices. These experiences reveal two important results. Firstly, agriculture can not be fully planned, but it cannot be continued without planning either. Secondly, it is necessary for agricultural products to have an exchange value, but price is a multifunctional phenomenon and how it is determined is important. The current equivalent of the claim that prices are determined through the market mechanism is to advocate that prices are determined by capitalist companies that have monopoly power in the markets. The increasingly severe problems and increasing fragility of agricultural food markets, which are already largely under the control of these institutions, already show that this structure is not a solution. On the other hand, it is no longer possible to provide food security by planning for primary agricultural products alone; agro-food production and distribution have acquired different structural characteristics, and it has become necessary to address food security within the framework of agro-food systems. Therefore, effective agricultural planning requires the planning of the agricultural food system as a whole. The e-nam system being popularized in India has achieved a certain success and shows not only that it is possible to organize and manage agricultural food markets after primary production but also that technological opportunities offer effective planning opportunities. In this study, based on historical and current experiences, the inadequacies of the market mechanism, the fact that agricultural production is carried out by using living things to produce living things, and the dependency of uncontrollable factors are taken into consideration in the development of a democratic mix of centralized and decentralized planning aimed at determining both the production quantities and prices by utilizing the capacity and conditions of farmers and technological opportunities to meet today's needs. In accordance with this result, a basic planning proposal is presented. [ABSTRACT FROM AUTHOR]

Predicting the average monthly rainfall in Mecca is crucial for sustainable development, resource management, and infrastructure protection in the region. This study aims to enhance the accuracy of long short-term memory (LSTM) deep regression models used for rainfall forecasting using an advanced grid-search-based hyperparameter optimization technique. The proposed model was trained and validated on a historical dataset of Mecca's monthly average rainfall. The model's performance improved by 5.0% post-optimization, reducing the root-mean-squared error (RMSE) from 0.1201 to 0.114. The results signify the value of grid search optimization in improving the LSTM model's accuracy, demonstrating its superiority over other common hyperparameter optimization techniques. The insights derived from this research provide valuable input for decision-makers in effectively managing water resources, mitigating environmental risks, and fostering regional development. HIGHLIGHTS Utilized grid search optimization to fine-tune LSTM network parameters, achieving unprecedented forecasting accuracy.; Demonstrated the effectiveness of the proposed method through extensive validation against historical rainfall data.; Highlighted the potential of the method for improving water resource management and planning in arid regions.; Contributed to the body of knowledge in meteorological forecasting with a scalable and adaptable AI approach.;

Territorial Planning is a scientific discipline, aimed at achieving the physical organization of the space. The objective of the work was to present the main results of the implementation and operation of this service in sugarcane agriculture, which allows greater reliability in the control of the land and its use, support for decision making in planting programs, varieties, calculation of fertilizers, herbicides, production estimates, blocking and identification of limiting factors. Its implementation and operation defined the bases for updating the Specialized Cadastre in the sugarcane area of the country. It contributed to the performing of the program to bring the areas closer to the plant, allowing us to have an agricultural planning strategy that favors the correct use of the land, as well as the determination of vulnerable areas in the sugar mill companies with a view to facing the effects of climatic change. It constituted the geospatial expression of the scientific-technical services offered by the Sugarcane Research Institute to the farmer. It made possible the blocking of the sugarcane areas and the compaction of the blocks. The service facilitates efficient decision-making to establish comprehensive technologies, with impact on increasing agricultural yields. [ABSTRACT FROM AUTHOR]

In Mediterranean farming systems, the semi-arid conditions and agricultural ecosystems have made site-specific management an important approach. This method aims to understand and handle the variability of soil properties and crop management, particularly through the utilization of geospatial information and accessible technology. Over three years in a 30 ha experimental field located in the Alentejo region (Portugal), crop establishment was monitored using data from soil apparent electrical conductivity (ECa), remote sensing (Sentinel-2), and in situ soil sampling. The procedure began with Step 1, involving the acquisition of soil spatial information and spatial interpolation. Subsequently, in Step 2, management zones (MZs) for soil characteristics were delineated using a combination of ECa measurements and soil analysis, and Step 3 spanned over three years of gathering meteorological data and crop remote sensing measurements. In Step 4, site-specific crop MZs were delineated by vegetation indexes (VIs). This article aims to increase the importance of in situ and remote assessments to more accurately identify areas with different productive potential. Results showed three MZs based on the percentage of sand, ECa, altimetry, exchangeable calcium, and exchangeable calcium properties, validated by crop VIs (Normalized Difference Vegetation Index (NDVI), Normalized Difference Red-Edge Index (NDRE), and Normalized Difference Moisture Index (NDMI)) over time. Although there are many sensorial techniques available for site-specific management, this paper emphasizes a methodology for the farmer to identify different MZs combining remote and in situ evaluations, supporting new opportunities for a more rational use of natural resources. Based on soil parameters, three site-specific management areas could be selected. NDMI was the index that best explained the MZs created according to soil properties. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) is one of the main components of the hydrological cycle and plays a crucial role for water resources. It is sensitive to climate change, and therefore, estimating ET under changing climatic conditions is essential in comprehending hydrological processes, particularly in agricultural water management. In this study, the impact of climate change on ET in the Central Anatolia region of Türkiye was assessed. For this purpose, RCP4.5 and RCP8.5 climate change scenarios based on two Earth System Models, HadGEM2-ES and MPI-ESM-MR, were employed for three future time periods: 2025-2049, 2050-2074, and 2075-2098. As a baseline period for comparison, the time interval spanning 1980-2000 was considered. ET values were computed by using the Penman-Monteith equation, recommended by the Food and Agriculture Organization, along with five widely utilized methods. The study revealed a consistent increase in ET depending on the employed methods for the future period in response to climate change. The average of the ET amounts for the region was determined as 1089 mm for the reference period 1980-2000. As the average of the six methods utilized, amounts estimated by HadGEM RCP4.5, HadGEM RCP8.5, MPI RCP4.5, and MPI RCP8.5 models for the future period were obtained as 1 199, 1 285, 1 166, and 1 248 mm, respectively. Considering the results, it is found that the ET amount in the Central Anatolia region of Türkiye will increase by up to 11% by the end of this century under the optimistic RCP 4.5 scenario and by up to 19% under the extreme RCP 8.5 scenario. These findings regarding increased evapotranspiration play a significant role in water resource management and agricultural production planning in the region, holding crucial implications for sustainable agriculture.

Agriculture is pivotal for the economy of a country as it is a major source of food, employment and raw materials. However, challenges such as diseases, soil degradation, and water scarcity persist. Technology adoption can address these issues, improving production and quality. Machine learning, a subset of Artificial Intelligence (AI), enables prediction, classification, and automation in agriculture. It optimizes irrigation, fertilization, and crop selection, aiding decision-making for food security and crop management. This study proposes two robust machine learning architectures for classification and regression based on distinct datasets. Firstly, we delve into a crop recommendation dataset obtained from Kaggle, consisting of various input attributes such as the pH of the soil, temperature, humidity, and nutrient levels. Leveraging machine learning classification techniques such as Extra Tree Classifier (ETC), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), K-Nearest Neighbour (KNN), Gaussian Naive Bayes (GNB), and Support Vector Machine (SVM), we suggest twenty-two different crops founded on these inputs. Through the use of K-fold cross-validation, Explainable AI (XAI) and feature engineering, we identify the best-performing model, with Random Forest coming out on top scoring an accuracy of 99.7% with precision, recall, F1 score, and confusion matrix. Secondly, we investigate wheat yield prediction data snagged from the World Bank and Food and Agriculture Organization (FAO), covering the years 1992-2013 for Pakistan. Using Multivariate Imputation by Chained Equations (MICE) to tackle data restrictions, we gauge wheat production for 2014-2024 and forecast the 2025 yield using machine learning regression models. Once again, using hyper parameter tuning with K-fold cross-validation, Support Vector Regressor (SVR) stands out as the top-performing model, achieving an accuracy of 99.9% with R2 Score. Transparency and confidence in agricultural decision-making are increased when machine learning decisions are made comprehensible using Explainable AI (XAI) approaches. Two widely used XAI approaches, namely Feature Importance and Local Interpretable Model-Agnostic Explanations (LIME) are used to interpret and explain outcomes of the proposed models. The study can increase agricultural productivity, minimize risks, enhance food security, and promote more environmentally friendly farming approaches.

In Mediterranean farming systems, the semi-arid conditions and agricultural ecosystems have made site-specific management an important approach. This method aims to understand and handle the variability of soil properties and crop management, particularly through the utilization of geospatial information and accessible technology. Over three years in a 30 ha experimental field located in the Alentejo region (Portugal), crop establishment was monitored using data from soil apparent electrical conductivity (ECa), remote sensing (Sentinel-2), and in situ soil sampling. The procedure began with Step 1, involving the acquisition of soil spatial information and spatial interpolation. Subsequently, in Step 2, management zones (MZs) for soil characteristics were delineated using a combination of ECa measurements and soil analysis, and Step 3 spanned over three years of gathering meteorological data and crop remote sensing measurements. In Step 4, site-specific crop MZs were delineated by vegetation indexes (VIs). This article aims to increase the importance of in situ and remote assessments to more accurately identify areas with different productive potential. Results showed three MZs based on the percentage of sand, ECa, altimetry, exchangeable calcium, and exchangeable calcium properties, validated by crop VIs (Normalized Difference Vegetation Index (NDVI), Normalized Difference Red-Edge Index (NDRE), and Normalized Difference Moisture Index (NDMI)) over time. Although there are many sensorial techniques available for site-specific management, this paper emphasizes a methodology for the farmer to identify different MZs combining remote and in situ evaluations, supporting new opportunities for a more rational use of natural resources. Based on soil parameters, three site-specific management areas could be selected. NDMI was the index that best explained the MZs created according to soil properties.

Evapotranspiration (ET) is one of the main components of the hydrological cycle and plays a crucial role for water resources. It is sensitive to climate change, and therefore, estimating ET under changing climatic conditions is essential in comprehending hydrological processes, particularly in agricultural water management. In this study, the impact of climate change on ET in the Central Anatolia region of Türkiye was assessed. For this purpose, RCP4.5 and RCP8.5 climate change scenarios based on two Earth System Models, HadGEM2-ES and MPI-ESM-MR, were employed for three future time periods: 2025-2049, 2050-2074, and 2075-2098. As a baseline period for comparison, the time interval spanning 1980-2000 was considered. ET values were computed by using the Penman-Monteith equation, recommended by the Food and Agriculture Organization, along with five widely utilized methods. The study revealed a consistent increase in ET depending on the employed methods for the future period in response to climate change. The average of the ET amounts for the region was determined as 1089 mm for the reference period 1980-2000. As the average of the six methods utilized, amounts estimated by HadGEM RCP4.5, HadGEM RCP8.5, MPI RCP4.5, and MPI RCP8.5 models for the future period were obtained as 1 199, 1 285, 1 166, and 1 248 mm, respectively. Considering the results, it is found that the ET amount in the Central Anatolia region of Türkiye will increase by up to 11% by the end of this century under the optimistic RCP 4.5 scenario and by up to 19% under the extreme RCP 8.5 scenario. These findings regarding increased evapotranspiration play a significant role in water resource management and agricultural production planning in the region, holding crucial implications for sustainable agriculture. [ABSTRACT FROM AUTHOR]

Coffee is a major industrial crop that creates high economic value in Thailand and other countries worldwide. A lack of certainty in forecasting coffee production could lead to serious operation problems for business. Applying machine learning (ML) to coffee production is crucial since it can help in productivity prediction and increase prediction accuracy rate in response to customer demands. An ML technique of artificial neural network (ANN) model, and a statistical technique of autoregressive integrated moving average (ARIMA) model were adopted in this study to forecast arabica coffee yields. Six variable datasets were collected from 2004 to 2018, including cultivated areas, productivity zone, rainfalls, relative humidity and minimum and maximum temperatures, totaling 180 time-series data points. Their prediction performances were evaluated in terms of correlation coefficient (R2), and root means square error (RMSE). From this work, the ARIMA model was optimized using the fitting model of (p, d, q) amounted to 64 conditions through the Akaike information criteria arriving at (2, 1, 2). The ARIMA results showed that its R2 and RMSE were 0.7041 and 0.1348, respectively. Moreover, the R2 and RMSE of the ANN model were 0.9299 and 0.0642 by the Levenberg-Marquardt algorithm with TrainLM and LearnGDM training functions, two hidden layers and six processing elements. Both models were acceptable in forecasting the annual arabica coffee production, but the ANN model appeared to perform better.

Site selection for pistachio orchards is an important issue for sustainable agricultural policies, crop productivity, agricultural planning, and communities. This study aims to investigate suitable places for pistachio in the Mardin Province (SE Turkey) by considering several variables, such as meteorological data, topographic conditions, economic factors, and soil characteristics, using Geographic Information System (GIS) and Multi-Criteria Decision Analysis. Pistachio farmers, expert opinions, and literature data were used to determine the requirements for pistachio cultivation. Four main assessment criteria (thirteen sub-criteria), sixty value ranges, and fourteen exclusion criteria were determined for the pistachio land suitability assessment. The weighting of the evaluation criteria was calculated using the Analytical Hierarchy Process (AHP). Farmers and experts have stated that meteorological factors are more important than soil, topography, and economic factors. All data were transferred to the GIS environment, and a land suitability map was created using the weighted linear combination method. The results show that Mardin province has very suitable lands for pistachio cultivation. The resulting map determined that the 228,891.59 ha area in Mardin province is very suitable for pistachio. To evaluate the accuracy of the land suitability map generated for pistachio, the Receiver Operating Characteristic (ROC) curve was used. The value of the area under the curve (AUC) was calculated to be 0.806, which indicates that the study is consistent. The created suitability map will be an essential data source for developing sustainable agricultural strategies in the Southeastern Anatolia region. [ABSTRACT FROM AUTHOR]

Climate change is likely to intensify the variability of weather patterns, leading to a rise in extreme seasonal aberrations. The extreme weather events like heat waves, cold waves and frost have great influence on agriculture productivity. These events have negatively impact on crop production and farmers' livelihood. The prediction of magnitude, frequency, timing and duration of extreme weather events in the changing scenario of climate can be supportive towards adaptation of suitable strategies to mitigate its adverse effects on agriculture and socio-economic aspects. The study of occurrence of significant climatic events and their trend analysis were done by utilizing daily maximum temperature, minimum temperature for Morena district which lies in Gird Agroclimatic zone of Madhya Pradesh for the period of 30 years (1984-2013) with the IMD criteria. The study revealed that heat waves varied with annual time scale. The maximum number of heat waves was observed in the year 2010 (57). The 12.4 average annual heat waves were observed during the period 1984-2013. About 4 heat waves could be expected in the month of June. An increasing trend of heat waves was noticed and pointed towards rising of maximum temperature over the region. The cold waves were also exhibited variability. The maximum number of cold waves was observed in the year 2007-08 (20). On an average 5.8 cold waves were observed per year during the period 1984- 85 to 2012-13. The trend analysis revealed that cold waves were becoming less frequent. On an average, 9 frost events per year could be expected. The decreasing trend of frost events was noticed at annual time scale. There is a need for combining disaster reduction, natural resource management and climate change adaptation to reduce vulnerability and enhancement of resilience. Changes in technology and agricultural practices will help to create a society better able to survive the impacts of climatic extremes. [ABSTRACT FROM AUTHOR]

Coffee is a major industrial crop that creates high economic value in Thailand and other countries worldwide. A lack of certainty in forecasting coffee production could lead to serious operation problems for business. Applying machine learning (ML) to coff ee production is crucial since it can help in productivity prediction and increase prediction accuracy rate in response to customer demands. An ML technique of artificial neural network (ANN) model, and a statistical technique of autoregressive integrated moving average (ARIMA) model were adopted in this study to forecast arabica coffee yields. Six variable datasets were collected from 2004 to 2018, including cultivated areas, productivity zone, rainfalls, relative humidity and minimum and maximum temperatures, totaling 180 time-series data points. Their prediction performances were evaluated in terms of correlation coefficient (R2), and root means square error (RMSE). From this work, the ARIMA model was optimized using the fitting model of (p, d, q) amounted to 64 conditions through the Akaike information criteria arriving at (2,1,2). The ARIMA results showed that its R2 and RMSE were 0.7041 and 0.1348, respectively. Moreover, the R2 and RMSE of the ANN model were 0.9299 and 0.0642 by the Levenberg-Marquardt algorithm with TrainLM and LearnGDM training functions, two hidden layers and six processing elements. Both models were acceptable in forecasting the annual arabica coffee production, but the ANN model appeared to perform better. [ABSTRACT FROM AUTHOR]

The proper application of agricultural machinery requires, among other aspects, the necessary inputs to perform planned maintenance (PM) in a timely manner. The objective of the research was focused on developing a procedure for the integrated planning of the workload and the PM inputs for the soil preparation tractors. The research was carried out at "Panchito Gómez Toro" Sugar Agroindustrial Company, in an area of 1833.4 ha, in 48 blocks dedicated to sugar cane. LabraS software was used for tillage planning and functionalities were added to plan the maintenance and associated supplies (oils, greases and coolant and brake fluids). The conditions were characterized by prevailing problems of effective depth, poor drainage and stoniness (between 19 and 22%), as well as by the predominance of the medium soil texture (93%) and the conditions of the soil surface in areas of cane demolition (74%). Satisfactory results were achieved through LabraS software in the integrated data collection of the workload and the PM inputs for the available tractors, and according to their ownership (MTZ-80, T-150K, Belarus 1523 and Komatsu), in addition the information was specified by month and total in the year. This result made possible to carry out adequately the quarterly and annual balance of lubricants requested by AZUMAT Logistic Company, in addition to varying the demand based on the existing statistics, or the changes arising thank to the computerization of the process. [ABSTRACT FROM AUTHOR]

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The long-term fluctuations in dry-wet spells were assessed at standard meteorological week (SMW) over India using Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) rainfall data. The weekly sum of rainfall was embedded in Markov Chain Probability Model in Google Earth Engine (GEE) platform to compute initial and conditional probabilities of dry-wet spells during 2009-2020. An effective monsoon window (23rd SMW-39th SMW) was identified where initial probabilities (IPs) of dry (Pd) and wet (Pw) spells intersect at 50% probability level. Significant spatiotemporal variation of IPs was observed with initiation and withdrawal of monsoon over India. The analysis of coefficient of variation (CV) showed low CV (<60%) in Pd and high CV (>60%) in Pw in semi-arid and arid regions whereas northern, central and eastern regions observed high CV (>60%) in Pd and low CV (<40%) in Pw. The drought prone and moisture sufficient zones were indentified based on the analysis of long-term frequency distribution of dry-wet spells and trend. Inter-comparison of IPs between CHIRPs with IMD (Indian Meteorological Department) and NOAA CPC (National Oceanic and Atmospheric Administration/Climate Prediction Centre) showed encouraging results. The study provides baseline reference for climate-resilient agricultural crop planning with respect to food security. [ABSTRACT FROM AUTHOR]

The Sugarcane Research Institute worked on the development of the software (SW) LabraS specialized in the planning of soil tillage processes. Evaluating the functional adequacy of the LabraS software in the planning of the sustainable soil preparation for sugarcane cultivation is the objective of this paper. The work was carried out at "Héctor Rodríguez" Base Business Unit (UEB), of the AZCUBA Sugar Group. The evaluation included the 2020-2021 soil preparation campaign, with 2 619.2 ha, concentrated in 57 blocks dedicated to sugarcane. The research conditions were characterized as complex, with a predominance of poorly drained areas (57%), medium and heavy textured soils (89%), and fallows or cane fields with very low agricultural yields without harvest (67%). The results showed an adequate selection of the technological alternatives, their operational variants and tasks by management condition. In addition, the correct application of the ISMACE criteria (integration of knowledge on soil, machinery, crop and working environment) in the algorithm of the LabraS software, for selecting the technologies with the best technological, economic, energetic and environmental impact, generally demonstrated satisfactory results in the validation of the functional adequacy for the planning of sustainable soil preparation. [ABSTRACT FROM AUTHOR]

Introduction: With the growing population of the world, water, food and energy supply will be one of the most important challenges ahead. Agriculture as the most important food producer is not only the consumer of water and energy, but also the most important supplier of energy. As a result, a balance must be struck between harvesting and exploitation of production resources and the amount of agricultural production. Due to the close relationship between water-food-energy systems and also their interaction with each other, a new concept called the "nexus" approach has been proposed which refers to the integrated nature and interactions of water-food-energy planning. This approach has provided suitable options for political decision makers, managers and planners in order to conserve existing resources and achieve sustainable development. Material and Methods: In this study, we have tried to introduce a mathematical programming model using multi-objective mathematical programming (MOP) technique for water-food-energy nexus that has the ability to process managerial decisions. In this model, in addition to examining the economic aspect, the control of greenhouse gas emissions has also been investigated. The regions of Mashhad, Chenaran and Torqabeh-Shandiz are the most important regions in the study area of Mashhad province in the production of crops. The data required for the study were collected through review of reports and agricultural statistical yearbooks of the year 2020-2021 and interviews with experts in each region and through consulting engineering companies. The hypothetical model under study includes lands covered by crops of Mashhad, Chenaran and Torqabeh-Shandiz. In this model, the water needed to irrigate crops is supplied from surface and groundwater sources. Electricity (electricity consumption) is used to collect and pump of irrigation water, produce food, and supply the domestic and industrial sectors. In the process of generating electricity, production of food, irrigation of crops and consumption of fertilizers and pesticides, greenhouse gases are emitted, especially CO2. In this study, 6 objectives including: maximizing gross profit, maximizing the production of calories from food, minimizing emission of greenhouse gases, minimizing consumption of fertilizers and pesticides, minimizing consumption of irrigation water, and minimizing consumption of energy have been pursued. Results and Discussion: The results of the proposed model showed that the rate of change in the level of cultivation area in MOP compared to the current cultivation pattern in Mashhad, Chenaran and Torqabeh-Shandiz decreased by 25.92%, 53.05% and 55.88%, respectively. The level of optimal cultivation for barley in Mashhad in order to maximize net profit objective increased by 16934 hectares (46.71%) and its maximizing caloric production equal to 8484 ha, which has decreased compared to the current pattern (22%). The cultivation area of barley in minimum irrigation water consumption decreased by 10877 hectares (1.11%) and in other minimization objectives it changed to 12892 hectares which increased by 17% in Mashhad region. Wheat, barley, alfalfa, corn, sugar beet, tomato and potato have the highest decrease in cultivation area in the MOP among crops. The total area of optimal cultivation in the net profit maximization model of Mashhad, Chenaran and Torqabeh-Shandiz equal to 48639, 26027 and 75 hectares, which showed an increase of 41.4%, 11.61% and 55.8%, respectively. Furthermore, in the model aimed at minimizing energy, irrigation water, fertilizer, pesticide consumption, and greenhouse gas emissions, the recommended cultivation areas are as follows: 25,475 hectares for energy consumption, 15,954 hectares for irrigation water consumption, and 100 hectares each for fertilizer consumption, pesticide consumption, and greenhouse gas emissions. These figures clearly indicate the need to reduce the cultivation area dedicated to agricultural products that have a significant environmental impact. Consequently, it is crucial to alter the cultivation pattern and adopt a strategy that focuses on producing crops with a lower environmental impact. By implementing this strategy, the objective is to cultivate crops that require less energy, irrigation water, fertilizer, and pesticides, while also minimizing greenhouse gas emissions. This approach aims to mitigate the environmental footprint associated with agricultural practices. By reducing the cultivation area for crops that have high environmental effects and transitioning towards crops that have a lesser impact on the environment, it is possible to achieve a more sustainable and environmentally friendly agricultural system. Conclusion: The purpose of this study was to propose a nonlinear multi-objective mathematical programming model with water-food-energy nexus approach for crops in Mashhad province. In this study, in addition to economic relations, energy and environmental issues (greenhouse gas emissions) were also analyzed. The various components of the water-food-energy nexus, including energy supply planning, water supply and demand, food production, and control of greenhouse gas emissions, were modeled. The results showed that considering the MOP model based on economic and environmental objectives, the area under cultivation of wheat, barley, alfalfa, tomatoes, sugar beets and potatoes has significantly decreased. In other words, in order to achieve the objectives of maximum profit and minimum environmental impact, the area under wheat, barley, tomato, corn should be reduced and the area under cucumber, onion, potato and sugar beet should be increased. According to the results of this study, the following suggestions are presented: - Implementation of the proposed optimal model of water-food-energy nexus allows farmers to simultaneously maintain economic income, environmental considerations, optimal and sustainable consumption of resources (water-food-energy) to select and consider suitable policies. So, it will only be a sustainable policy if it can be built within the combined framework of water, food, energy and the environment. - In order to minimize the emission of greenhouse gases and its damage to the environment, the area under cultivation of agricultural products that have high environmental impact should be reduced, and in contrast to changes in cultivation pattern, the strategy to produce crops with less impact. Therefore, by developing a cropping pattern model, the productivity of the production capacities of the agricultural sector can be maximized and at the same time the damages and destructive consequences of crop production can be reduced. [ABSTRACT FROM AUTHOR]

The long-term fluctuations in dry-wet spells were assessed at standard meteorological week (SMW) over India using Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) rainfall data. The weekly sum of rainfall was embedded in Markov Chain Probability Model in Google Earth Engine (GEE) platform to compute initial and conditional probabilities of dry-wet spells during 2009-2020. An effective monsoon window (23rd SMW–39th SMW) was identified where initial probabilities (IPs) of dry (Pd) and wet (Pw) spells intersect at 50% probability level. Significant spatiotemporal variation of IPs was observed with initiation and withdrawal of monsoon over India. The analysis of co-efficient of variation (CV) showed low CV (60%) in Pw in semi-arid and arid regions whereas northern, central and eastern regions observed high CV (>60%) in Pd and low CV (

Effective governance of agricultural systems is needed for achieving goals of food security, resilient food systems, and addressing the impacts of climate change. Local governments have an increasing interest in the role of agriculture in meeting these goals. However, alignment varies greatly between local governing systems and agricultural systems. Governability is a measure of the degree to which a system can be governed for a set of specified purposes or goals. To test the limits of governability in relation to agricultural planning, we interviewed 22 agricultural planners from municipal, regional, and provincial government, and analyzed agricultural plans (n = 8) and Official Community Plans (n = 6) for six municipalities in Metro Vancouver, Canada to identify interactions between broader municipal governance, agricultural planning, and agricultural systems outcomes. Findings indicate that the governing system for agriculture in this region includes both mandated obligations (conservation of farmland) and voluntary obligations (economic development, advocacy, public awareness). Multiple limits to governing agricultural systems include the promotion and implementation of simple solutions to complex problems, limited ability to engage with the diversity of the agricultural sector and their different needs, and governance mismatches with the boundaries of agriculture (i.e., farm parcels, Agricultural Land Reserve area) and the administrative scale of the municipality. The discussion identifies specific areas where municipal governance systems could transition to improve agricultural outcomes such as farmland protection, farmer economic viability, and integration with broader food systems.

Agricultural communities and crop production are negatively impacted by invasive species, with the effects of pathogenic fungi, parasitic insects and weedy plants being well studied. Mammals and birds are also recognized as impacting crops, but reptiles, such as non-native green iguanas (Iguana iguana), are typically not considered agricultural pests. Research on non-native green iguanas has largely focused on the lizard's interactions with native species with little attention given to its impact in the agricultural landscape. We conducted semi-structured interviews with farmers from 20 farms in Puerto Rico to explore the effect of the invasive green iguana on the production of crops and how farmers manage impacts, if any. A total of 34 of 55 crop species reported by farmers were negatively affected by the green iguana. We found that green iguanas were absent from 20% of farms, did not consume crops in 10% of the farms and caused negative impacts in 70% of the remaining farms. Negative impacts included crop loss and infrastructural damage, which had behavioral, emotional, and economic effects on farmers. Specific outcomes of these effects were revenue loss, refurbishing costs, changes in crop selection, management costs and emotional stress. Farmers considered management strategies as mitigation measures that needed to be constant to produce any positive effects on crop yield. They reported use of mesh fencing, hunting, and domestic animals as attempts to reduce negative effects of green iguanas on crop production. Recognition of this species as an agricultural pest is warranted in Puerto Rico and perhaps elsewhere in its introduced range. Agricultural extension agents should consider providing guidance on strategies to reduce negative impacts of green iguanas including cultivating less susceptible crops when possible. [ABSTRACT FROM AUTHOR]

The research quantified the probable monthly, quarterly, and annual precipitation height and analyzed the synoptic systems intervening over time for an important grain producing region located in the Brazilian legal Amazon. Through stochastic and geostatistical analysis, the study estimated the probable height of rain, showed its seasonality, its spatial behavior and the months with extreme rainfall. For stochastic modelling of rain, the gamma distribution, the maximum likelihood method was used to estimate the parameters, and the Kolmogorov-Smirnov test as an adhesion test. For spatialization of rain data, geostatistical modelling was performed using ordinary kriging as an interpolation method. We conclude that: there are two well-defined seasons in the middle north region of Mato Grosso (MNMT), a rainy one between October and April and a dry one between May and September; the months of April and October can be considered as months of transition between the two seasons; the months with the highest probable rainfall are December, January and February; the probable annual precipitation (PPA) in the MNMT region has an average value of 1622 mm, with a minimum of 1341 and a maximum of 1955 mm; the spatial distribution of rain is influenced by latitude and there is a tendency for the total to increase in the SE-NW direction. Finally, spatial analysis shows that the highest rainfall occurs in the north and northwest of the study area. The smallest, however, occur in the south and southeast of the area, mainly in the municipality of Nova Ubiratã. [ABSTRACT FROM AUTHOR]

The probability of a working day is the ratio of workable days to the total available days in a working season for the intended operation. Soil moisture is the most important limiting factor in terms of rice harvesting. Therefore, to determine the PWD for this operation, a model was developed to estimate the soil moisture based on soil-water balance. To calibrate and validate the model, the soil moisture was monitored in the paddy field of the University of Guilan during the rice harvest seasons in 2017 and 2018. The calibration process was performed by applying a decreasing ratio, i.e. the senescence factor (α), at a daily evapotranspiration rate compared to that of previous day. At the validation stage, the simulated soil moisture contents were compared with measured values, which indicated a good model accuracy (NRMSE = 6.53%) of the soil moisture estimation. Therefore, this model can be used for the rice harvesting operation feasibility evaluation and PWD calculation.

The Brazilian Northeast has great agricultural potential; however, the region suffers from high variability in precipitation. Water is the main element for plant development, and its entry and exit in the soil can be counted by water balance (WB). Therefore, the objective of this study was to determine the spatial and seasonal water conditions in Northeastern Brazil and thus show that the region can be a major agricultural producer. A historical series of rainfall and air temperature from 1950 to 1990, collected from 1536 surface weather stations, representing the entire region, was used. Potential evapotranspiration (PET) was estimated using the Thornthwaite (1948) method and WB by the Thornthwaite and Mather (1955) method, using an available water capacity (WC) of 100 mm, as it is the value used to characterize water availability. Descriptive analysis was performed to identify the variations of the data set, and the probability test was performed by the Kolmogorov–Smirnov method. The data were specialized using the kriging method. The distribution of air temperature values showed that the region had a temperature between 20 and 29 °C. The state of Maranhão (MA) was the warmest, with a probability of occurrence of 28 °C reaching 92%. MA is a state with climatic classes like Am, Aw, and As according to Köppen (1936). The rainfall in the northeast was between 955 mm annual−1 and 1600 mm annual−1, with the highest concentration in the state of MA and the lowest in Rio Grande do Norte (RN). Soil water storage (STO) was greater in January to June, mainly on the coast. Most of the water surplus (EXC) was distributed between May and July, a total of 60%, concentrated in MA and the northeastern coast. The averages were 200 mm annual−1 to 700 mm annual−1 for the water deficit (DEF), with the highest values concentrated in the Ceará (CE) state, with a high probability of occurrence. In CE the classes As and BSh according to Köppen (1936) predominate. The general mean of the region Northeast Brazil for STO, EXC, and DEF was 43.6 (± 17.6) mm, 231.4 (± 276) mm, and 430.6 (± 168.6) mm, respectively. [ABSTRACT FROM AUTHOR]

Due to randomness in the occurrence of hydrological phenomena, the estimation of probable rain precipitation in a given region is important in assisting decision-making. This work aimed to adjust the probabilistic model of the Gamma distribution to the monthly and annual rainfall totals recorded in the city of Cruzeiro do Sul, Acre, for the period between 1970 and 2019, in addition to estimating the expected values at different probability levels. Using the maximum likelihood method, the distribution parameters were estimated, with adherence ratified by the Kolmogorov-Smirnov test. The results showed that the Gamma distribution was adequate to adjust the data; the region has two well-defined periods in its rainfall pattern; the mean precipitation values recorded in the locality are between 25% and 40% of probability. Finally, probable rainfall values were presented at different probability levels for the city of Cruzeiro do Sul.

ABSTRACT The estimate of the potential sugarcane productivity through agroclimatic models aids in the agricultural planning of the crops and the quantification of the yield for a given region. For these estimated values to be considered robust there is a need for validating the performance of such models in different areas and agricultural varieties. Hence, the aim of this study was to validate the Agro-Ecological Zone (AEZ) method with fifteen sugarcane varieties in the region of the Vale do São Patrício, state of Goiás, Brazil. We evaluated the data referring to the cane-plant (one-and-a-half-year sugarcane), as well as the first and second sugarcane ratoons (both with one-year cycles) in an irrigated and dry farming system. The productivities obtained in dry farming were corrected due to the occurrence of a water deficit in the crop. The results indicated that the AEZ method presented productivity estimates more satisfactory for the one-year cultivation cycles (ratoon cycles) for all varieties studied, with the model adjusting best to the CTC15 variety (RMSE = 8.70 t ha-1; MAE = 6.05 t ha-1; d = 0.99).

This paper deals with the time series analysis of precipitation patterns in Africa’s most populated nation using recently developed flexible modelling techniques to study the monthly precipitation data of some major economically viable and highly populated regions in Nigeria. The results indicate that there is a significant trend for Lagos rainfall data, implying that precipitations have systematically increased over time in this city. Additionally, the seasonal component is more prominent in the cases of Kano and Kaunda than for Ibadan and Lagos. The findings of this study have relevant policy implications for forecasting, agricultural planning, relating precipitation variability in Nigeria with standard climate drivers, as well as for disaster or risk reduction in the context of climate change, global warming and migration in the post-pandemic era.

This paper deals with the time series analysis of precipitation patterns in Africa's most populated nation using recently developed flexible modelling techniques to study the monthly precipitation data of some major economically viable and highly populated regions in Nigeria. The results indicate that there is a significant trend for Lagos rainfall data, implying that precipitations have systematically increased over time in this city. Additionally, the seasonal component is more prominent in the cases of Kano and Kaunda than for Ibadan and Lagos. The findings of this study have relevant policy implications for forecasting, agricultural planning, relating precipitation variability in Nigeria with standard climate drivers, as well as for disaster or risk reduction in the context of climate change, global warming and migration in the post-pandemic era. [ABSTRACT FROM AUTHOR]

Most of the quantitative studies only analyse socioeconomic variables, leaving aside psychological aspects or generational integration related factors. This approach has exhibited low explicative power of the problem. The main objective of this investigation is to obtain the weight that the socioeconomic, psychological and generational integration process variables have over agricultural succession through structural equations. The data was obtained from 126 horticulture farms from Zarcero, Costa Rica. The results indicate that the generational integration process is the determining factor of generational succession, which is accurately explained through the psychological factors exposed by the theory of planned behaviour. The socioeconomic variables add little to the understanding of the problem. These results demonstrate that regardless of the socioeconomic conditions, the key determining factor is the integration of the sons/daughters in the farm activities.

Copyright of Revista Mexicana de Ciencias Agrícolas is the property of Instituto Nacional de Investigaciones Forestales, Agricolas y Pecuarias and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Agriculture is a crucial and strategic sector for developing countries. The agricultural sector in Turkey has been suffering from regression in recent years due to several reasons. In an attempt to reverse this process, we analyze the cultivation possibilities of high profit-margin crops in Turkish lands and develop a ranking among eight alternative crops. To perform a comprehensive analysis encompassing several dimensions, three MCDM methods are utilized; namely fuzzy AHP to determine the weights of evaluation criteria, and TOPSIS and PROMETHEE to develop a ranking among the crop alternatives. The crop alternatives are evaluated against several economic, technical, social and environmental criteria. The results favor the cultivation of soy bean, goji berry and buckwheat, while tamarind appears to be the least favored crop among the considered alternatives. The analysis results are enhanced with a sensitivity analysis. [ABSTRACT FROM AUTHOR]

Copyright of Revista IRRIGA - Brazilian Journal of Irrigation & Drainage is the property of Revista IRRIGA (Brazilian Journal of Irrigation & Drainage) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With advancements in the mechanisation of sugarcane farming, studies have been fundamental to improving the process—from soil preparation to harvest. Faced with increasing challenges of economic scenarios, alternatives should be sought aimed at optimising resources, reducing costs, improving operational efficiency, logistics, among others. Planting is one of the main agricultural operations, any deviation in this phase harms the crop during the crop cycle, so planning in advance the area to be planted is essential for better results. Analysis of better planting scenarios prior to harvest combined with the use of autopilot requires knowledge of the systematisation areas and skilled labour to guarantee the quality of the process and reduce losses and damages. The objective of this study is to both evaluate and optimise sugarcane planting scenarios based on travel and manoeuvre time, travel distance, number of manoeuvres, and fuel consumption. The study was conducted in the municipality of Tanabi, SP, during the 2013 planting season. The results showed fewer manoeuvres and longer planting lines in the optimised area, increased the availability of the machine and generated possible cost reduction. [ABSTRACT FROM AUTHOR]

Copyright of Revista Ambiente e Água is the property of Revista Ambiente e Agua and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Terra Latinoamericana is the property of Sociedad Mexicana de la Ciencia del Suelo A.C. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The estimate of the potential sugarcane productivity through agroclimatic models aids in the agricultural planning of the crops and the quantification of the yield for a given region. For these estimated values to be considered robust there is a need for validating the performance of such models in different areas and agricultural varieties. Hence, the aim of this study was to validate the Agro-Ecological Zone (AEZ) method with fifteen sugarcane varieties in the region of the Vale do São Patrício, state of Goiás, Brazil. We evaluated the data referring to the cane-plant (one-and-a-half-year sugarcane), as well as the first and second sugarcane ratoons (both with one-year cycles) in an irrigated and dry farming system. The productivities obtained in dry farming were corrected due to the occurrence of a water deficit in the crop. The results indicated that the AEZ method presented productivity estimates more satisfactory for the one-year cultivation cycles (ratoon cycles) for all varieties studied, with the model adjusting best to the CTC15 variety (RMSE = 8.70 t ha-1; MAE = 6.05 t ha-1; d = 0.99). [ABSTRACT FROM AUTHOR]

Most of the quantitative studies only analyse socioeconomic variables, leaving aside psychological aspects or generational integration related factors. This approach has exhibited low explicative power of the problem. The main objective of this investigation is to obtain the weight that the socioeconomic, psychological and generational integration process variables have over agricultural succession through structural equations. The data was obtained from 126 horticulture farms from Zarcero, Costa Rica. The results indicate that the generational integration process is the determining factor of generational succession, which is accurately explained through the psychological factors exposed by the theory of planned behaviour. The socioeconomic variables add little to the understanding of the problem. These results demonstrate that regardless of the socioeconomic conditions, the key determining factor is the integration of the sons/daughters in the farm activities. [ABSTRACT FROM AUTHOR]

Copyright of Ege Üniversitesi Ziraat Fakültesi Dergisi is the property of Ege Universitesi, Ziraat Fakultesi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The study presents the projected changes in two important agroclimatic parameters, namely, in the annual sum of active temperatures with a threshold of 10°С (SAT) and in annual total precipitation in Russia and neighboring countries. These changes are estimated using climatic data obtained by averaging the simulation results of 31 CMIP5 global climate models under the RCP4.5 moderate scenario of anthropogenic impact on the terrestrial climate system provided by the Voeikov Main Geophysical Observatory. The changes are evaluated for the current 20 years (2011–2030) as compared to 1981–2000 and for 2034–2053, when the average global air temperature rise will reach 2°С relative to the preindustrial level as compared to 2011–2030. A statistically significant phased increase in the analyzed agroclimatic parameters is noted for the whole territory of Russia in the subsequent periods as compared to the previous ones. The scenario-based calculations reveal that the SAT increase will be more evenly distributed in space, whereas the increase in annual total precipitation will be rather nonuniform. The results may be used for the agricultural planning and for the realization of protection activities, with account of the prediction of the distribution and mass reproduction of agricultural plant pathogens. [ABSTRACT FROM AUTHOR]

The objectives of this research are to determine the optimal agricultural plane to achieve the optimum crop combination that maximizes the profits and the total and net agricultural income of the Al- Rasheed / Hamorabi Farm in Baghdad using L.P technique to reach the production volume that maximizes profit to choose the best plan and to increase the efficiency of the use of available resources at the farm level, to optimize the utilization of natural water resources and to protect them from the risk of attrition and to promote their use by deriving mathematical models for use in water needs assessment, including the application of an agricultural rotation to maintain soil fertility. The research has reached a number of conclusions, perhaps the most important is that the optimal solution using the linear programming method shows us the difference in the optimum crop combination of the actual crop structure of the agricultural crops and their areas. The farm has achieved a much higher income than the resulting income of the actual farm plan, as explained by the resource-based model, the LHS of the model, is less than the available resources that represent the RHS, which means that optimal plans have used fewer resources and given higher net income. From the conclusions reached, we can make a number of recommendations. The most important of these is the implementation of the linear programming technique to determine the extent to which the available resources are invested efficiently, which helps to increase the production in order to achieve the economic efficiency of the farmers and the need to generalize and apply this method in associations with similar environments to determine the optimal use of different productive resources. [ABSTRACT FROM AUTHOR]

Copyright of Informe Gepec is the property of Informe Gepec and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

ABSTRACT This study aimed to develop artificial neural networks for the estimation of tractor fuel consumption during soil preparation, according to the adopted system. The multilayer perceptron network was chosen. As input data: the soil mechanical penetration resistance, the mobilized area by implements, the working gear and the tractor engine speed. The number of layers and neurons varied to form different architectures. The adjustment was verified based on various statistical criteria. The values estimated by the networks did not differ significantly from those obtained experimentally. The conclusion was that the networks showed adequate reliability and accuracy to predicting the fuel consumption in each tillage system, in function of the input data and this can be a useful tool for planning and management of agricultural operations.