Agricultural Planning Research Articles

ĐẶC ĐIỂM CÁC NHÂN TỐ THÀNH TẠO CẢNH QUAN HUYỆN BA BỂ TỈNH BẮC KẠN

The objective of this paper is to analyze the characteristics of the factors that make up the landscape. These factors are the basis for the establishment of landscape maps and landscape assessment for planning as well as sustainable development of agriculture, forestry, and tourism in Ba Be district, Bac Kan province. This is a locality that has attracted great attention from domestic and foreign scientists because it holds the great value of natural resources. The task of establishing landscape maps and landscape classification systems based on factors such as geology, topography, climate, soil, vegetation, etc., is a scientific foundation for regulation. planning and sustainable development of agriculture, forestry, and tourism for this study area.

Agricultural land suitability analysis for an integrated rice–crayfish culture using a fuzzy AHP and GIS in central China

Integrated rice–crayfish (Procambarus clarkii) culture (IRCC) is an effective form of ecological intensification that has been widely and rapidly promoted in China. Agricultural land suitability analysis is one of the key tools for ensuring the effective use of land resources and sustainable development of IRCC. The aim of this study was to assess the land suitability for IRCC in Hubei Province, which has the largest area of rice–crayfish farming in China, using the integrated fuzzy analytic hierarchy process (FAHP) and geographic information system (GIS) approach. The results indicated that ≥ 10 °C accumulated temperature (12.8 %), distance from water bodies (11.5 %), slope (11.2 %), land use (7.4 %), and soil texture (7.3 %) were the fivemost influential determinants of highly suitable areas for IRCC. It was estimated that 6.4 % (11,898.0 km2) of the study area was highly suitable, 11.3 % (21,061.0 km2) was moderately suitable, 2.2 % (4,004.0 km2) was marginally suitable, and 80.1 % (149,026.0 km2) was currently not suitable for IRCC. We also found that the highly suitable areas were mainly distributed in the south-central region of Hubei Province, which were significantly correlated with the existing IRCC areas. About 59.8 % (7,120.5 km2) of the highly suitable areas were available for further IRCC development in Hubei Province, but Qianjiang, Huanggang, Xianning, and Enshi needed to impose a ceiling on IRCC, because the extent of their existing IRCC areas was greater than that of the highly suitable areas. Sensitivity and correlation analyses demonstrated that the suitability results for IRCC were reliable and robust. The results of this study provide useful guidelines in terms of agricultural land use planning and management, enabling the rational and sustainable development of IRCC both locally and nationally.

Productivity and penalty in sugarcane from three meteorological databases in Jataí-GO

The agroecological zone model proposed by FAO (ZAE/FAO) is an important tool in agricultural planning and can be used in Jataí, which has been gaining space in the production of sugarcane. The objective of this study was to compare the observed meteorological databases (BDMEP) with the estimated ones (Xavier and NASA POWER), in the estimation of potential productivity (PP) and attainable (PA) of sugarcane and its penalties for management (PMJ) and water deficit (PDF), using the ZAE/FAO model in the municipality of Jataí - GO. To this end, the BDMEP, Xavier and NASA POWER database, 33 years old (1984-2017), were used. Using BDMEP, Xavier and NASA POWER as input in the ZAE model, we observed an average PP of 259±11, 275±10 and 267±11 Mg ha-1 as, respectively. PA reaches 200 Mg ha-1 when using BDMEP in ZAE, with 50% of the crop year with PA above 180 Mg ha-1. There was an average penalty of 114.01±6.9 for management and 85.67±4.08 Mg ha-1 in stem yield. The meteorological variables of Xavier's database can be used in the ZAE model in the prediction of productivity penalty for water deficit and sugarcane management, with 63% and 88% adjustment to BDMEP, respectively. NASA POWER can be used in the simulation of penalty for failures in sugarcane management, with up to 87% approximation of those observed. Both sources when used in the prediction model assist in agricultural planning.

AN ANALYSIS OF THE IMPACT OF SOME SOCIO-ECONOMIC VARIABLES IN REGULATING THE CULTIVATION AND MARKETING OF THE RICE CROP IN IRAQ

This research was aimed to study the results of the application of agricultural legislation and the variables affecting compliance with such legislation, as well as knowing the role of agricultural legislation in supporting the agricultural policy steps in Iraq. The results showed that the variable of providing loans increases the probability of farmers’ commitment to the governmental agricultural plan to a large extent, amounting to about (28.7), and the variable of the sale price of the crop to the state has an impact on the probability of commitment to the plan at a degree of (1.02) greater than the chances of non-compliance with the agricultural plan, as well as the variable value of the returns achieved under The existence of government support to a degree or a fixed rate of (1) in favor of adhering to the agricultural plan. As for the farmers' decision to market their crops to the state, it was found that the most influential variables were the commitment to the agricultural plan, almost six times (6.25), which will increase the probability of marketing the crop to the state compared to the commercial one. The other variable was the provision of loans with a double degree of about (2.72). The research concluded that paying attention to the aspect of financing and providing agricultural loans to farmers will lead to farmers getting rid of the financial restrictions imposed on them by the private sector. Also, the continuation of providing support will lead to an improvement in the level of farm income for farmers and not to search for other means to obtain income and thus adhere to the government legislation drawn up. The research recommended the need to provide government support and use modern technologies to increase production rates and achieve greater returns for farmers, and the presence of a supervisory body working to ensure that farmers do not violate the imposed government legislation.

Research on the Optimization of "Internet +" Agricultural Economic Integration in the Yangtze River Delta

With the continuous high-quality development of the Internet economy, today's agricultural economy and the combination of the Internet have achieved better economic benefits. "Internet" agriculture should be the update of traditional agriculture, Internet service projects, big information content, Internet of Things technology, cloud computing and other information technologies. Recently, e-commerce, logistics and other software have been used. Give the steps of production and manufacturing, marketing, agricultural service project commodity circulation plan, and its connection and update, complete the professional and intelligent data information of agricultural development, and complete the reasonable layout of human resources expenditure. Give appropriate material wealth and funds to create more efficient agricultural development models and enhance the value of agricultural products. The project analysis report discusses the combination of the Internet and the agricultural economy in the Yangtze River Delta region, as well as various problems, and finally gives preventive measures and suggestions to promote the improvement of the rural revitalization model.

New ridge regression, artificial neural networks and support vector machine for wind speed prediction

For wind energy conversion systems (WECS), forecasting wind speed is crucial for meeting customer demands while monitoring, controlling, planning, and dispatching the electricity production. The goal of the research is to more easily predict wind speed for planning and feasible studies of wind farms. All data was derived from weather stations in a windy city in a mountain area for one month within five years. A kernel ridge regression (RR) model is suggested, and the results are compared with two reference prediction models of support vector machines (SVM) and artificial neural networks (ANN), to validate the model's efficiency for three different predicting horizons (1-h, 12-h, and 24-h ahead). The root means square error (RMSE), and root mean square (R2) are utilized to assess the effectiveness of a prediction model. Using one layer and 30 neurons, the optimum outcome was achieved with an RMSE of 0.264 and a value of 0.811. Tests revealed that 70%–30% for training and testing yields the lowest RMSE of 1.244 compared to 1.874 for 60%–40%. A study of ANN, SVM, and ridge regression found that predictions made with the RR provided the most precision in comparison to the R2 and RMSE values. This study's relevance lies in its ability to forecast wind speeds and for this reason, ridge regression using mutual information feature selection performs better than other methods when trying to forecast wind velocity. Cost and risk management in wind power planning will benefit from this research.

Perspectives on Disparities and Equity in Cancer Outcomes: A Call to Action.

Despite remarkable progress in cancer prevention, treatment, and survivorship in the United States over the past 3 decades, there remain considerable disparities in cancer incidence and mortality among various groups based on race, ethnicity, and other social determinants of health. For most cancer types, African Americans have the highest mortality rate and lowest survival rate of any racial and ethnic group. Here the author highlights some of the factors responsible for cancer health disparities and proposes that cancer health equity is a fundamental human right. These factors include inadequate health insurance coverage, medical mistrust, lack of diversity in the workforce, and social and economic exclusion. Recognizing that health disparities do not exist in isolation but are part of a complex set of problems associated with education, housing, employment, health insurance, and community structures, the author contends that the challenge cannot be solved by public health measures alone but would require a multifaced and coordinated approach involving several sectors of the economy-business, schools, financial institutions, agriculture, and urban planning. Several immediate and medium-term action items are proposed to set the foundation for sustained efforts that would have an impact in the long term. These include developing culturally tailored interventions through community engagement to enhance uptake of cancer screening and clinical trials among racial and ethnic minorities and other medically underserved patients, expanding access to equitable and affordable quality health care by increasing access to health insurance, and prioritizing investment in early-career cancer researchers to enhance diversity and improve equity in the workforce.

Statistical Analysis of Rainfall Distribution in Biratnagar, Nepal: A case study

Rainfall is a key component in the construction of many engineering projects, including canals, bridges, culverts, and road drainage systems. The primary goal of this study is to ascertain the annual, monthly and seasonal fluctuations in rainfall in Biratnagar, Nepal over a period of 10 years (2010-2019). The mean, standard deviation, coefficient of variation, skewness, kurtosis, Precipitation Concentration Index (PCI), normal rainfall along with Mann-Kendall test were calculated to check the rainfall variations. The annual data showed the biggest varieties in rainfall, with an average annual rainfall of 1542.43 mm during a ten years period. Furthermore, an erratic monthly rainfall pattern was found by the non-parametric Mann-Kendall test. However, the test value for each month was non-significant at the 5% level of significance. We also found that the rainfall is least in the winter and most during monsoon season. Based on these calculations, It is seen that the pattern of rainfall is inconsistent or unpredictable. However, present study will aid in the understanding of rainfall variations and trends, assisting in the management of water resources, ecosystems, irrigation, sustainable agricultural planning, engineering structural design, and other areas.

Farmer and Adviser Perspectives on Business Planning and Control in Mediterranean Agriculture: Evidence from Argolida, Greece

Adoption and implementation of business planning and control methods is essential to efficiently and effectively allocate resources for producing food within sustainable agricultural systems in the Mediterranean Basin. To investigate this issue, an analysis of twenty-eight representative farming businesses from the National Farm Accountancy Data (FADN) for the region of Argolida (Greece) was undertaken, together with a survey of twenty agricultural advisers (ten agronomists and ten accountants) from the representative associations of agronomists and accountants for the area under study. Results demonstrate farmers’ needs for enhancing managerial competencies, particularly the systematic use of planning and control methods, for effective decision making and strategic design. The research illustrates how these methods can serve as a tool to enhance efforts towards a more sustainable approach to farming. Advisers are encouraged to diversify their role from a purely market driven approach to a role which includes guidance and scientific advice for supporting the sector’s needs for sustainable development. This will support farmers’ decision-making based on the latest scientific knowledge and use of available data to enhance the sustainability of these important agricultural systems.

Developing a new model for ecological capability evaluation of irrigated lands in Firouzabad Township, Iran

Agricultural planning is a very complex task, since there are numerous goals, which should be achieved simultaneously, and various components and elements, which must be considered at the same time. The process of agricultural suitability evaluation for crop production requires specialized geo-environmental information and the expertise of a computer scientist to analyze and interpret the information. The main objective of this paper is to test a new model (based on Iranian ecological and FAO models) for ecological capability evaluation with geometric mean evaluation for better planning management of irrigated lands. Next, the proposed method was verified and compared with other well-known methods such as the Iranian ecological model with Boolean logic, arithmetic mean, and WLC. To test the models, we used the normalized difference vegetation index (NDVI). The test results indicated that the method revised by geometric mean evaluation (overall accuracy %=95 and Kappa coefficient =0.91) was the best among the used methods, and the arithmetic mean method (overall accuracy %=46 and Kappa coefficient =0) had the lowest accuracy. Thus, this method (Geometric mean evaluation) has high flexibility in locating agricultural lands. Overall, this study can be used as a basic method to evaluate ecological suitability for other regions with similar conditions owing to its simplicity and high precision.

Exploring the spatial patterns of soil salinity and organic carbon in agricultural areas of Lesvos Island, Greece, using geoinformation technologies

The salt-affected soils national map of Greece was recently made available within the initiative of the Global Soil Partnership (GSP) of Food and Agriculture Organization of the United Nations FAO. The present study explores the development of higher resolution soil property maps included in this national scale product adopting a modified version of the FAO methodology and a logistic regression (LR) method based on ground and satellite data. Furthermore, it also investigates the correlation between saline soils and soil organic carbon (SOC) using geospatial analysis methods. The island of Lesvos in Greece has been selected as a case study. A probabilistic model for saline soils in the agricultural land of Lesvos is produced by exploiting geoinformation technologies. As a result, the spatial distribution of saline soils in the croplands of Lesvos was obtained. Indicatively, areas with p > 0.80 for the occurrence of saline soils accounting for ∼20% of a total area of 169.51 km2 of the croplands in Lesvos. The Nagelkerke R2 coefficient showed that the probabilistic model interprets 11.3% of the variance of the dependent variable from the independent factors. The model accuracy was assessed adopting the receiver operating characteristic (ROC) curve, which showed a reasonable adaptability with area under curve (equal to 0.73). The methodological approach proposed herein can support decision-making on agricultural land protection and planning activities which are key priority today due to environmental instability, food security, and climate change.

Spatiotemporal Rainfall Variability and Trend Analysis of Shimsha River Basin, India.

Karnataka state has the second highest rainfed agricultural land in India, where agricultural output relies heavily on rainfall. The Shimsha basin, a sub-basin of Cauvery in the state, comes under a semi-arid region and predominantly consists of rainfed agricultural land. Rainfall patterns have changed dramatically with time resulting in frequent floods and droughts. Understanding the spatiotemporal distribution of rainfall and its change patterns in the area would benefit sustainable agriculture planning and water resources management practices. The current study aims to determine the variability and trend in rainfall. The daily rainfall data of the Shimsha basin from 1989 to 2018 is collected, and the annual, seasonal, and monthly rainfall totals and the number of rainy days are derived. All the time series are subjected to statistical methods to examine rainfall variability and trend. The mean, standard deviation, coefficient of variation (CV), and Standardized Anomaly Index are used for the preliminary and variability analysis, while the coefficient of skewness and kurtosis are used to understand the rainfall distribution characteristics. The homogenous and serially independent series are identified by homogeneity and serial correlation tests. The trend in the homogenous and serially independent series is identified by Mann-Kendall and Spearman's rank correlation tests, while the magnitude of the trend is quantified using the Sen's slope technique, and the trend change point is evaluated using the sequential Mann-Kendall test. Based on the study, the average rainfall in the study area is 801.86mm, with CV ranging from 43.3 to 22.27%. The southwest monsoon (SWM) season brings the greatest rain to the basin, followed by the post-monsoon (PM), summer, and winter seasons. In the annual time frame, except one station, all other stations have shown significant or insignificant increasing trends. The seasonal rainfall has shown insignificant rising trends during the summer and winter seasons while insignificant increasing and decreasing trends during the PM season. The SWM season has indicated significant increasing trends, insignificant increasing and decreasing trends. Overall, the study area has noticed an increased annual and seasonal rainfall except for the post-monsoon season, during which the rainfall showed a considerable decline. The findings of the study are helpful in water resource management, agricultural planning, and socioeconomic development in the study area.

Assessing governability of agricultural systems: Municipal agricultural planning in Metro Vancouver, Canada

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.

A novel approach to vulnerability assessment for adaptation planning in agriculture: An application to the Lower Bhavani Irrigation Project, India

The global discourse on climate change adaptation in various sectors has largely taken a top-down approach. However, impact analysis based on top-down approach with uncertainties at every stage of the process may lead to poor adaptation responses and hence there is a need for an alternative to this conventional approach. This study aims to implement a bottom-up, risk-based approach to vulnerability assessment in the agriculture sector, by adapting the Climate Risk Informed Decision Analysis (CRIDA) approach. This paper explains the risks that climate variability and change pose to the agricultural system, with Lower Bhavani Irrigation Project in the South India as a case study. The climate stressors of aridity index and climate variability were incremented or perturbed to develop different future climate scenarios, and the changes in paddy yield and supply–demand ratio were examined.The supply–demand relationship in the system was highly vulnerable to the aggregate effects of climate variability and change, whereas the risk to paddy yield was low. Since the change in crop water demand induces risk to the system, we recommend planning demand management measures such as changes in cropping calendar, system of rice intensification, alternate wet and dry irrigation, deficit irrigation etc. to minimize the impacts. As the range of projections in climate variablesis large, and the yield responses are low, we recommend having both robust and flexible adaptation plans. The bottom-up approach presented here provides solutions relevant to the system,hence it is highly suitable and recommended for local adaptation planning in agriculture sector.

GEOMEMBRANE BASINS DETECTION BASED ON SATELLITE HIGH-RESOLUTION IMAGERY USING DEEP LEARNING ALGORITHMS

Abstract. Agriculture is a very important economic sector in Morocco, which requires a set of tools to improve agricultural production. Among these tools, the use of geomembrane basins. The latter is of great importance in smart farming planning, management practices or even in livestock use. In this context, this study evaluates a recognition and classification of the geomembrane basin using remote sensing satellite images; based on the Yolov3 deep learning neural network. This paper first adjusts the network model to make it suitable for detecting small targets on remote sensing images, then uses the k-means algorithm to calculate the grid size of the Yolo network model suitable for geomembrane basins, then uses yolov3 to train the data that makes up the satellite remote sensing imagery. The network model for the detection of the geomembrane basins is obtained by the test phase. Finally, the geomembrane basin detection model adapted to the remote sensing image is obtained in the validation phase. Through the research and analysis of the experimental results, it can be seen that this method effectively detects the geomembrane basins in the remote sensing images and ensures the high detection accuracy of the experimental results, which gave us an accuracy of 75%.

Retracted: Application of Data Mining Algorithm in Agricultural Products Logistics Network Planning

Retracted: Application of Data Mining Algorithm in Agricultural Products Logistics Network Planning

Improving the spatial and temporal estimation of ecosystem respiration using multi-source data and machine learning methods in a rainfed winter wheat cropland

The investigation of ecosystem respiration (RE) and its vital influential factors along with the timely and accurate detection of spatiotemporal variations in RE are essential for guiding agricultural production planning. RE observation in the plot region is primarily based on the laborious chamber method. However, upscaling the spatial-temporal estimates of RE at the canopy scale is still challenging. The present study conducted a field experiment to determine RE using the chamber method. A multi-rotor unmanned aerial vehicle (UAV) equipped with a multispectral camera was employed to acquire the canopy spectral data of wheat during each RE test experiment. Moreover, the agronomic indicators of aboveground plant biomass, leaf area index, leaf dry mass as well as agrometeorological and soil data were measured simultaneously. The study analyzed the potential of multi-information for estimating RE at the field scale and proposed two strategies for RE estimation. In addition, a semiempirical, yet Lloyd and Taylor-based, remote sensing model (LT1-NIRV) was developed for estimating RE observed across different growth stages with a small margin of error (coefficient of determination [R2] = 0.60–0.64, root-mean-square error [RMSE] = 285.98–316.19 mg m−2 h−1). Further, five machine learning (ML) algorithms were utilized to independently estimate RE using two different datasets. The rigorous analyses, which included statistical comparison and cross-validation for estimating RE, confirmed that the XGBoost model, with the highest R2 and lowest RMSE (R2 = 0.88 and RMSE = 172.70 mg m−2 h−1), performed the best among the evaluated ML models. The LT1-NIRV model was less effective in estimating RE compared with the other ML models. Based on this comprehensive comparison analysis, the ML model can successfully estimate variations in wheat field RE using high-resolution UAV multispectral images and environmental factors from the wheat cropland system, thereby providing a valuable reference for monitoring and upscaling RE observations.

Sustainable agricultural lands management by analyzing and predicting urban growth—a case study of Mitidja Plain, Algeria

The suitability of agricultural lands is basic data for both sustainable agricultural planning and optimal orientation of future urbanization projects. Indeed, unplanned urban expansion leads to many social and ecological problems. The prediction of urban expansion will strongly help the decision makers and local authorities to create a suitable future urban planning while preserving the regional ecosystem and especially the agricultural lands. Using multitemporal satellite images acquired in 1997, 2001, and 2013 over the Mitidja plain in Algeria, the aim of this study is the analysis of the land use and land cover change (LULCC) spatio-temporal distributions from 1997 to 2013 to model and then predict the future LULCC in 2025. The cellular automata Markov chain model (CA-Markov) that we developed is used for LULCC prediction with a focus on urban sprawl and agricultural lands degradation over the Mitidja plain. Once calibrated and validated, the CA-Markov model generates land suitability maps that indicate the best locations for urban expansion while safekeeping the agricultural lands. The results are interesting and present valuable tools for urban planners and policymakers to ensure the sustainability management of agricultural areas over the study region.

Incorporating Seasonality into an Agroeconomic Model of Ethiopia

Ethiopia’s agriculture-based economy generates highly seasonal outputs, with most production occurring during the long-rains Meher season and a lesser amount during the short-rains Belg season. Despite numerous studies detailing the economic impacts of Meher precipitation, there is relatively little consideration of other seasonal or subseasonal climate impacts in Ethiopia, with most economic models only considering aggregated annual-scale production to simulate the Ethiopian economy. This paper serves to address both seasonal and subseasonal effects of climate using a partial equilibrium agroeconomic model of Ethiopia. First, an annual-scale model is disaggregated into a seasonal time step, corresponding to the two major cropping seasons in Ethiopia. Second, the effect of yield gains from a subseasonal forecast–based planting strategy is considered in the updated model. We found that crop yields and corresponding economic indicators varied widely by season and location, and that subseasonal forecast–based management strategies can, on average, increase gross domestic product (GDP), per capita calorie consumption, and lead toward reduced poverty, warranting the inclusion of seasonal and subseasonal processes in economic models and agricultural planning.

An improved Wavenet network for multi-step-ahead wind energy forecasting

Accurate multi-step-ahead wind speed (WS) and wind power (WP) forecasting are critical to the scheduling, planning, and maintenance of wind farms. Previous forecasting methods tend to focus on improving forecast accuracy by integrating different models and disaggregating data while neglecting the forecasting ability of basic models. In addition, traditional multi-step-ahead output strategies have limitations that constrain the forecasting capability of models. To overcome the above challenges, this study proposes a novel forecasting model called ED-Wavenet-TF. It adopts two Wavenet networks as Encoder and Decoder connected by the multi-head self-attention mechanism. And, teacher forcing is used as the multi-step-ahead output strategy for WS and WP forecasting. In the training phase, ED-Wavenet-TF uses a portion of the actual data to correct the errors at the intermediate forecasting steps, while in the forecasting phase, it runs through an inference loop to make forecasts. In this study, two WS datasets and two WP datasets are used to validate the performance of ED-Wavenet-TF with univariate input. The results show that compared with Wavenet, the symmetric mean absolute percentage error of ED-Wavenet-TF at four forecasting steps is lower by at least 4.8577% on average for the WS datasets and 8.9463% on average for the WP datasets. The advantages of ED-Wavenert-TF over ten comparable models are confirmed by four evaluation indicators and the Harvey, Leybourne, and Newbold statistical hypothesis test. Moreover, ED-Wavenet-TF is extended to make multi-step-ahead forecasts with multivariate inputs, whose effectiveness is demonstrated on another open WS dataset.