Agricultural Resource Management Research Articles

Deficit Irrigation-Based Improvement in Growth and Yield of Quinoa in the Northwestern Arid Region in China

Given the current global water scarcity issues, which particularly affect arid regions such as northwestern China, it is crucial to find crop planting patterns that result in efficient water resource utilization. Quinoa, as a drought-resistant and highly nutritious crop, has garnered significant attention from agricultural researchers in recent years. From 2019 to 2020, a series of experimental studies were conducted under non-mulching drip irrigation conditions to investigate the growth adaptability and the response to different irrigation levels of quinoa in an arid region in northwestern China. A comparative analysis of quinoa’s dry matter accumulation, yield, thousand-grain weight, harvest index, and water use efficiency under varying irrigation levels revealed that increasing irrigation significantly enhanced quinoa’s dry matter accumulation and yield. By optimizing the irrigation strategies, we found that the water-saving practice of initiating moderate irrigation in the sensitive water-demanding stages (flowering and fruiting) of quinoa increased the yield. The experiment results showed that, in 2020, the optimal irrigation amount was 3675 m3·ha−1 during a 14-day irrigation cycle, meeting quinoa’s growth requirements while improving water resource utilization efficiency. This study not only provides a scientific basis for the efficient cultivation of quinoa in the arid regions of northwestern China, but also offers new insights into and technical support for agricultural water resource management in the region, contributing to the sustainable development of agriculture in arid areas.

Predictive Modeling of Extreme Weather Forecasting Events: an LSTM Approach

For a variety of industries, including agriculture, water resource management, and flood forecasting, accurate rainfall prediction is crucial. The purpose of this research work is to improve rainfall forecast system by employing the Long Short-Term Memory (LSTM) based system. The LSTM utilized in the aforementioned study made predictions by using meteorological input variables such as temperature, humidity, and rainfall. Numerous elements affect the LSTM network's performance, such as the kind and volume of data, the suitability of the model architecture, and the tuning of hyperparameters. The dataset used for model training spans from January 2015 to December 2021 and includes rainfall data collected from the Zonal Agricultural Research Station (ZARS), Shenda Park, Kolhapur. Prior to model training, the input data undergoes rigorous preprocessing. This preprocessing involves data correction, achieved through moving averages, followed by feature scaling and normalization methods. These steps are crucial to align the dataset with the unique capabilities of the LSTM model. The total dataset has a R squared (R2) value 0.23517 and a mean squared error (MSE) value 92.1839, according to the simulated findings. These metrics affirm the robust performance of the LSTM model, suggesting a high probability of accurate rainfall predictions, particularly in non-linear and complex scenarios. Decision-makers in flood predictions, agriculture, and water resource management will find the knowledge gathered from this study to be useful. They emphasize how crucial it is to use cutting-edge techniques like LSTM to increase rainfall forecast accuracy and guide strategic planning in associated industries.

Rising risks of hydroclimatic swings: A large ensemble study of dry and wet spell transitions in North America

Hydroclimatic swings between dry and wet spells are increasing globally, raising concerns due to their severe impacts on society, agriculture, infrastructure systems, and water resource management. These extremes and their projected changes are traditionally assessed in isolation, which can underestimate associated risks and future resilience and adaptation plans. This study investigates climate change projections of such lagged compound dry and wet spells across North America using a single model initial-condition large ensemble (SMILE). Three dry-wet spell indices are merged into an integrated indicator to provide a comprehensive perspective on changing risks. We apply an ensemble pooling approach using the Canadian Regional Climate Model version 4 Large Ensemble (CanRCM4-LE) to enhance sample size for index estimation. Results suggest that hydroclimatic swings across North America are expected to become more frequent and intensified in a warmer climate. Trends of spatial fraction and aggregation during transitions between dry and wet spells indicate future water resource management challenges. Hot spots for intensified transitions with higher frequency, such as Northern Central America, and the southern part of Eastern and Western North America, including Mexico and the state of California, overlap with larger spatial extent and higher aggregation during transitions. The seasonal analysis of spatial characteristics indicates winter may become wetter and summer drier at higher warming levels, potentially intensifying transitions between dry and wet extremes. Clustered hydroclimatic swings may lead to severe environmental, hydrological, and socio-economic consequences, necessitating appropriate mitigation and adaptation measures.

Assessing sensory attributes and quality of lettuce from open field, greenhouse, and controlled environment production systems

Lettuce is a popular, low-calorie, nutrient-rich leafy vegetable known for its health benefits and bioactive compounds. The transition from open-field to controlled-environment agriculture has revolutionized lettuce production, enabled year-round cultivation while provided advantages such as enhanced environmental management, diminished pest challenges, and improved crop quality. This study assessed the consumers’ perception of quality attributes of Eden lettuce from three production systems: open field, greenhouse, and controlled environment. Sensory evaluation encompassed the assessment of attributes such as colour, flavour, texture, and nutrition, with panellists evaluating lettuce samples from each production system, and the Kruskal-Wallis H test was used to analyse the data. Controlled environment cultivation showed the highest texture intensity, with significant variations in sweetness, sourness, bitterness, and astringency among production systems. Aroma and aftertaste remained relatively consistent across different systems. Acceptability levels of sensory attributes varied among lettuce samples from different production systems. Controlled environment lettuce received the highest acceptability ratings for texture, flavour, and colour, while notable variations were observed in colour acceptability among production systems. Taste attributes varied among production systems, with open field lettuce displaying diverse taste profiles, greenhouse lettuce leaning towards bitterness, and controlled environment lettuce emphasizing sweetness. Texture attributes also differed, with open field exhibiting balanced profiles, greenhouse yielding firmer textures, and controlled environments emphasizing moderate firmness. In conclusion, the choice of production system has a substantial impact on lettuce quality and sensory attributes. Controlled environment cultivation consistently yielded lettuce with superior attributes, while greenhouse cultivation led to firmer textures and specific taste profiles. On the other hand, open field cultivation exhibited a broader range of taste attributes. These findings provide valuable insights for agricultural practices, resource management, and quality control in lettuce cultivation, offering opportunities for growers and consumers to make informed choices based on their preferences and requirements.

AN IN-DEPTH ANALYSIS OF ARTIFICIAL INTELLIGENCE APPROACHES FOR RAINFALL PREDICTION

Natural disasters and floods brought on by heavy rainfall pose serious threats to human health and lives every year on a global scale. The intricacy of meteorological data makes it difficult to provide accurate rainfall predictions, despite their critical importance in nations like India where agriculture is the primary occupation. Rainfall forecasting has recently benefited from Artificial Intelligence (AI) developments such as Deep Learning (DL) and Machine Learning (ML) techniques. This article provides a comprehensive survey of recent studies that use AI techniques for rainfall prediction, analyzing them based on the ML algorithms and DL methods used, organized by publication year. The findings show that DL approaches are more effective than traditional ML methods and shallow neural network models. This research is important as it has significant impacts on agriculture, disaster preparedness, and water resource management. Finally, it outlines future research directions for further advancements in rainfall prediction through AI methodologies.

Prognostication of Weather Patterns using Meteorological Data and ML Techniques

In the field of modern weather prediction, the accurate classification is essential, impacting critical sectors such as agriculture, aviation, and water resource management. This research presents a weather forecasting model employing two influential classifiers random forest and technique based on gradient boosting, both implemented using the Scikit-learn library. Evaluation is based on key metrics including F1 score, accuracy, recall, and precision, with Gradient Boosting emerging as the superior choice for precipitation prediction. The study examines the performance of Random Forest Regression, Gradient Boosting Regression, and Radial Basis Function Neural Network in forecasting precipitation, drawing on prior research that demonstrated the superiority of the Random Forest algorithm in terms of accuracy and speed. Ensemble methods, particularly the Voting Classifier, a fusion of Random Forest and Gradient Boosting, outperform individual models, offering a promising avenue for advancing weather classification.

Research on precise phenotype identification and growth prediction of lettuce based on deep learning

In recent years, precision agriculture, driven by scientific monitoring, precise management, and efficient use of agricultural resources, has become the direction for future agricultural development. The precise identification and assessment of phenotypes, which serve as external representations of a crop's growth, development, and genetic characteristics, are crucial for the realization of precision agriculture. Applications surrounding phenotypic indices also provide significant technical support for optimizing crop cultivation management and advancing smart agriculture, contributing to the efficient and high-quality development of precision agriculture.This paper focuses on lettuce and employs common nutritional stress conditions during growth as experimental settings. By collecting RGB images throughout the lettuce's complete growth cycle, we developed a deep learning-based computational model to tackle key issues in the lettuce's growth and precisely identify and assess phenotypic indices. We discovered that some phenotypic indices, including custom ones defined in this study, are representative of the lettuce's growth status. By dynamically monitoring the changes in phenotypic traits during growth, we quantitatively analyzed the accumulation and evolution of phenotypic indices across different growth stages. On this basis, a predictive model for lettuce growth and development was trained.The model incorporates MSE, SSIM, and perceptual loss, significantly enhancing the predictive accuracy of the lettuce growth images and phenotypic indices. The model trained with the reconstructed loss function outperforms the original model, with the SSIM and PSNR improving by 1.33% and 10.32%, respectively. The model also demonstrates high accuracy in predicting lettuce phenotypic indices, with an average error less than 0.55% for geometric indices and less than 1.7% for color and texture indices. Ultimately, it achieves intelligent monitoring and management throughout the lettuce's life cycle, providing technical support for high-quality and efficient lettuce production.

Mapping the Progress and Direction of Sustainable Agriculture Research in Indonesia: A Bibliometric Analysis Perspective

This study presents a comprehensive bibliometric analysis aimed at mapping the progress and direction of sustainable agriculture research in Indonesia. With a focus on addressing environmental, social, and economic challenges while ensuring food security and agricultural productivity, sustainable agriculture has emerged as a critical area of focus globally. In Indonesia, a country rich in agricultural diversity and natural resources, sustainable agricultural practices are paramount to mitigate the impact of climate change, preserve biodiversity, and enhance the livelihoods of farmers and rural communities. Through systematic collection and analysis of relevant publications from academic databases, including Web of Science, Scopus, and Google Scholar, this study identifies key thematic areas, trends, collaboration networks among researchers, and potential research gaps within sustainable agriculture research in Indonesia. The analysis spans from 1982 to 2023, covering a wide range of topics such as agroecology, organic farming, precision agriculture, and sustainable resource management. Results reveal a significant impact of Indonesian research contributions, highlighting collaborative efforts and interdisciplinary approaches towards addressing complex agricultural sustainability challenges. The study provides valuable insights for policymakers, researchers, and stakeholders to prioritize areas for further development, formulate evidence-based policies, and foster interdisciplinary partnerships in promoting sustainable agricultural practices in Indonesia.

Study on the Significance and the Drivers of Indigenous Traditional Knowledge in Agricultural Water Resource Management in Water Scarcity Purulia District of West Bengal

Abstract- The paper focuses on the Indigenous traditional knowledge as a vital resource of society with multidimensional use. We can see the application of this knowledge in the field of agriculture, water resource management, disease treatment etc. Agricultural water resource management is a big challenge in the drought prone and topographically diversified Purulia district. This district is the homeland of different indigenous people. Tribal community dominated Purulia district have its own cultural heritage, still today the indigenous people are using their ancestor’s knowledge in different arena of life. This article tries to find out the different traditional and local methods, techniques and tools of agricultural water resource management and their significance in the field of efficient water resource management, it also focuses on the factors which attempt to drive away this local knowledge from grass root level.

Water Footprint Assessment of Agricultural Crop Productions in the Dry Farming Region, Shanxi Province, Northern China

Exploring the crop production water footprint and their driving factors is of significant importance for management of agricultural water resources. However, how do we effectively assess the total agricultural water consumption and explore the significance of their driving factors, i.e., population, economy, and agricultural production conditions, using a backpropagation neural network (BPNN)? It is still ambiguous. Water consumption for crops during the growing season is explicitly explored by way of water footprint indicators (green water footprint, WFPg, and blue water footprint, WFPb). This study provides new insights into the factors driving the changes in crop production water footprint in Taiyuan City over the period of 2005–2021. Simulations of crop evapotranspiration using the CROPWAT model were quantified. The results showed that Taiyuan City has a low crop yield level below the average level of China, with the highest crop yield in maize. The crop production water footprint in Taiyuan City showed a non-linearly decreasing trend over time. The average annual crop production water footprint was 187.09 × 103 m3/kg in Taiyuan City, with the blue water footprint and green water footprint accounting for 63.32% and 36.68%, respectively. The crop production water footprint in the west and north of Taiyuan City was significantly higher than those in other areas, accounting for 42.92% of the total crop production water footprint. Oilseed crops contributed most to the total crop production water footprint, accounting for 47.11%. The GDP and total sown area of crops were more important for the changes in WFPb. Agricultural machinery power and agriculture-to-non-agriculture ratio were more important for the changes in WFPg. Agricultural machinery power and GDP were more important for the changes in IWFP. In-depth analysis of the factors driving the changes in crop production water footprint is dramatically important for agricultural decision makers to mitigate water resource pressure in Taiyuan City.

Gender roles and ethnicity in agricultural water management: a case study of Tinh Bien District, An Giang Province, Vietnam

Women form a crucial part of Vietnam’s agricultural labor force and contribute to national economic development. Still, often the nature of their contribution is inadequately recognized by broad sectors of society. Due to entrenched gender roles and norms, women’s influence over agricultural activity decision-making at the household and institutional levels is limited and poorly acknowledged. This paper explores women’s participation at a household and local level in agricultural water resources management. This research draws upon field studies conducted in Tinh Bien district, An Giang province, Vietnam, a predominantly wetlands environment but with some prominent low hills that present different challenges for water management. The study employed focus group discussion, in-depth interviews, and participant observations to gather data at households and higher levels of governance. The findings highlight that despite being central to socio-economic production across multiple aspects of water wetlands management, women’s voices are overlooked for inclusion within management institutions. It was noted that Khmer minority women tend to be better represented at the household level than Kinh women, but both Khmer and Kinh women’s decision-making power over wetlands and water management at higher institutional levels is still limited.

Climate variability, trend, and associated risks: Tana sub-basin, Ethiopia

Abstract The study focused on analyzing the variability and trends of climate parameters in the Tana sub-basin. Various statistical methods and indices were employed to assess precipitation and temperature patterns in the region. The findings indicated a statistically non-significant increasing trend in rainfall across the sub-basin, with values ranging from 1.64 to 5.37 mm/year. In terms of temperature, there was an increasing trend observed, but it was also not statistically significant. The seasonality index ranged between 0.87 and 1.03, indicating different rainfall distribution patterns. In 36.69% of the sub-basin, rainfall occurs in marked seasonal patterns with a long dry season, and the remaining (63.31%) is concentrated in 3 or fewer months, indicating a different rainfall distribution pattern. In addition, the study assessed the precipitation concentration and found that 57.5% of the rainfall data exhibited a strong irregular concentration, 41.5% showed an irregular concentration, and 1% exhibited a moderate concentration. The study underscores the presence of climate variability and trends in the Tana sub-basin, emphasizing the need to align agricultural and water resource management practices with the observed climate variability.

Spatial Insights into Drought Severity: Multi-Index Assessment in Małopolska, Poland, via Satellite Observations

This study focuses on the assessment of drought severity, employing a comparative analysis between the normalized multi-band drought index (NMDI; calculated using Sentinel-2 imagery) and the combined drought indicator (CDI). The research aims to pinpoint the most accurate and reliable remote sensing techniques, which reflect ground-based measurements, thereby enhancing the precision of drought monitoring systems. By investigating the specific area of Małopolska, the study not only contributes to the global knowledge base on drought assessment methodologies but also addresses the regional needs for improved drought management practices. Through an exhaustive analysis correlating satellite-derived indices with in situ meteorological data, this research elucidates the potential of integrating NMDI and CDI for a comprehensive understanding of drought dynamics in Małopolska. In particular, the indices depict different dynamics of drought levels, as well as the location of regions more prone to its occurrence. The findings are poised to advance drought monitoring capabilities, offering significant insights for agricultural sustainability and water resource management in the region.

A Novel Fusion-Based Methodology for Drought Forecasting

Accurate drought forecasting is necessary for effective agricultural and water resource management and for early risk warning. Various machine learning models have been developed for drought forecasting. This work developed and tested a fusion-based ensemble model, namely, the stacking (ST) model, that integrates extreme gradient boosting (XGBoost), random forecast (RF), and light gradient boosting machine (LightGBM) for drought forecasting. Additionally, the ST model employs the SHapley Additive exPlanations (SHAP) algorithm to interpret the relationship between variables and forecasting results. Multi-source data that encompass meteorological, vegetation, anthropogenic, landcover, climate teleconnection patterns, and topological characteristics were incorporated in the proposed ST model. The ST model forecasts the one-month lead standardized precipitation evapotranspiration index (SPEI) at a 12 month scale. The proposed ST model was applied and tested in the German federal states of Brandenburg and Berlin. The results show that the ST model outperformed the reference persistence model, XGBboost, RF, and LightGBM, achieving an average coefficient of determination (R2) value of 0.845 in each month in 2018. The spatiotemporal Moran’s I method indicates that the ST model captures non-stationarity in modeling the statistical association between predictors and the meteorological drought index and outperforms the other three models (i.e., XGBoost, RF, and LightGBM). Global sensitivity analysis indicates that the ST model is influenced by a combination of environmental variables, with the most sensitive being the preceding drought indices. The accuracy and versatility of the ST model indicate that this is a promising approach for forecasting drought and other environmental phenomena.

An analysis of tax benefits by race and ethnicity for farm households

AbstractThis research simulates the impact on tax liabilities of five tax provisions that provide benefits to farm households by race and ethnicity. Using pooled data from the Agricultural Resource Management Survey (2017–2021), we find that, relative to White non‐Hispanic principal operator farm households, certain business tax benefits are less prevalent and of lower value for Hispanic, African American, and Native American principal operator farm households due to differences in income and investment levels. Household tax credits are significantly higher for Hispanic and Native American principal operator farm households and Asian principal operator farm households have significantly higher total tax benefits from the five provisions examined.

Modeling Evapotranspiration Using SWAT for the Middle Narmada Catchment

This study delves into the hydrological dynamics of a region by analyzing monthly and annual datasets that encompass the intricate interplay among rainfall, potential evapotranspiration (PET), and actual evapotranspiration (ET) using SWAT model for the Gadarwara watershed of middle Narmada catchment. The research identifies a distinct wet season characterized by escalating precipitation from January to June, peaking in July and August. Interestingly, despite heightened rainfall during these months a decline in actual evapotranspiration is discerned, hinting at potential environmental influences. The transition from the wet to the dry season reveals a cyclical pattern culminating in minimal ET values in December. The annual dataset unveils years marked by significant variability in rainfall, ET, and PET, underscoring the region's responsiveness to climatic fluctuations. These insights bear paramount importance for sectors such as agriculture and water resource management, facilitating strategic planning during periods of elevated water availability. Furthermore, the data contributes to the calibration and validation of SWAT, enhancing their reliability in simulating real-world processes. In sum, the comprehensive dataset enhances our understanding of regional hydrology, offering guidance for sustainable water management practices amid evolving climatic conditions.

Innovative approaches to improving the agricultural sector in the era of digitalization of the economy

Conducting a study on this topic is becoming relevant due to the rapid introduction of digital technologies in the agricultural sector, which requires urgent study to effectively address current challenges in agriculture. The purpose of this paper is to analyse innovative approaches to improving the efficiency of economic activity in the agri-food sector. The methods used include the analytical method, classification method, functional method, statistical method, synthesis method, and others. The study found that the use of innovative approaches has significantly improved the productivity and sustainability of the agricultural sector. The use of blockchain technologies has increased trust in the supply chain by providing verified and transparent information on product quality. The analysis of large volumes of data has made it possible to develop specialized solutions and strategies tailored to specific business conditions in different regions. The results show an increase in the resilience of the agricultural sector to changes in economic and climate conditions. The findings of the study highlight the potential of digital transformation as a strategic tool for innovative and sustainable development of the agricultural sector. The study identified key strategies for the future implementation of digital technologies, taking into account the needs of the agricultural sector. The conclusion emphasizes the importance of introducing digital technologies to increase efficiency, improve management and ensure the sustainability of the industry in today’s digital economy. The practical significance of the paper is to analyse and highlight the factors that affect productivity and resource management in agriculture, which serves as a basis for further research and development of specific strategies and recommendations to improve the efficiency of the sector

The Application of a Smart Nexus for Agriculture in Korea for Assessing the Holistic Impacts of Climate Change

Sustainable development involves maximizing the benefits of development while minimizing its consequent effects on the environment. This study uses a water–energy–food nexus framework, the Smart Nexus for Agriculture in Korea (SNAK), to assess the impact of climate change on sustainable resource management in agriculture. The nexus database applied in this study comprises three individual databases related to resources, interlinkages, and resource management scenarios, which include all variables and scenarios of the framework. Different resource management scenarios were evaluated via investigating the interlinkages between resources and quantifying resource consumption and sustainability. The variable selection and application module uses the interlinkage database to quantitatively model how the production and supply of one resource affects the consumption of other resources. The scenario analysis module involves the identification and application of resource management scenarios based on policies for individual resources and climate change. The sustainability evaluation module links the previous two modules to quantify food production, the consumption of food and energy resources, carbon (CO2) emissions, and land use in each scenario. Finally, resource security and economic benefits were considered when estimating the sustainability index of each scenario. The SNAK platform is anticipated to possess the ability to analyze environmental, social, and economic systems grounded in water, energy, and food. It is believed that the platform can optimize the timing and allocation of agricultural resources, leading to the derivation of optimal management scenarios. Furthermore, the platform will utilize water–energy–food linkage assessments to formulate scenario-based policies addressing food demand, water resource utilization, and energy consumption.

Bi-directional prediction of hydrothermal carbonization characteristics of agroforestry and livestock wastes with variable components: Graph learning model-aided waste recycle

Agricultural resource management is essential in present-day society. The hydrothermal carbonization (HTC) is a promising technology for recovering valuable resources from wastes. However, there is a paucity of efficient approaches to predict and comprehend the agricultural waste HTC process. Machine learning modeling approaches have emerged as an important tool for simulating thermochemical reaction processes such as HTC. However, previous studies all limit to predicting products properties of single-component HTC, which are contrary to the complex composition of raw materials, and demonstrate poor generalization performance. Herein, the homogeneity graph data is employed to represent the HTC data with variable components. Then, based on graph encoder-decoder transformer method, a bi-directional prediction model is established to model the HTC process of agroforestry and livestock wastes. In the case of the forward prediction, the model can predict the composition and yield of hydrochar according to the given HTC conditions. The comparisons with the experimental data other than the training and validation datasets show that the prediction accuracy (R2) of ash, carbon and nitrogen contents of the hydrochar are 0.895, 0.908 and 0.840, respectively, which proves the good generalizability of the model. Moreover, owing to the embedding vector, which connects the reactant data with the product data in HTC, the model can also achieve backward prediction of HTC reaction conditions such as raw material composition and reaction temperature. Furthermore, by dimensionality reduction analysis of the embedding vector in the model, the similarity of any two HTC reactions can be quantified. The established HTC-graph neural network (GNN) model shows both high accuracy and good generalization ability in bi-directional prediction of HTC process. Based on the effectiveness of the proposed model and the given evaluation indexes, the corresponding HTC parameters can be determined, which greatly benefits to the resources saving and energy consumption reduction.

Assessing the accuracy of OpenET satellite-based evapotranspiration data to support water resource and land management applications

Remotely sensed evapotranspiration (ET) data offer strong potential to support data-driven approaches for sustainable water management. However, practitioners require robust and rigorous accuracy assessments of such data. The OpenET system, which includes an ensemble of six remote sensing models, was developed to increase access to field-scale (30 m) ET data for the contiguous United States. Here we compare OpenET outputs against data from 152 in situ stations, primarily eddy covariance flux towers, deployed across the contiguous United States. Mean absolute error at cropland sites for the OpenET ensemble value is 15.8 mm per month (17% of mean observed ET), mean bias error is −5.3 mm per month (6%) and r2 is 0.9. Results for shrublands and forested sites show higher inter-model variability and lower accuracy relative to croplands. High accuracy and multi-model convergence across croplands demonstrate the utility of a model ensemble approach, and enhance confidence among ET data practitioners, including the agricultural water resource management community.