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It is vital to provide useful hydrological forecasts for urban and agricultural water management, hydropower generation, flood protection and management, drought mitigation and alleviation, and river basin planning and management, among other things. This paper introduces a simple and flexible hydrological time series forecasting framework. Predicting water levels is crucial, given the need for sustainable environmental management. The prognosis should be reasonable to persuade individuals to take proper precautions. While many methods have been developed to predict water levels, here the effectiveness of two approaches to predicting river water levels was assessed. For this purpose, nine years of data were used, which were divided into input data (2014–2021) and validation data (2022), on water levels in the Morava e Binçës river for the Vitia station, in the form of monthly time series records, to identify the best model among those used and to identify the information necessary for water resource management and hazard control. Models Autoregressive Integrated Moving Average(ARIMA) and Error Trend and Seasonality, or Exponential Smoothing (ETS), were examined using the R package to determine the most accurate. The results indicate the applicability of both models, as evidenced by the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE). The predictive analysis based on historical water levels allowed the identification of distinct periods characterized by high and low water levels between 2022 and 2024, which is important for the area in question due to the numerous flood events occurring here. [ABSTRACT FROM AUTHOR]

Crop price forecasting is difficult in that supply is not as elastic as demand, therefore, supply and demand should be stabilized through long-term forecasting and pre-response to the price. In this study, we propose a Parametric Seasonal-Trend Autoregressive Neural Network (PaSTANet), which is a hybrid model that includes both a multi-kernel residual convolution neural network model and a Gaussian seasonality-trend model. To compare the performance of the PaSTANet, we used daily data from the Garak market for four crops: onion, radish, Chinese cabbage, and green onion, and performed long-term price forecasts for one year in 2023. The PaSTANet shows good performance on all four crops compared to other conventional statistical and deep learning-based models. In particular, for onion, the (mean absolute error (MAE) for the long-term forecast of 2023 is 107, outperforming the second-best Prophet (152) by 29.6%. Chinese cabbage, radish, and green onion all outperform the existing models with MAE of 2008, 3703, and 557, respectively. Moreover, using the confidence interval, the predicted price was categorized into three intervals: probability, caution, and warning. Comparing the percentage of classified intervals about the true prices in our test set, we found that they accurately detect the large price volatility. [ABSTRACT FROM AUTHOR]

Motivated by the comovement of realized volatilities (RVs) of agricultural commodity prices, we study whether multi-task forecasting algorithms improve the accuracy of out-of-sample forecasts of 15 agricultural commodities during the sample period from July 2015 to April 2023. We consider alternative multi-task stacking algorithms and variants of the multivariate Lasso estimator. We find evidence of in-sample predictability but scarce evidence that multi-task forecasting improves out-of-sample forecasts relative to a classic univariate heterogeneous autoregressive (HAR)-RV model. This lack of systematic evidence of out-of-sample forecasting gains is corroborated by extensive robustness checks, including an in-depth study of the quantiles of the distributions of the RVs and subsample periods that account for increases in the total spillovers among the RVs. We also study an extended model that features the RVs of energy commodities and precious metals, but our conclusions remain unaffected. Besides offering important lessons for future research, our results are interesting for financial market participants, who rely on accurate forecasts of RVs when solving portfolio optimization and derivatives pricing problems, and policymakers, who need accurate forecasts of RVs when designing policies to mitigate the potential adverse effects of a rise in the RVs of agricultural commodity prices and the concomitant economic and political uncertainty. [ABSTRACT FROM AUTHOR]

The agricultural industry is critical to the Indian GDP, yet it faces several issues, such as energy, unemployment, and water scarcity. Among these challenges, reliable access to electricity stands out as a crucial factor for implementing smart farming techniques tailored to diverse crops and irrigation patterns. This paper addresses the intricate task of load forecasting in the agriculture sector, employing machine learning and ensemble learning methods. By scrutinizing various agroclimatic and weather characteristics, we pinpoint exogenous features influencing load prediction and prioritize their significance. The study introduces a groundbreaking approach, Temporal Adaptive Ensemble Learning (TAEL), designed to augment accuracy by incorporating temporal dynamics. Six distinct cases, varying in input parameters, are meticulously examined, employing ensemble approaches and benchmarking against Multiple Linear Regression (MLR) and K-Nearest Neighbor (KNN) models. The findings emphasize the vital role of integrating agroclimatic and time series features, showcasing notable enhancements in algorithmic performance across all cases. The proposed TAEL model stands out, achieving a remarkable mean accuracy of 87.1% and a 20.4% MAPE error. This underscores its effectiveness compared to traditional statistical and machine learning approaches, addressing limitations observed in existing studies. The research not only identifies critical factors influencing load prediction but also pioneers a novel approach, TAEL, contributing to the advancement of agricultural load forecasting methodologies. [ABSTRACT FROM AUTHOR]

In farming technologies, it is difficult to properly provide the accurate crop nutrients for respective crops. For this reason, farmers are experiencing enormous problems. Although various types of machine learning (deep learning and convolutional neural networks) have been used to identify crop diseases, as has crop classification-based image processing, they have failed to forecast accurate crop nutrients for various crops, as crop nutrients are numerical instead of visual. Neural networks represent an opportunity for the precision agriculture sector to more accurately forecast crop nutrition. Recent technological advancements in neural networks have begun to provide greater precision, with an array of opportunities in pattern recognition. Neural networks represent an opportunity to effectively solve numerical data problems. The aim of the current study is to estimate the right crop nutrients for the right crops based on the data collected using an artificial neural network. The crop data were collected from the MNIST dataset. To forecast the precise nutrients for the crops, ANN models were developed. The entire system was simulated in a MATLAB environment. The obtained results for forecasting accurate nutrients were 99.997%, 99.996%, and 99.997% for validation, training, and testing, respectively. Therefore, the proposed algorithm is suitable for forecasting accurate crop nutrients for the crops. [ABSTRACT FROM AUTHOR]

The accurate forecasting of agricultural futures prices is critical for ensuring national food security. Therefore, this study proposes a text‐based deep learning forecasting model. This model first uses the ChineseBERT + a text convolution neural network to classify Weibo text and obtain a raw sentiment index. Then, complete ensemble empirical mode decomposition with adaptive noise, variational mode decomposition, correlation coefficient, and sample entropy are combined to decompose and reconstruct the raw sentiment index and obtain a denoised sentiment index. Subsequently, the neural basis expansion analysis with exogenous variables is improved by designing a weight coefficient and Optuna is used to optimize the designed weight coefficient and the hyperparameters. Finally, the SHapley Additive exPlanations value is used to increase the interpretability of prediction results. Corn futures prices for the Dalian Exchange are used in forecasting to validate the accuracy and stability of the proposed model. Experimental results show that the proposed denoising sentiment index contributes more to the improvement of predictive model performance than the raw sentiment index. The proposed text‐based deep predictive model demonstrates strong predictive ability for prediction horizons of 30 and 60 days. SHapley Additive exPlanations value analysis shows that the three features with greater effects on corn futures prices are as follows: "Corn Spot Price of Zhengzhou market," "CBOT_corn_futures_price," and "Pork futures price." [ABSTRACT FROM AUTHOR]

In recent years, ensuring food security has become a global concern, necessitating accurate forecasting of agriculture security to aid in policymaking and resource allocation. This article proposes the utilization of transformers, a powerful deep learning technique, for predicting the Agriculture Security Index (ASI). The ASI is a comprehensive metric that evaluates the stability and resilience of agricultural systems. By harnessing the temporal dependencies and complex patterns present in historical ASI data, transformers offer a promising approach for accurate and reliable forecasting. The transformer architecture, renowned for its ability to capture long‐range dependencies, is tailored to suit the ASI forecasting task. The model is trained using a combination of supervised learning and attention mechanisms to identify salient features and capture intricate relationships within the data. To evaluate the performance of the proposed method, various evaluation metrics, including mean absolute error, root mean square error, and coefficient of determination, are employed to assess the accuracy, robustness, and generalizability of the transformer‐based forecasting approach. The results obtained demonstrate the efficacy of transformers in forecasting the ASI, outperforming traditional time series forecasting methods. The transformer model showcases its ability to capture both short‐term fluctuations and long‐term trends in the ASI, allowing policymakers and stakeholders to make informed decisions. Additionally, the study identifies key factors that significantly influence agriculture security, providing valuable insights for proactive intervention and resource allocation. [ABSTRACT FROM AUTHOR]

Land transfer is essential for advancing moderate-scale agriculture, and the digital economy plays a key role in facilitating these transfers. Based on microdata from four rounds of the China Household Finance Survey spanning from 2013 to 2019, this study employs a Linear Probability Model (LPM) to examine the impact of digital economy on rural land transfer-out decisions. The results indicate that: (1) The development of the digital economy significantly increases the likelihood of rural households transferring land out. Specifically, for each unit increase in the digital economy index, the probability of a household transferring land out increases by 3.39%. These conclusions remain robust even after accounting for endogeneity and conducting robustness checks. (2) The digital economy facilitates rural land transfer-out by promoting non-agricultural employment and entrepreneurship among farmers, as well as enhancing online social interactions and information acquisition. (3) There are heterogeneous impacts due to different scales and transferees, variations in physical and human capital, as well as differences in village topographies. This study deepens the understanding of the digital economy's impact on land transfer decision-making and provides relevant policy references for facilitating land transfer and agricultural modernization. [ABSTRACT FROM AUTHOR]

A timely and consistent assessment of crop yield will assist the farmers in improving their income, minimizing losses, and deriving strategic plans in agricultural commodities to adopt import-export policies. Crop yield predictions are one of the various challenges faced in the agriculture sector and play a significant role in planning and decision-making. Machine learning algorithms provided enough belief and proved their ability to predict crop yield. The selection of the most suitable crop is influenced by various environmental factors such as temperature, soil fertility, water availability, quality, and seasonal variations, as well as economic considerations such as stock availability, preservation capabilities, market demand, purchasing power, and crop prices. The paper outlines a framework used to evaluate the performance of various machine-learning algorithms for forecasting crop yields. The models were based on a range of prime parameters including pesticides, rainfall and average temperature. The Results of three machine learning algorithms, Categorical Boosting (CatBoost), Light Gradient-Boosting Machine (LightGBM), and eXtreme Gradient Boosting (XGBoost) are compared and found more accurate than other algorithms in predicting crop yields. The RMSE and R2 values were calculated to compare the predicted and observed rice yields, resulting in the following values: CatBoost with 800 (0.24), LightGBM with 737 (0.33), and XGBoost with 744 (0.31). Among these three machine learning algorithms, CatBoost demonstrated the highest precision in predicting yields, achieving an accuracy rate of 99.123%. [ABSTRACT FROM AUTHOR]

This study explores crop yield forecasting through weight agnostic neural networks (WANN) optimized by a modified metaheuristic. WANNs offer the potential for lighter networks with shared weights, utilizing a two-layer cooperative framework to optimize network architecture and shared weights. The proposed metaheuristic is tested on real-world crop datasets and benchmarked against state-of-the-art algorithms using standard regression metrics. While not claiming WANN as the definitive solution, the model demonstrates significant potential in crop forecasting with lightweight architectures. The optimized WANN models achieve a mean absolute error (MAE) of 0.017698 and an R-squared ( R 2 ) score of 0.886555, indicating promising forecasting performance. Statistical analysis and Simulator for Autonomy and Generality Evaluation (SAGE) validate the improvement significance and feature importance of the proposed approach. [ABSTRACT FROM AUTHOR]

Large-scale yield estimation in the field or plot during wheat grain filling can contribute to high-throughput plant phenotyping and precision agriculture. To overcome the challenges of poor yield estimation at a large scale and for multiple species, this study employed a combination of multispectral and RGB drones to capture images and generation of time-series data on vegetation indices and canopy structure information during the wheat grubbing period. Five machine learning methods, partial least squares, random forest, support vector regression machine, BP neural networks, and long and short-term memory networks were used. The yield estimation of wheat grain filling period data was executed using a long and short-term memory network based on the preferred machine learning model, with a particular focus on distinguishing different heat-tolerant genotypes of wheat. The results unveiled a declining trend in the spectral reflectance characteristics of vegetation indices as the filling period progressed. Among the time-series data of the wheat filling period, the long and short-term memory network exhibited the highest estimation effectiveness, surpassing the BP neural network, which displayed the weakest estimation performance, by an impressive improvement in R2 of 0.21. The three genotypes of wheat were categorized into heat-tolerant genotype, moderate heat-tolerant genotype, and heat-sensitive genotype. Subsequently, the long and short-term memory network, which exhibited the most accurate yield estimation effect, was selected for regression prediction. The results indicate that the yield estimation effect was notably better than that achieved without distinguishing genotypes. Among the wheat genotypes, the heat-sensitive genotype demonstrated the most accurate prediction with an R2 of 0.91 and RMSE% of 3.25%. Moreover, by fusing the vegetation index with canopy structure information, the yield prediction accuracy (R2) witnessed an overall enhancement of about 0.07 compared to using the vegetation index alone. This approach also displayed enhanced adaptability to spatial variation. In conclusion, this study successfully utilized a cost-effective UAV for data fusion, enabling the extraction of canopy parameters and the application of a long and short-term memory network for yield estimation in wheat with different heat-tolerant genotypes. These findings have significant implications for informed crop management decisions, including harvesting and contingency forecasting, particularly for vast wheat areas. [ABSTRACT FROM AUTHOR]

This article explores the different perspectives on the causes of famines, specifically the neo-Malthusian and Senian theories. The author proposes a theory that combines these perspectives and provides a framework for understanding famines. They argue that understanding scarcity, abundance, and sufficiency is crucial for famine research. The author also addresses criticisms of their own work, particularly regarding the Bengal famine of 1943, and emphasizes the importance of constructive criticism in scholarly work. [Extracted from the article]

Accurate rain forecasting is crucial for optimizing agricultural practices and improving crop yields. This study presents a real-time rain forecasting model using a Bidirectional Long Short-Term Memory (BiLSTM) algorithm for an on-device AI platform. The model uses historical weather data to predict rainfall, enabling farmers to make data-driven decisions in irrigation, pest control, and field operations. This model enables farmers to optimize water use, conserve energy, and improve overall resource management. Real-time capabilities allow immediate adjustments to agricultural activities, mitigating risks associated with unexpected weather changes. The Bi-LSTM model achieved a mean accuracy of 92%, significantly outperforming the traditional LSTM (85%) and ARIMA (80%) models. This high accuracy is attributed to the model's bidirectional processing capability, which captures comprehensive temporal patterns in the weather data. Implementing this model can enhance decision-making processes for farmers, resulting in increased productivity and profitability in the agricultural sector. [ABSTRACT FROM AUTHOR]

This paper intends to introduce a novel sugarcane plant disease prediction. There are three stages to the projected method, namely: (i) Preprocessing, (ii) Feature extraction, and (iii) Disease detection phase. Initially, the input image is given to the pre-processing stage in which the grey transformation and bilinear interpolation are carried out. The grey transformation is undergone to improve the clarity of the picture and the bilinear interpolation is used as a re-sampling technique. The feature extraction step follows the pre-processed picture, where both texture and colour features are retrieved. More specifically, the texture features like GLCM and suggested Distance-based LBP (D-LBP) features will be extracted. Subsequently, the extracted features are subjected to the disease detection phase process, where a hybrid classifier is introduced. This hybrid classifier blends the concepts of 'Neural Network (NN) and Support Vector Machine (SVM)' for better prediction results. In order to verify the effectiveness of the work that is suggested, a comparison analysis is conducted comparing the suggested and current approaches. The proposed method achieves highest accuracy in 80% of learning which is 13.97%, 16.12%, 11.82%, and 12.9% better than the other methods such as SVM, DT, RF, and NN, respectively. [ABSTRACT FROM AUTHOR]

The importance of predicting crop yields has increased due to growing concerns of surrounding food security. Early forecasting of crop yields holds a pivotal role in avoiding starvations by estimating the food supply available for the expanding global population. Several Deep Learning (DL) and Machine Learning (ML) techniques are involved to develop effective and accurate crop yield prediction model. Nevertheless, existing models faces some limitations such as less accuracy, high error rate because of noisy data, high training time and extracted less effective features for prediction. To overcome these issues, the novel DL methodology is introduced for attaining high accurate crop yield prediction. Initially, the soil, weather and other resources big data are collected from the various agriculture field. In data collection phase, the input data of larger size are stored in the Hadoop platform for the purpose of storing as well as processing the entire data in a distributed manner. The data are pre-processed through the utilization of Missing value imputation and z-score based data normalization. From the pre-processed data, the optimal features are considered using Integrated Correlation Random recursive elimination (InCorRe) approach. Based on the previous soil and weather information, the suitable yield of crops can be predicted using Multi head Residual dilated convolution assisted gated unit (MResGat) model. Finally, the losses of the network model can be optimized using African vulture optimization algorithm (AVO). The proposed method is evaluated using the several performance metrics, which achieved 0.023% of MSE value and 0.036% of MAE values. [ABSTRACT FROM AUTHOR]

Accurately predicting agricultural commodity prices is crucial for India's economy. Traditional parametric models struggle with stringent assumptions, while machine learning (ML) approaches, though data-driven, lack automatic feature extraction. Deep learning (DL) models, with advanced feature extraction and predictive abilities, offer a promising solution. However, their application to agricultural price data ignored the exogenous factors. Hence, the study explored advanced versions of the well-known univariate models, NBEATSX and TransformerX. The research employed price data for essential crops like Tomato, Onion, and Potato (TOP) from major Indian markets and complemented it with corresponding weather data (precipitation and temperature). To provide a comprehensive analysis, the study also evaluated traditional statistical methods (ARIMAX and MLR) and a suite of ML algorithms (ANN, SVR, RFR, and XGBoost). The performance of these models was rigorously evaluated using error metrics like RMSE, MAE, sMAPE, MASE and QL. The findings were significant indicating DL models, particularly when augmented with exogenous variables, consistently outshone other methods with NBEATSX and TransformerX showing an average RMSE of 110.33 and 135.33, MAE of 60.08 and 74.92, sMAPE of 22.14 and 24.00, MASE of 1.02 and 1.32 and QL of 30.04 and 34.07, respectively. They exhibited lower error metrics, as compare to the statistical and ML models underscoring their effectiveness and potential in agricultural crop price forecasting. This study not only bridged a crucial research gap but also highlighted the robust potential of DL models in enhancing the accuracy of agricultural commodity price predictions in India. [ABSTRACT FROM AUTHOR]

This study aimed to explore big-time series data on agricultural commodities with an autocorrelation model comprising long-term processes, seasonality, and the impact of exogenous variables. Among the agricultural commodities with a large amount of data, chili prices exemplified criteria for long-term memory, seasonality, and the impact of various factors on production as an exogenous variable. These factors included the month preceding the new year and the week before the Eid al-Fitr celebration in Indonesia. To address the factors affecting price fluctuations, the Seasonal Autoregressive Fractionally Integrated Moving Average (SARFIMA) model was used to manage seasonality and long-term memory effects in the big data analysis. It improved with the addition of exogenous variables called SARFIMAX (SARFIMA with exogenous variables is known as SARFIMAX). After comparing the accuracy of both models, it was discovered that the SARFIMAX performed better, indicating the influence of seasonality and previous chili prices for an extended period in conjunction with exogenous variables. The SARFIMAX model gives an improvement in model accuracy by adding the effect of exogenous variables. Consequently, this observation concerning price dynamics established the cornerstone for maintaining the sustainability of chili supply even with the big data case. [ABSTRACT FROM AUTHOR]

The vegetable sector is a vital pillar of society and an indispensable part of the national economic structure. As a significant segment of the agricultural market, accurately forecasting vegetable prices holds significant importance. Vegetable market pricing is subject to a myriad of complex influences, resulting in nonlinear patterns that conventional time series methodologies often struggle to decode. In this paper, we exploit the average daily price data of six distinct types of vegetables sourced from seven key wholesale markets in Beijing, spanning from 2009 to 2023. Upon training an LSTM model, we discovered that it exhibited exceptional performance on the test dataset. Demonstrating robust predictive performance across various vegetable categories, the LSTM model shows commendable generalization abilities. Moreover, LSTM model has a higher accuracy compared to several machine learning methods, including CNN-based time series forecasting approaches. With R2 score of 0.958 and MAE of 0.143, our LSTM model registers an enhancement of over 5% in forecast accuracy relative to conventional machine learning counterparts. Therefore, by predicting vegetable prices for the upcoming week, we envision this LSTM model application in real-world settings to aid growers, consumers, and policymakers in facilitating informed decision-making. The insights derived from this forecasting research could augment market transparency and optimize supply chain management. Furthermore, it contributes to the market stability and the balance of supply and demand, offering a valuable reference for the sustainable development of the vegetable industry. [ABSTRACT FROM AUTHOR]

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

Generative artificial intelligence tools have been shown to substantially increase productivity in a range of different contexts. I discuss the potential and limitations of the current models, and the evidence on how economic researchers can best make use of generative artificial intelligence in their work. To illustrate these points, I show how the data analysis tools of ChatGPT can be used to address a specific question: the accuracy of agricultural forecasts—and discuss the strengths and weaknesses of artificial intelligence in data cleaning, data analysis and producing graphs and illustrations. [ABSTRACT FROM AUTHOR]

The importance of accurate forecasting in agricultural hydrometeorology is clear. This research is an approach towards the use of a tracking machine with a hidden layer for error prediction at stationary points. The predicted error will be used to modify the model output. One of the strengths of this method is the use of a meteorological variable such as maximum and minimum temperature in applications. A tracking machine with a hidden layer tracks the time series of the short-term prediction error of the maximum and minimum temperature of the model with the kernel of trigonometric functions, which is formulated as follows: xt = ∑∞n=1anϕn (Ant² + Bnt + Cne-Dn(t/T) 2) + Cϵt t ∈ T It provides an error prediction that will effectively modify the model prediction. This machine is compact in terms of computing. The value of the standard deviation of the statistical population of the maximum temperature during the period was 10 celsius, which shows a significant improvement from the value of 9.5 to 10.01 by the tracking machine. Also, the standard deviation of the minimum temperature was about 8.5 degrees Celsius, which was improved by the machine from 7.7 to 8.4 degrees Celsius. In this research, we use the skill score criterion, whose value will show that the skill score of the model for short-term maximum temperature has grown from a negative value with a leap to more than 0.8, which shows the significant impact of the machine in improving forecasting. The minimum temperature prediction skill score of the model will show an increase in the way of improving the prediction. The comparison of the obtained results shows that the skill score and RMSE of predicting the maximum and minimum temperature of the modification of the output of the model have increased significantly compared to the model. Also, the monthly change in the skill score indicates the effect of the chasing car on the ability to correct the forecast, especially for the short-term maximum temperature. Investigations will show that the modification of the model has a uniform overfitting in the studied period. In addition, a powerful index independent of the concept of accuracy size will be introduced and used as a method to check the reliability of the model and tracking machine outputs, which indicates the level of confidence that can be had in the model and machine outputs. In this case, the reliability of the maximum and minimum temperature predictions and the significant growth of the index have shown stability in providing the output. After bias correction, the variability of the skill score has been significantly reduced, and by reducing the amount of forecasting error, the reliability of the model forecasts has increased from 60% to more than 85%. Depending on the location and time, the WRF model's forecasting performance is different, but after bias correction, this dependence is removed, and forecasting in all regions and times has almost the same performance. [ABSTRACT FROM AUTHOR]

Agriculture in Maharashtra has immense importance in India, acting as the back-bone of the economy and a primary livelihood source for a significant population. Being the third largest state in India, Maharashtra has a high scale crop production in the country which also has an important impact on the economy. Initially the study focus on developing predictive models that guide farmers in selecting suitable crops for the divisions in the state of Maharashtra. This study presents a Crop Recommendation System (CRS) designed to support Maharashtra's agricultural sector by utilizing a comprehensive dataset from 2001 to 2022 provided by the India Meteorological Department. This study helps in improvising technical efficiency and productivity of the farmers. Harvesting crops in optimal condition can help to produce efficient harvest hence the research concentrates on providing best crop recommendation system (CRS) with the help of Machine Learning and Deep Learning techniques. The data, enhanced for accuracy using expectation-maximization optimization, underpins predictive models that guide crop selection. EM contributes to a more robust and reliable dataset for subsequent analyses and modeling by iterative estimating and updating missing values based on probabilistic expectations. Key findings show that the Random Forest algorithm excels in predicting suitable crops with 92% accuracy. Further precision is achieved through a Long Short-Term Memory network forecasting weather patterns three months ahead, accommodating temporal data variations. Subsequently, the proposed system leverages these forecasts to recommend five ideal crops per division within Maharashtra, aiding farmers' decision-making and adapting to regional climatic conditions. A supplementary crop calendar offers monthly district-specific planting guidance. An intuitive Graphical User Interface delivers this information effectively, ensuring practical and informed agricultural choices across the state. In essence, the study provides an innovative tool for enhancing economic stability and sustenance in Maharashtra through technology-driven agriculture recommendations aligned with future weather expectations. [ABSTRACT FROM AUTHOR]

The new development pattern has identified two key avenues for the sustained advancement of high-quality agricultural and rural development: digitalisation and low-carbon development. The measurement of the digital economy and the agricultural carbon emission performance, and their spatial and temporal heterogeneity, is a crucial step in promoting the spatial coordination and sustainable development of digitalisation and low-carbon agriculture. This paper employs the entropy value method, SBM model, and coupling coordination degree model to investigate the coupling coordination measurement and spatial-temporal heterogeneity of the performance of the digital economy and agricultural carbon emissions. The data used are provincial panel data from 2013 to 2021. The simulation results demonstrate that, between 2013 and 2021, the digital economy of all provinces exhibited varying degrees of growth, yet the development of the digital economy between provinces exhibited a more pronounced tendency to diverge. Concurrently, the agricultural carbon emission efficiency in China exhibited a fluctuating upward trend. The development of the digital economy and the efficiency of agricultural carbon emission were found to be highly coupled. Their coupling and coordination relationship showed a downward trend followed by an upward trend. In general, it is suggested that we should increase investment in digital economy infrastructure and technology, promote digital agricultural applications, strengthen policy guidance and financial support, establish a coupling coordination mechanism and strengthen farmers' digital literacy and environmental awareness. [ABSTRACT FROM AUTHOR]

This study delves into the factors influencing the willingness of rural land transfers in different terrain areas, aiming to promote the improvement of land transfer institutions and accelerate the process of scale farming. Based on rural survey data from Anhui and Qinghai provinces in China, this research uses geographical detector and Binary Logistic Model to explore the differential factors affecting the willingness of farmers to participate in land contract transfer in the first and third terrain areas of China. The study examines four dimensions, including individual characteristics, family endowments, social support strategies, and geographical environment. The findings reveal the following: (1) By comparing the mean values, standard deviations, and coefficients of variation of the data from both provinces, it is evident that the indicators of individual characteristics, family endowments, social support strategies, and geographical environment differ significantly between the two provinces. This indicates substantial disparities in the basic attributes of farmers and their living environments. (2) The single-factor explanatory power significantly influencing farmers' willingness to engage in land transfer varies considerably and is statistically significant at the 1% level. The types of interaction between two factors mainly include dual-factor enhancement, nonlinear enhancement, single-factor nonlinear attenuation, and nonlinear attenuation. (3) There are commonalities and differences in the factors that significantly influence farmers' willingness to participate in land transfer in the two provinces. Common factors influencing farmers' land transfer willingness in both provinces include: the educational level of household heads, the health status of household heads, the number of family laborers, the arable land area, the differentiation of agricultural management objectives, the proportion of agricultural operating income, labor service economy, and relocation policies. Factors showing different influences include: the age of household heads, school-age children, the number of family members engaged in different occupations, the proportion of income from off-farm employment, minimum guarantee policies credit support, location distance, and terrain undulation. Therefore, in formulating land transfer policies, the government should prioritize significant driving factors influencing farmers' decision-making behavior in different regions. It is essential to develop and implement land transfer policies tailored to local conditions with the primary goal of safeguarding the rights and interests of the principal stakeholders, thus achieving sustainable land utilization. [ABSTRACT FROM AUTHOR]

This study aims to develop predictive models for rice yield by applying multivariate techniques. It utilizes stepwise multiple regression, discriminant function analysis and logistic regression techniques to forecast crop yield in specific districts of Haryana. The time series data on rice crop have been divided into two and three classes based on crop yield. The yearly time series data of rice yield from 1980–81 to 2020–21 have been taken from various issues of Statistical Abstracts of Haryana. The study also utilized fortnightly meteorological data sourced from the Agrometeorology Department of CCS HAU, India. For comparing various predictive models' performance, evaluation of measures like Root Mean Square Error, Predicted Error Sum of Squares, Mean Absolute Deviation and Mean Absolute Percentage Error have been used. Results of the study indicated that discriminant function analysis emerged as the most effective to predict the rice yield accurately as compared to logistic regression. Importantly, the research highlighted that the optimum time for forecasting the rice yield is 1 month prior to the crops harvesting, offering valuable insight for agricultural planning and decision-making. This approach demonstrates the fusion of weather data and advanced statistical techniques, showcasing the potential for more precise and informed agricultural practices. [ABSTRACT FROM AUTHOR]

Cotton (Gossypium spp.), a crucial cash crop in the United States, requires the constant monitoring of growth parameters for informed decision-making. Recently, forecasting models have gained prominence for predicting canopy indicators, aiding in-season planning and management decisions to optimize cotton production. This study employed unmanned aerial system (UAS) technology to collect canopy cover (CC) data from a 40-hectare cotton field in Driscoll, Texas, in 2020 and 2021. Long short-term memory (LSTM) models, trained using 2020 data, were subsequently applied to forecast the CC values for 2021. These models were compared with real-time auto-regressive integrated moving average (ARIMA) models to assess their effectiveness in predicting the CC values up to 14 days in advance, starting from the 28th day after crop emergence. The results showed that multiple-input multi-step output LSTM models achieved higher accuracy in predicting the in-season CC values during the early growth stages (up to the 56th day), with an average testing RMSE of 3.86, significantly lower than other single-input LSTM models. Conversely, when sufficient testing data are available, single-input stacked-LSTM models demonstrated precision in CC predictions for later stages, achieving an average RMSE of 3.06. These findings highlight the potential of LSTM models for in-season CC forecasting, facilitating effective management strategies in cotton production. [ABSTRACT FROM AUTHOR]

Harvest strategies are utilised in the management of fishery resources globally. Critical to their success is harvest strategy evaluation, whereby future performance is assessed through projection modelling. Using the observed relationship between a puerulus settlement index and model‐estimated recruitment, we evaluated a southern rock lobster (Jasus edwardsii) harvest strategy in South Australia. Short‐to‐medium puerulus settlement recruitment forecasting was incorporated into future projections of catch, catch per unit effort, and percent unexploited egg production (%UEP) during 2019–2022. Observed values of indicators that closely aligned with projected outputs indicated that puerulus forecasting underpinned reliable evaluation of future fishery performance. A key objective of the harvest strategy was to increase egg production in the fishery, with a 20% unexploited egg production targeted by 2036. The target was reached under the proposed strategy, but was sensitive to recruitment, thereby highlighting the need to consider climate change impacts in forecasting scenarios. [ABSTRACT FROM AUTHOR]

The importance of forecasting crop yields in agriculture cannot be overstated. The effects of yield forecasting are observed in all the aspects of the supply chain from staffing to supplier demand, food waste, and other business decisions. However, the process is often inaccurate and far from perfect. This paper explores the potential of using expert forecasts to enhance the crop yield predictions of our global-to-local XGBoost machine learning system. Additionally, it investigates the ERA5 climate model's viability as an alternative data source for crop yield forecasting in the absence of on-farm weather data. We find that, by combining both the expert's pre-season forecasts and the ERA5 climate model with the machine learning model, we can—in most cases—obtain better forecasts that outperform the growers' pre-season forecasts and the machine learning-only models. Our expert-informed model attains yield forecasts for 4 weeks ahead with an average RMSE of 0.0855 across all the plots and an RMSE of 0.0872 with the ERA5 climate data included. [ABSTRACT FROM AUTHOR]

The agro-pastoral transitional zone in northern China is an important agricultural and pastoral production base. This study focuses on the eastern section of the agro-pastoral transitional zone in northern China. Combined with spatio-temporal analysis, stepwise regression, and gray relation analysis, we analyzed the average annual growth rate, spatio-temporal changes, and influencing factors of agricultural and pastoral production in the study area during 2000–2020. The results show that, in the past 20 years, the agricultural and pastoral production of the 50 municipal districts and counties has significantly changed, among which the agricultural production of 38 municipal districts and counties has shown an extremely significant increase. Generally, the growth rate of agricultural production is higher than that of pastoral production. Agricultural and pastoral production in the study area is influenced by socio-economic and land use/cover factors, with an average correlation degree of 0.79 and 0.88, respectively. Climate change affects agricultural production in agricultural counties, with a correlation degree of 0.85. The results of this research provide valuable insights into understanding the long-term temporal and spatial changes in agricultural and pastoral production and help to develop sound agriculture and pastoral management practices in the eastern section of the agro-pastoral transitional zone in Northern China. [ABSTRACT FROM AUTHOR]

Ginger, a vital herbal commodity, experiences low yield rates, necessitating intensive cultivation and rigorous evaluation by farmers to ensure financial viability and alignment with market demands. This study was conducted to devise a harvest forecasting system that supports decision-making through minimal error rates by comparing double exponential smoothing (DES) and long short-term memory (LSTM) forecasting methods. The efficacy of these methods was assessed through a series of trials, analyzing data collected from 2015 to 2019, comprising 250 datasets. The evaluation focused on two primary metrics: the Mean Absolute Percentage Error (MAPE) and the Root MSE (RMSE), to determine the precision of forecast models. It was observed that the LSTM model outperformed the DES method, yielding a MAPE of 38.99% and an RMSE of 1244.85432, in contrast to the DES method which resulted in a MAPE of 43.49% and an RMSE of 12997.34261, at an alpha level of 0.4 and an optimal beta of 0.1. Given these findings, the LSTM model is recommended for the forecast of ginger yields due to its superior accuracy and lower standard error compared to the DES method. This comparative analysis underscores the importance of selecting appropriate forecasting models to enhance agricultural planning and productivity, particularly in crops with fluctuating yields such as ginger. [ABSTRACT FROM AUTHOR]

For market players and policy officials, commodity price forecasts are crucial problems that are challenging to address due to the complexity of price time series. Given its strategic importance, corn crops are hardly an exception. The current paper evaluates the forecasting issue for China's weekly wholesale price index for yellow corn from January 1, 2010 to January 10, 2020. We develop a Gaussian process regression model using cross validation and Bayesian optimizations over various kernels and basis functions that could effectively handle this sophisticated commodity price forecast problem. The model provides precise out-of-sample forecasts from January 4, 2019 to January 10, 2020, with a relative root mean square error, root mean square error, and mean absolute error of 1.245%, 1.605, and 0.936, respectively. The models developed here might be used by market players for market evaluations and decision-making as well as by policymakers for policy creation and execution. [ABSTRACT FROM AUTHOR]

The synergy between artificial intelligence (AI) and agricultural sciences has garnered substantial attention, especially in the realm of crop yield prediction. The present bibliometric analysis examines the worldwide research trends about the application of AI in predicting crop yields. The global literature on crop yield prediction using AI published between 1997 and 2022 is searched in the Scopus database. Five hundred and forty research articles were used to compile the analysis; they were located in the Scopus database and processed through the VOSviewer. Our research reveals a significant surge in scholarly publications, particularly focusing on countries including China, the United States, India and Canada. These research endeavours aim to apply AI methodologies for forecasting agricultural produce yields in tandem with developments in remote sensing technologies that facilitate more accurate yield predictions. These insights offer a valuable reference for researchers and illuminate potential directions for future investigations in the domain of AI-based crop yield prediction. [ABSTRACT FROM AUTHOR]

Precise and prompt predictions of crop yields are crucial for optimising farm management, post-harvest operations, and marketing strategies within the agricultural sector. While various machine learning approaches have been employed to forecast crop yield, their application to grain quality, particularly head rice yield (HRY), is less explored. This research collated crop-level HRY data across four seasons (2017/18–2020/21) from Australia's rice-growing region. Models were developed using the XGBoost algorithm trained at varying time steps up to 16 weeks pre-harvest. The study compared the accuracy of models trained on datasets with climate data alone or paired with vegetative indices using two- and four-week aggregations. The results suggest that model accuracy increases as the harvest date approaches. The dataset combining climate and vegetative indices aggregated over two weeks surpassed industry benchmarks early in the season, achieving the highest accuracy two weeks before harvest (LCCC = 0.65; RMSE = 6.43). The analysis revealed that HRY correlates strongly with agroclimatic conditions nearer harvest, with the significance of vegetative indices-based features increasing as the season progresses. These features, indicative of crop and grain maturity, could aid growers in determining optimal harvest timing. This investigation offers valuable insights into grain quality forecasting, presenting a model adaptable to other regions with accessible climate and satellite data, consequently enhancing farm- and industry-level decision-making. [ABSTRACT FROM AUTHOR]

Scientific agencies spend substantial sums producing and improving forecasts of seasonal climate, but they do so without much information about these forecasts' value in practice. Here we show that financial market participants value the production of seasonal forecasts: options traders price the uncertainty generated by upcoming United States National Oceanic and Atmospheric Administration Winter and El Niño Outlooks. Each outlook affects firms throughout the economy, with total market capitalization of $6 and $13 trillion, respectively. A 1% improvement in the skill of the El Niño Outlook reduces firms' exposure to a one standard deviation shock by $18 billion and induces traders to spend an additional $2 million hedging the outlook's news. Firms must not be able to undertake ex-ante adaptation that would eliminate their exposure to the forecasted portion of seasonal climate without imposing substantial costs of its own. Traders of financial options bet that firms' stock prices will be affected by forecasts of seasonal climate produced by the U.S. National Oceanic and Atmospheric Administration. Firms are exposed throughout the economy, and traders spend more to hedge the news from more skillful forecasts [ABSTRACT FROM AUTHOR]

The "Three Rural Issues", encompass challenges related to agriculture, farmer, and rural area, which hold significant importance in driving comprehensive rural revitalization efforts in China. Farmer entrepreneurship, as a crucial means to enhance productivity, create job opportunities, and increase residents' income, has gradually become a key driving force in promoting rural revitalization in the new stage of development in China. With the rapid development of rural e-commerce, farmer entrepreneurship has encountered new opportunities. This study utilizes the 2020 China Family Panel Studies (CFPS) data and employs a structural equation model (SEM) to analyze the direct impact of rural e-commerce participation on farmer entrepreneurial behavior, considering factors such as human capital, social capital, and network infrastructure. This study further explores the indirect effects and mechanisms of e-commerce participation as a mediating variable and analyzes the impact and mechanisms on agricultural entrepreneurship behavior. The findings are as follows: (1) E-commerce participation significantly promotes farmer entrepreneurial behavior; (2) E-commerce participation as a mediating variable has a positive indirect effect on the relationship between social trust, network infrastructure, human capital, and farmer entrepreneurial behavior; (3) E-commerce participation has a significant positive influence on farmer entrepreneurship in the agricultural sector, and farmers with higher levels of network infrastructure and human capital have a higher probability of choosing agricultural entrepreneurship under the influence of e-commerce participation. Finally, this study provides policy recommendations in terms of infrastructure construction, entrepreneurial policy environment, and education level, aiming to optimize the situation of farmer entrepreneurship and contribute to the comprehensive promotion of rural revitalization.Overall, the research in this paper effectively combines theory and empirical evidence to outline the direct and indirect impact mechanisms of rural e-commerce participation on farmers' entrepreneurial behavior and agriculture-related entrepreneurial behavior and to test the effects of their impacts. First, most of the existing literature deals with farmers in individual sample areas, while the sample selected in this paper is farmers in the whole country, which is relatively more generalizable; second, most of the previous studies explore the level of e-commerce in the inter-provincial or county areas, while this paper expands the empirical study of rural e-commerce on the entrepreneurial behavior of farmers and the micro-period of agricultural entrepreneurial behavior, and focuses on the impacts of the e-commerce activities of farmers on their entrepreneurial behavior. [ABSTRACT FROM AUTHOR]

The relationship between agricultural credit and agricultural production as well as the impacts on sustainable development (i.e., poverty alleviation, reduction of inequalities, food and nutrition security, and stimulation of economic growth) have been widely documented. The objective of this study is to analyze the impact of credit on cattle production and deforestation in Colombia through spatial panel data models. For this purpose, a departmental data panel for the period 2011–2020 was built, based on available information from public entities. The results suggest that in Colombia, the relationship between access to credit and cattle production is significant and can be either negative or positive. In addition, there is evidence of spatial dependence, meaning that cattle production in one department is being affected by cattle production in a neighboring department or by all the departments that make up the national territory. Regarding deforestation, results show that, although the number of cattle present in a department does affect its annual deforestation rate due to a poor coverage of sustainably intensified cattle ranching systems, there is no relationship between deforestation and the access to credit nor any spatial correlations. [ABSTRACT FROM AUTHOR]

Every year, many farmers die as a result of snakebites or other envenomations all over the world. There is currently a need to identify the snakebite regions that cause severe damage in the Indian farmer's family, especially in the study area. The study area, i.e., India's Krishna district, is highly prone to snakebites and floods. It best suits the high yield of agricultural crops due to fertile lands and rich water resources. The present research focuses on the identification of the snakebites-prone areas using geospatial analytics based on the Area Land (AL), Rainfall (RF), Flood Zone (FZ), Snakebite (SB), River Side (RS), and Locality (L). A novel Snake Bite Impacted Area (SBIA) method is proposed and implemented to find the Geospatial predictive correlation analysis of snakebites. To measure the performance of SBIA, various popular data classification methods are tested, such as multi-class SVM, Decision Trees (DT), Boosted Trees (BT), Linear Discriminate (LD), and K-Nearest Neighbor (KNN). Among all popular techniques, the SBIA performance is high with an accuracy of 94.12, whereas the accuracy of LD is 76.5, then both SVM and KNN are 82.4, whereas DT and BT are 94.1. In addition the AUC result of SBIA is compared with existing Ecological Ninche Models to predict snakebite prone areas with location. Overall, the performance of SBIA is good, which combines spatial and non-spatial data to predict the snakebite-prone areas. [ABSTRACT FROM AUTHOR]

It aims to improve the application efficiency of modern science and technology in farm operation, enhance the prediction effect of machine learning and blockchain technology on farm supply and demand issues, and help enterprise managers to better manage and operate enterprise projects. First, the agricultural super business model under the blockchain + Internet of Things (IoT) is analysed, and the farm management system of the IoT + blockchain is constructed. Then, the demand forecasting model of agricultural products is established. Autoregressive Integrated Moving Average model (ARIMA) and Support Vector Machine (SVM) models are used to predict demand. Finally, the characteristics of the two models in prediction are discussed. The results show that the ARIMA model can reach 99.2% in the application efficiency evaluation of farm operation. Moreover, in the case of proper selection of model indicators, the prediction result is close to the actual value, the prediction error fluctuation is small, and the maximum error value is 0.4318. The local error of SVM model in predicting the demand of farm supply chain is slightly larger, and the maximum error value is 8.5430. By comparing the error results of the two models, the prediction accuracy of ARIMA model is higher than that of SVM model. The ARIMA model designed in this work can provide effective product demand prediction for farm operation and provide a strong technical reference for farm operation. The research not only provides technical support for machine learning and blockchain technology to predict farm supply and demand, but also contributes to the integrated management and sustainable development of enterprises. [ABSTRACT FROM AUTHOR]

Crop yield forecasting during an ongoing season is crucial to ensure food security and commodity markets. For this reason, here, a scalable approach to forecast corn yields at the field-level using machine learning and satellite imagery from Sentinel-2 and Landsat missions is proposed. The model, evaluated on 1319 corn fields in the U.S. Corn Belt from 2017 to 2022, integrates biophysical parameters from Sentinel-2, Land Surface Temperature (LST) from Landsat, and agroclimatic data from ERA5 reanalysis dataset. Resampling the time series over thermal time significantly enhances predictive performance. The addition of LST to our model further improves in-season yield forecasting, through its capacity to detect early drought, which is not immediately visible to optical sensors such as the Sentinel-2. Finally, we propose a new two-stage machine learning strategy to mitigate early season partially available data. It consists in extending the current time series on the basis of complete historical data and adapting the model inference according to the crop progress. [ABSTRACT FROM AUTHOR]

Scientific measurement of Agricultural Carbon Emissions (ACE) and its formation mechanism is of great significance for the effective formulation of high-quality agricultural development strategies. Therefore, the objectives were to study the characteristics of ACE, its formation mechanism, and its economic relevance in Chongqing by adopting the IPCC and 1997-2019 data, and with LMDI index and Decoupling index. The empirical analysis shows that the total ACE presents an M-shaped trend of "rising-steady fluctuation-falling". Energy structure, energy intensity, and population size have a negative driving effect on ACE, but economic activity is the main factor. The decoupling elasticity characteristics of ACE and economic growth are mainly weak decoupling and strong decoupling, of which the relationship is significantly coordinate. Therefore, to consolidate and enhance the goal of ACE reduction, it is necessary to strengthen the research and development of agricultural low-carbon production technology and promotion. We will continue to deepen supply-side structural reform in agriculture. We will improve the ecological environment in agriculture and rural areas. [ABSTRACT FROM AUTHOR]

Agricultural carbon emission reduction is inseparable from discussions about government systems, especially in countries with distinctive institutional arrangements such as China. Environmental regulation may influence the impact of environmental decentralisation on agricultural carbon intensity. Using panel data from 30 provinces in mainland China from 2000 to 2015, this paper depicts the spatiotemporal pattern and dynamic evolution of environmental decentralisation, environmental regulation, and agricultural carbon intensity. It uses the spatial Durbin model to empirically study the internal connections between the three. The empirical results show that the enhancement of environmental decentralisation, environmental administrative decentralisation, environmental supervision decentralisation, and environmental monitoring decentralisation is not conducive to reducing agricultural carbon intensity. The results also show that the enhancement of the intensity of environmental regulation of "terminal governance type" and "front-end governance type" is conducive to reducing agricultural carbon intensity, but this effect has a negative "space spillover" phenomenon. Other findings are as follows: (1) the enhancement of the two types of environmental regulation intensity is conducive to changing the impact of environmental decentralisation on agricultural carbon intensity from a "grabbing-hand" to a "helping-hand"; (2) compared with balanced grain-producing and marketing areas and major grain-marketing areas, environmental decentralisation and two types of environmental regulations have a greater negative impact on the agricultural carbon intensity of major grain-producing areas. Therefore, the central government should change how it evaluates political performance; accelerate the construction of a diversified performance evaluation system; increase the weight of low-carbon, green, and other indicators that reflect the friendly development of agriculture in the system; and encourage local governments to adjust the direction of agricultural production. [ABSTRACT FROM AUTHOR]

Access to an agricultural market information system (AMIS) is essential to reduce farmers' risk from market speculation related to information asymmetry. However, AMIS's sustainability depends on the users' needs and willingness to pay (WTP) for market information. This paper explores the WTP for market information among market‐oriented farmers, as well as its determinants. Data were collected through a structured survey with 479 farmers. A contingent dichotomous choice dual boundary estimation is used to measure WTP and a logistic regression is used to ascertain the main determinants of the stated WTP. Experienced losses, access to market information, use of non‐virtual means of information, and degree of farm specialization are the main determinants of the WTP. These findings are useful for establishing a feasible AMIS, and designing its services according to the characteristics, needs, capacities and preferences of the identified potential users. [ABSTRACT FROM AUTHOR]

Science and technological knowledge advancements have resulted in a vast number of data for the agricultural industry. Crop yield prediction (CYP) is a problematic issue in the agricultural field. The proposed work constructs a hybridization of the XGBoost-SVM-C4.5 framework to forecast crop yield. Also, the proposed methodology is employed to predict different crop yields based on temperature, rainfall, and soil parameters. The experimental setup was based on data gathered from the Indian Meteorological Department for various crops. Five metrics were analyzed to determine the performance of each approach under research: Determination Coefficient, Root Mean Squared Error, Correlation coefficient, Mean Absolute Error and Mean Squared Error. Experiments have been conducted to determine the most effective approach for predicting various crop yields. Additionally, the results of this research give an appropriate method for forecasting the future of food production, allowing for a more precise plan for agricultural production. [ABSTRACT FROM AUTHOR]