Crop Production Forecasts Research Articles

Contrasting performance of panel and time-series data models for subnational crop forecasting in Sub-Saharan Africa

We comprehensively examine methodologies tailored for subnational crop yield and production forecasting by integrating Earth Observation (EO) datasets and advanced machine learning approaches. We scrutinized diverse input data types, cross-validation methods, and training durations, focusing on maize production and yield predictions in Burkina Faso and Somalia. Central to our analysis is the comparative assessment of using time-invariant features within a panel data (PD) model versus a time-series data (TD) model. The TD model performed well in predicting both production and yield, while the PD model offered comparable yield predictions. Time-invariant features such as livelihood zones, soil properties, and cropland extents enriched the spatial understanding of crop data, enhancing the R-squared by 0.09 (0.21) for production and 0.11 (0.03) for yield, with corresponding reductions in the Mean Absolute Percentage Error by 90 % (238 %) for production and 5 % (4 %) for yield in Burkina Faso (Somalia). While Burkina Faso's consistent crop data allowed for effective modeling with brief training, Somalia benefited from the adaptability of the PD model to crop statistics outliers, particularly with extended training in high-producing regions. The PD approach showed promise in addressing data gaps, although predicting crop productions for unobserved districts remained a challenge. Our findings highlight the harmonious integration of EO data and machine learning in the field of agricultural forecasting and emphasize the importance of region-specific methodologies, especially in the rapidly changing landscape of EO data convergence.

Case study on climate change effects and food security in Southeast Asia

Agriculture, a cornerstone of human civilization, faces rising challenges from climate change, resource limitations, and stagnating yields. Precise crop production forecasts are crucial for shaping trade policies, development strategies, and humanitarian initiatives. This study introduces a comprehensive machine learning framework designed to predict crop production. We leverage CMIP5 climate projections under a moderate carbon emission scenario to evaluate the future suitability of agricultural lands and incorporate climatic data, historical agricultural trends, and fertilizer usage to project yield changes. Our integrated approach forecasts significant regional variations in crop production across Southeast Asia by 2028, identifying potential cropland utilization. Specifically, the cropland area in Indonesia, Malaysia, Philippines, and Viet Nam is projected to decline by more than 10% if no action is taken, and there is potential to mitigate that loss. Moreover, rice production is projected to decline by 19% in Viet Nam and 7% in Thailand, while the Philippines may see a 5% increase compared to 2021 levels. Our findings underscore the critical impacts of climate change and human activities on agricultural productivity, offering essential insights for policy-making and fostering international cooperation.

ARIMA and ARIMAX Analysis on the Effect of Variability of Rainfall, Temperature on Wheat Yield in Haryana

The national and state governments require crop production forecasts to make a variety of policy decisions on import-export, storage, distribution, price, and other factors. This article presents a pre-harvest forecasting method specially developed for crops grown in the western region of Haryana (India). The western region includes Hisar, Sirsa, and Bhiwani districts. For crop forecasting in the Hisar, Sirsa, and Bhiwani regions ARIMA and ARIMAX models have been framed. For the development of the ARIMAX model, climate data during the growing season of the crop were used as input along with the crop yield. The percentage difference between the root mean square error and the wheat yield estimations determined by the real-time yield(s) indicates how well-competing models performed in terms of forecasting. The ARIMAX model performs well at all time points with a lower measurement error compared to the ARIMA model.

Application of Internet of Things Technologies in Agriculture

The development of agriculture in the Russian Federation and the Republic of Belarus includes implementing «smart systems» in agriculture based on modern wireless, intelligent technologies and the Internet of Things. This survey presents related works published in the last decade on the use of the Internet of Things to develop agriculture. The survey is based on publications from the scientific electronic library eLIBRARY.ru. We categorized the publications according to the areas of agricultural production as follows: animal husbandry, crop production, greenhouses and weather forecast, water management and irrigation, machinery management, mapping and geodesy, and digital platforms. The survey shows that in Russia and Belarus IoT technologies are developing in agriculture intensively as in advanced countries.

Technological Applications in Smart Farming: A Bibliometric Analysis

Precision agriculture, often known as smart farming, employs cutting-edge technology to change farming processes. This paper covers smart farming's history, important advancements, and possible impacts on agriculture. The essay begins with the history of "smart farming," or precision agriculture, and how GPS technology improved fertilization, pest control, and irrigation. Since then, automation and sensor technology have enabled continuous crop vitality, moisture, and environmental monitoring, enabling empirical decision-making. Networking technologies, especially the Internet of Things, have transformed smart farming. Farmers may gather, analyze, and apply real-time data using connected drones, satellite images, and farm management software. Big data analytics and AI allow farmers to leverage enormous datasets for crop health monitoring, production forecasts, and resource management. Smart farming improves accuracy and sustainability. By using resources efficiently, farmers may reduce their environmental impact. Due to remote monitoring and control, farmers can adapt to changing conditions and manage their operations from anywhere. Smart farming has numerous benefits, but various barriers limit its broad implementation. Due to expensive startup costs, specialized training, and limited Internet connection, some farmers are unwilling to use these technologies. Governments, NGOs, and industry actors must collaborate to provide financing, training, and improved connections to address these difficulties. This review and study of smart farming's growth highlights its revolutionary potential in agriculture. Smart farming may enhance output, optimize input returns, and ensure long-term viability, making it an exciting new route for agriculture.

A Global Forecasting Approach to Large-Scale Crop Production Prediction with Time Series Transformers

Accurate prediction of crop production is essential in effectively managing the food security and economic resilience of agricultural countries. This study evaluates the performance of statistical and machine learning-based methods for large-scale crop production forecasting. We predict the quarterly production of 325 crops (including fruits, vegetables, cereals, non-food, and industrial crops) across 83 provinces in the Philippines. Using a comprehensive dataset of 10,949 time series over 13 years, we demonstrate that a global forecasting approach using a state-of-the-art deep learning architecture, the transformer, significantly outperforms popular tree-based machine learning techniques and traditional local forecasting approaches built on statistical and baseline methods. Our results show a significant 84.93%, 80.69%, and 79.54% improvement in normalized root mean squared error (NRMSE), normalized deviation (ND), and modified symmetric mean absolute percentage error (msMAPE), respectively, over the next-best methods. By leveraging cross-series information, our proposed method is scalable and works well even with time series that are short, sparse, intermittent, or exhibit structural breaks/regime shifts. The results of this study further advance the field of applied forecasting in agricultural production and provide a practical and effective decision-support tool for policymakers that oversee crop production and the agriculture sector on a national scale.

Forecasting Analysis of Rice Production and Sufficiency Consumption of Rice (Oriza sativa) in Konawe District

Rice is an essential food crop because rice is still used as a staple food and is a strategic commodity in Indonesia. The availability of affordable rice for the community is one of the key factors in national security. Food crop production forecasts are needed to support government policies in overcoming food problems, especially rice in Indonesia. This study aims to determine the estimates for the amount of rice production in Konawe Regency in the future by using the Least Squares method, knowing the adequacy of rice in Konawe Regency. This research was conducted in Konawe Regency, Southeast Sulawesi, from May to December 2021. The data source used in this study was secondary data obtained from the Southeast Sulawesi Central Statistics Agency (BPS) and the Agriculture Service. Data on the rice production in Konawe Regency for the 2000-2020 period. From the simulations carried out using the Least Squares model, it is obtained that the forecasting results of rice production in Konawe Regency will continue to increase from year to year for the next ten years (2021-2030). The average increase in rice production in the next ten years is 1.60% per year, with the Konawe Regency rice consumption need in 2020 of 49,594.08 tons. Based on rice production and demand, the total availability of rice in Konawe Regency in 2021 is 74,585.5 tons, indicating a status of surplus rice availability. Determination of the accuracy of forecasting rice production in Konawe Regency is calculated using the Mean Absolute Percentage Error (MAPE) which produces a relatively small error value of 1.3%.

Crop Type Maps for Operational Global Agricultural Monitoring

Crop type maps identify the spatial distribution of crop types and underpin a large range of agricultural monitoring applications ranging from early warning of crop shortfalls, crop condition assessments, production forecasts, and damage assessment from extreme weather, to agricultural statistics, agricultural insurance, and climate mitigation and adaptation decisions. Despite their importance, harmonized, up-to-date global crop type maps of the main food commodities do not exist to date. To address this critical data gap of global-scale consistent, up-to-date crop type maps, we harmonized 24 national and regional datasets from 21 sources covering 66 countries to develop a set of Best Available Crop Specific masks (BACS) over the major production and export countries for wheat, maize, rice, and soybeans, in the context of the G20 Global Agriculture Monitoring Program, GEOGLAM.

Agri-PAD: a scalable framework for smart agriculture

<span lang="EN-US">More recently, big data tools and technologies have been applied in the agriculture sector leading to major benefits. Many frameworks have been proposed that employ big data technologies in the field of agriculture, however, such existing frameworks are focused on a particular aspect of agriculture and do not consider multiple stakeholders and applications. The objective of this research is to develop a holistic framework named Agri-PAD that encompasses almost all aspects of agriculture including crop selection, crop monitoring, soil monitoring, weather conditions, precision farming, and market demand. The Agri-PAD framework includes three major categories of machine learning based agriculture applications that is precision, recommendation, and enterprise applications. The Agri-PAD framework is capable of providing remote sensing of fields, precision farming, effective supply chain, and support informed decision making leading to enhanced productivity. To validate the efficacy of the proposed framework, the two most prominent agricultural applications, crop production forecasting and crop harvesting recommendation have been investigated and accuracy of 99% has been achieved. We believe that the Agri-PAD framework enables all stakeholders in the agriculture cycle to connect and apply big data analytics at every step leading to a more efficient and smarter agriculture ecosystem.</span>

Information rigidities in USDA crop production forecasts

AbstractUSDA invests significant public resources into developing its crop projection reports. These publications inform decisions across the supply chain. Several previous studies find that revisions to the department's production and yield forecasts for major agricultural commodities are positively correlated and conclude that they deviate from what would be observed under rational expectations, possibly due to smoothing on the part of forecasters. Yet correlated revisions may also be explained by information rigidities that cause forecasts to be infrequently or only partially updated. We apply a recently developed test to these USDA revisions for corn, soybeans, and wheat, and find no significant evidence that the forecasts are smoothed strategically. Rather, we show that information rigidities are the more likely culprit, due to production and yield information that is either too costly to obtain or too noisy. Our results demonstrate that data challenges are the main source of inefficiency in USDA projections, and that the department can improve the efficiency of its forecasts by making investments that improve its access to crop data, perhaps through crop‐monitoring satellite and remote sensing technology.

Spatial Variability of Bt Cotton On-farm Situation: A Survey

The survey was carried out during November 2015 in selected cotton fields of seven villages viz., Kalmala, Jagir Venkatapur and Ijapur of Raichur district and Gogi, Ulalkal, Hothpet and Maddarki villages of Yadgir districts covering part of TBP and UKP irrigation commands, respectively. The villages were selected based on the predominance of cotton area. Farmers plots were visited and location were recorded using GPS and observations were made on management practices, leaf reddening incidence and per cent leaf reddening, NDVI values, chlorophyll content, anthocyanin content and seed cotton yield. The spatial variability maps were generated using “krigging”, and interpolation method under GIS environment using observations on spatial spectral variability in NDVI, SPAD (chlorophyll content), anthocyanin content, leaf reddening index, reddening percentage and seed cotton as influenced by irrigation ecosystem, soil type and foliar spray of 19:19:19, date of sowing and cultivars. The data was further subjected for correlation studies. There is a lot of variation in the seed cotton yields among Bt cotton farmers across the Upper Krishna project and Tunga Bhadra Project irrigation commands of the state. Soils cultivated and cultivars used often remain the same the difference in yield could be due to nutritional management, the occurrence of leaf reddening, other factors, or combination of many of these attributing characteristics. Since leaf health is indicative of plant health and yield in turn, leaf health could be assessed through leaf spectral reflectance viz, NDVI, SPAD, and LRI. Based on this a survey was undertaken in November 2015 at Raichur (in three taluks) and Yadgir (in four taluks) districts falling under TBP and UKP irrigation commands, respectively, taking in all 90 farmers. The spectral observations were made on standing field crops besides leaf samples were collected to estimate leaf anthocyanin in the laboratory and GIS mapping was done using ESRI-made ArcGIS version 10.4 from Sujala-III, Remote Sensing and GIS laboratory, College of Agriculture, Raichur, was used to import GPS data and field data attachment for spatial analysis of leaf reddening incidence in cotton. The results on Spectral observations on NDVI, SPAD, leaf reddening index, reddening percentage in addition to leaf anthocyanin content varied due to locations, irrigation or otherwise, foliar spray, fertilization level, date of sowing, and cultivars.The leaf chlorophyll content is an important biometric character, the content quality, and duration (stability) indicate general crop health and ultimately yield directly if not influenced by the rate and efficiency of translocation to sink (kapas yield in case of cotton), therefore, it is often used to monitor real-timeN fertilization (LCC, SPAD, Green Seeker) as well as to forecast crop condition and yield. Periodic spectral observations on SPAD and NDVI are useful indicators of crop health and performance in commercial crop like cotton which could be extrapolated to field scale for crop management and production forecast.

In-Season Wheat Yield Forecasting at High Resolution Using Regional Climate Model and Crop Model

In-season crop production forecasts at the regional or sub-regional scale are essential to aid in food security through early warning of harvest shortfall/surplus, tailoring crop management decisions and addressing climatic shock. Considering the efforts to establish a framework towards quantifying the crop yield prediction at regional scales are limited, we investigated the utility of combining crop model with the regional weather prediction model to forecast winter wheat yields over space. The exercise was performed for various lead-times in the regions of Punjab and Haryana for the years 2008–2009. A numerical weather prediction (NWP) model was used to generate micro-meteorological variables at different lead times (1-week, 2-weeks, 3-weeks and 5-weeks) ahead of crop harvest and used within the CERES-Wheat crop simulation model gridded framework at a spatial resolution of 10 km. Various scenarios of the yield forecasts were verified with district-wide reported yield values. Average deviations of −12 to 3% from the actual district-wise wheat yields were observed across the lead times. The 3-weeks-ahead yield forecasts yielded a maximum agreement index of 0.86 with a root mean squared error (RMSE) of 327.75 kg/ha and a relative deviation of −5.35%. The critical crop growth stages were found to be highly sensitive to the errors in the weather forecast, and thus made a huge impact on the predicted crop yields. The 5-weeks-ahead weather forecasts generated anomalous meteorological data during flowering and grain-filling crop growth stages, and thus had the highest negative impact on the simulated yields. The agreement index of the 5-week-ahead forecasts was 0.41 with an RMSE of 415.15 kg ha−1 and relative deviation of −2.77 ± 5.01. The proposed methodology showed significant forecast skill for extended space and time scale crop yield forecasting, offering scope for further research and practical applicability.

Comparison of weather-based wheat yield forecasting models for different districts of Uttarakhand using statistical and machine learning Techniques

The prediction of crop yield before harvest is crucial for facilitating the formulation and implementation of policies about food safety, transportation cost, and import-export, storage and marketing of agro-products. The weather plays a crucial role in crop growth and development. Therefore, models using weather variables can provide reliable forecasts for crop yield and choosing the right model for crop production forecasts can be difficult. Therefore in the present study, an attempt was made to find the best model for wheat yield forecast by using five different techniques viz. Stepwise Multiple Linear Regression (SMLR), Artificial Neural Network (ANN), Least Absolute Shrinkage and Selection Operator (LASSO), Elastic Net (ELNET) and Ridge regression. Historical wheat yield data (taken from the Directorate of Economics and Statistics, Ministry of Agriculture and Farmers Welfare) and weather data of past 18-20 years were collected for seven different districts of Uttarakhand. Analysis was carried out by fixing 80% of the data for calibration and remaining dataset for validation. The present study concluded that the performance of ANN was good for crop yield forecasting as compared to the other models based on the value of RMSE (0.005 - 0.474) and nRMSE (0.166 - 26.171).

A Review on Applications and Utility of Remote Sensing and Geographic Information Systems in Agriculture and Natural Resource Management

Emerging technologies include remote sensing, global positioning systems (GPS), geographic information systems (GIS), and the Internet of Things. The Internet of Things (IoT), Big Data analysis, and artificial intelligence (AI) are all the promising tools that are being used to solve complications, improve agricultural operations, and reduce expenses. Satellite remote sensing has been indispensable in understanding Earth and atmospheric dynamics over the last five decades. When compared to ground or aerial sensor acquisitions, satellite sensors have the ability to provide data at global sizes at a lower cost. With the support of satellite remote sensing, the scientific community has attained significant progress in recent years. In consideration of these efforts, the current study is intended to provide a comprehensive review of the function of remote sensing in assessing different water security challenges and other purposes. Crop production forecasting, drought assessment, cropping system analysis, horticultural assessment and development, crop development, thorough site analysis, satellite agro-meteorology, precision farming, crop insurance, and other operational big agricultural applications are examples. This research examines various uses as well as potential gaps in the market.

Using real-time mobile phone data to characterize the relationships between small-scale farmers’ planting dates and socio-environmental factors

Accurate and operational indicators of the start of growing season (SOS) are critical for crop modeling, famine early warning, and agricultural management in the developing world. Erroneous SOS estimates–late, or early, relative to actual planting dates–can lead to inaccurate crop production and food-availability forecasts. Adapting rainfed agriculture to climate change requires improved harmonization of planting with the onset of rains, and the rising ubiquity of mobile phones in east Africa enables real-time monitoring of this important agricultural decision. We investigate whether antecedent agro-meteorological variables and household-level attributes can be used to predict planting dates of small-scale maize producers in central Kenya. Using random forest models, we compare remote estimates of SOS with field-level survey data of actual planting dates. We compare three years of planting dates (2016–2018) for two rainy seasons (the October-to-December short rains, and the March-to-May long rains) gathered from weekly Short Message Service (SMS) mobile phone surveys. In situ data are compared to SOS from the Water Requirement Satisfaction Index (SOSWRSI) and other agro-meteorological variables from Earth observation (EO) datasets (rainfall, NDVI, and evaporative demand). The majority of farmers planted within 20 days of the SOSWRSI from 2016 to 2018. In the 2016 long rains season, many farmers reported planting late, which corresponds to drought conditions. We find that models relying solely on EO variables perform as well as models using both socio-economic and EO variables. The predictive accuracy of EO variables appears to be insensitive to differences in reference periods that were tested for deriving EO anomalies (1, 3, 5, or 10 years). As such, it would appear that farmers are either responding to short-term weather conditions (e.g., intra-seasonal variability), or longer trends than were included in this study (e.g., 25–30 years), when planting. The methodologies used in this study, weekly SMS surveys, provide an operational means for estimating farmer behaviors–information which is traditionally difficult and costly to collect.

The ARYA crop yield forecasting algorithm: Application to the main wheat exporting countries

Wheat is the most important commodity traded in the international food market. Thus, accurate and timely information on wheat production can help mitigate food price fluctuations. Within the existing operational regional and global scale agricultural monitoring systems that provide information on global crop yield and area forecasts, there are still fundamental gaps: #1. Lack of quantitative Earth Observation (EO) derived crop information, #2. Lack of global but detailed (national or subnational level) and timely crop production forecasts and #3. Lack of information on forecast uncertainties. In this study we present the Agriculture Remotely-sensed Yield Algorithm (ARYA) an EO-based method, advancing the state of EO-data application and usage (addressing gap #1) to forecast wheat yield. The algorithm is based on the evolution of the Difference Vegetation Index (DVI) using MODIS data at 1 km resolution and the Growing Degree Days (GDD) from reanalysis data. Additionally, we explore how Land Surface Temperature (LST) can be included into the model and whether this parameter adds any value to the model performance when combined with the optical information. ARYA is implemented at the national and subnational level to forecast winter wheat yield in the main wheat exporting countries of US, Russia, Ukraine, France, Germany, Australia and Argentina from 2001 to 2019 (covering over 70% of wheat exports globally) in a timely manner by providing daily forecasts (addressing gap #2). The results show that ARYA provides yield estimations with RMSE’s within 0.3 ± 0.1 t/ha at national level and 0.6 ± 0,1 t/ha at subnational level after Day Of the Year (DOY) 140 (mid May) in the Northern Hemisphere and DOY 280 (beginning of October) in the Southern Hemisphere. This means that ARYA can provide crop yield estimates of wheat yield with 5–15% error at national and 7–20% error at subnational level starting from 2 to 2.5 months prior to harvest.

Can Private Forecasters Beat the USDA? Analysis of Relative Accuracy of Crop Acreage and Production Forecasts

AbstractThis study examines the accuracy of United States Department of Agriculture (USDA) crop Acreage and Production forecasts for corn, soybeans, and winter wheat relative to their private counterparts over 1970–2019. Our main findings suggest that USDA forecasts often had significantly smaller errors than their private counterparts. The accuracy of both USDA and private forecasts has improved over time, but the accuracy of USDA forecasts has improved more than that of private forecasts, maintaining the USDA’s relative accuracy advantage. The accuracy advantage of Prospective Plantings and Acreage reports highlights the importance of survey-based approaches used for these forecasts.

Comparative Study on Fuzzy Models for Crop Production Forecasting

Fuzzy sets theory is a very useful technique to increase effectiveness and efficiency of forecasting. The conventional time series is not applicable when the variable of time series are words variables i.e. variables with linguistic terms. As India and most of the Asian countries are of agriculture-based economy with very smaller farmer land holding area in comparison to America, Australia and Europe counterparts, it becomes more important for these countries to have an approximate idea regarding future crop production. It not only will help in planning policies for future but also will be a great help for farmers and agro based companies for their future managements. For small area production, soft computing technique is an important and effective tool for predicting production, as agriculture production involve a high degree of uncertainties in many parameters. In the present study, 21 years agricultural crop yield data is used and a comparative analysis of forecast is done with three fuzzy models. The robustness of the model is tested on real time agricultural farm production data of wheat crop of G.B. Pant University of Agriculture and Technology Pantnagar, India. As soft computing techniques involve uncertainty of the system under study, it becomes more and more important for forecasting models to be accurate with the prediction. The efficiency of the three models is examined on the basis of statistical errors. The models under study are judged on the basis of Mean Square Error and average percentage error. The results of the study are in case of small area production prediction and will encourage for predicting large scale production.

Validation of white oat yield estimation models using vegetation indices

ABSTRACT The use of remote sensing in agriculture presents some practical applications in crop production forecast. In this context, studies with remote sensing are scarce for crops such as white oats, which may indicate the capacity of using this technique in the crop. The aim of this study was to evaluate the accuracy in validation of white oat biomass and grain yield estimates by spectral models previously calibrated using two vegetation indices (NDVI and IRVI) at three phenological stages. The mean values of NDVI and IRVI were correlated with the grain and biomass yield of white oats to obtain regression equations. The accuracy was verified by the determination coefficient (R2), root mean square error (RMSE) and mean bias error (MBE). The models were calibrated using data from a field experiment carried out in 2017 and validated with data from the same experiment, but conducted in 2018. The models had good generalization capacity for estimating yield of white oats, especially for biomass yield. Parametrized models in more advanced phenological stages, showed lower error of estimation. Models calibrated with the vegetation index IRVI had lower error of estimation than when calibrated with NDVI.

Integrated Application of Remote Sensing and GIS in Crop Information System—A Case Study on Aman Rice Production Forecasting Using MODIS-NDVI in Bangladesh

This research work studies the integrated application of satellite Remote Sensing (RS) and Geographic Information System (GIS) for the monitoring and forecasting of rice crop (Aman) production in Bangladesh. Normalized Difference Vegetation Index (NDVI) images of Terra MODIS products MOD13A1 (h25v06 and h26v06) with 500 m spatial resolution, composed using Maximum Value Composite (MVC) techniques, were used to cover Bangladesh for the period of 2011–2017. Country scale NDVI (district-wise summation) was calculated pixel-by-pixel to draw a regression curve while using Bangladesh Bureau of Statistics (BBS) estimations of Aman production for the months of September–November. The regression study of district-wise pixel-based summation of MODIS-NDVI and ground-based BBS-estimated Aman production shows a strong correlation (R2 = 0.54–0.78); for the months of September and October, most of the regression coefficient indicates significant correlation due to maximum photosynthetic activities. Therefore, based on the highest regression coefficient value of September and October, Aman Crop Production (ACP) models were developed and the ACP Model-2 was exploited (from the derived set of coefficient values) to acquire year-wise rice production for all the years (2011–2017). The simulated ACP Model-2 demonstrates good agreement between the estimated and predicted yearly Aman rice production for the 2011–2017 time period with Mean Bias Error (MBE) = (−9435 to 23,156) M.Ton; Root Mean Square Error (RMSE) = 253–4426 M.Ton; Model Efficiency (ME) = (0.89–0.93); and, Correlation Coefficients = (0.72–0.94). Hence, the MODIS–NDVI-based regression model seems to be effective for Aman crop production forecasting in the context of food security issues in Bangladesh. The applied system is simple, rationally accurate, and fit for the generation of nationwide crop statistics.