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Journal articles on the topic 'Crop forecasting'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Mariño, Miguel A., John C. Tracy, and S. Alireza Taghavi. "Forecasting of reference crop evapotranspiration." Agricultural Water Management 24, no. 3 (1993): 163–87. http://dx.doi.org/10.1016/0378-3774(93)90022-3.

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2

Buklagin, D. S. "Agricultural crop yield forecasting methods." Machinery and Equipment for Rural Area, no. 12 (December 20, 2020): 25–28. http://dx.doi.org/10.33267/2072-9642-2020-12-25-28.

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The main areas of the development and use of digital technologies and systems for forecasting the yield of agricultural crops based on satellite data are described. Proposals are given for the development of research in the field of the use of space technologies and their widespread use in agriculture.

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3

Veenadhari, Dr S. "Crop Advisor: A Software Tool for Forecasting Paddy Yield." Bonfring International Journal of Data Mining 6, no. 3 (2016): 34–38. http://dx.doi.org/10.9756/bijdm.10461.

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4

Gotsch, N., and P. Rieder. "Forecasting future developments in crop protection." Crop Protection 9, no. 2 (1990): 83–89. http://dx.doi.org/10.1016/0261-2194(90)90083-j.

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5

MOHAN, S., and N. ARUMUGAM. "Forecasting weekly reference crop evapotranspiration series." Hydrological Sciences Journal 40, no. 6 (1995): 689–702. http://dx.doi.org/10.1080/02626669509491459.

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6

Stone, Roger C., and Holger Meinke. "Operational seasonal forecasting of crop performance." Philosophical Transactions of the Royal Society B: Biological Sciences 360, no. 1463 (2005): 2109–24. http://dx.doi.org/10.1098/rstb.2005.1753.

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Integrated, interdisciplinary crop performance forecasting systems, linked with appropriate decision and discussion support tools, could substantially improve operational decision making in agricultural management. Recent developments in connecting numerical weather prediction models and general circulation models with quantitative crop growth models offer the potential for development of integrated systems that incorporate components of long-term climate change. However, operational seasonal forecasting systems have little or no value unless they are able to change key management decisions. C

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7

Ben Dhiab, Ali, Mehdi Ben Mimoun, Jose Oteros, et al. "Modeling olive-crop forecasting in Tunisia." Theoretical and Applied Climatology 128, no. 3-4 (2016): 541–49. http://dx.doi.org/10.1007/s00704-015-1726-1.

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8

Jain, R. C., and Ranjana Agrawal. "Probability Model for Crop Yield Forecasting." Biometrical Journal 34, no. 4 (1992): 501–11. http://dx.doi.org/10.1002/bimj.4710340410.

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9

Biibosunov, Bolotbek, Baratbek Sabitov, Saltanat Biibosunova, Zhamin Sheishenov, Sharshenbek Zhusupkeldiev, and Zhyldyz Mamadalieva. "Machine learning for crop yield forecasting." Cybernetics and Physics, Volume 12, 2023, Number 3 (November 30, 2023): 174–81. http://dx.doi.org/10.35470/2226-4116-2023-12-3-174-181.

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Amid the persistent rise in global population, there has been a heightened focus on food security by academia, governmental initiatives, and international endeavors. Food security serves as a critical pillar in the national security framework, contributing to a nation’s sovereignty and self-sufficiency in food supply. To fulfill global requirements for essential food items, there is an imperative need to enhance agricultural efficiency across countries. Concurrently, agricultural practices must align with contemporary quality standards and meet consumer needs, drawing upon an integrated approa

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10

Bhatakar, Ajinkya, Lalit Tayde, Sandesh Raut, Ankit Pakhare, Tejas Bavaskar, and Shivaji Chavhan. "AGRICULTURE CROP PRICE PREDICTION USING MACHINE LEARNING." International Scientific Journal of Engineering and Management 04, no. 03 (2025): 1–9. https://doi.org/10.55041/isjem02521.

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The accurate forecast of crop prices is of considerable importance to farmers, policymakers, and stakeholders to enable informed decisions and ensure economic stability in the agricultural sector. The traditional forecasting methods are largely ineffective when it comes to accurate forecasting, due to the complex and dynamic nature of the agricultural market. This study proposes a machine learning based solution to effectively forecast crop prices. Various machine learning models such as regression, decision trees, and neural networks were used to analyze and forecast crop prices using histori

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11

Guo, William W., and Heru Xue. "Crop Yield Forecasting Using Artificial Neural Networks: A Comparison between Spatial and Temporal Models." Mathematical Problems in Engineering 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/857865.

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Our recent study using historic data of wheat yield and associated plantation area, rainfall, and temperature has shown that incorporating statistics and artificial neural networks can produce highly satisfactory forecasting of wheat yield. However, no comparison has been made between the outcomes from the spatial neural network model and commonly used temporal neural network models in crop forecasting. This paper presents the latest research outcomes from using both the spatial and temporal neural network models in crop forecasting. Our simulation shows that the spatial NN model is able to pr

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12

Son, D. V. "SOIL YIELD FORECASTING." Bulletin of Shakarim University. Technical Sciences 1, no. 4(16) (2024): 72–80. https://doi.org/10.53360/2788-7995-2024-4(16)-10.

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This research project serves as a comprehensive meta-analysis in the field of agricultural science, specifically focusing on the prediction of crop yields. This endeavor involves collating and synthesizing findings from a variety of studies and articles that have explored different methodologies and models for forecasting agricultural outputs. The objective of this comprehensive review is to identify trends, methodologies, and key factors that consistently influence crop yield predictions across different studies.It synthesizes methodologies from various studies, emphasizing machine learning (

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13

Matsikira, Normias, Gideon Mazambani, and Martin Muduva. "A Comparative Study of Ensemble Classification Algorithms for Crop Yield Forecasting." Advances in Machine Learning & Artificial Intelligence 6, no. 1 (2025): 01–05. https://doi.org/10.33140/amlai.06.01.05.

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This study explores the application of ensemble learning techniques to improve predictive model accuracy. It focuses on combining classifiers to outperform individual models using structured and unstructured data from agricultural datasets. Artificial neural networks (ANNs) and ensemble methods were used to increase deep neural network efficiency. Experiments with different network structures, training iterations, and topologies were conducted, evaluating measures like sensitivity and specificity. The research also includes predicting crop yields using ensemble classification algorithms, compa

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14

Young, Linda J. "Agricultural Crop Forecasting for Large Geographical Areas." Annual Review of Statistics and Its Application 6, no. 1 (2019): 173–96. http://dx.doi.org/10.1146/annurev-statistics-030718-105002.

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Crop forecasting is important to national and international trade and food security. Although sample surveys continue to have a role in many national crop forecasting programs, the increasing challenges of list frame undercoverage, declining response rates, increasing response burden, and increasing costs are leading government agencies to replace some or all of survey data with data from other sources. This article reviews the primary approaches currently being used to produce official statistics, including surveys, remote sensing, and the integration of these with meteorological, administrat

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15

Pramod Kumar Dwivedi and Dr. Prabhat Pandey. "Propose and implement a Rule-Based System to Predict Crop Yield Production." Journal of Advances in Science and Technology 21, no. 1 (2024): 28–34. http://dx.doi.org/10.29070/bs9vmv33.

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Accurate meteorological forecasting plays a pivotal role in agricultural decision-making, particularly in determining crop yield potential and optimizing agricultural practices. This study investigates the application of data mining techniques in meteorological forecasting to enhance crop yield prediction accuracy Preliminary findings suggest that data mining techniques, including machine learning algorithms, neural networks, and ensemble methods, offer significant potential for improving the accuracy and reliability of meteorological forecasts for crop yield prediction. In conclusion, this st

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16

Bojar, W., L. Knopik, J. Żarski, and R. Kuśmierek-Tomaszewska. "Integrated assessment of crop productivity based on the food supply forecasting." Agricultural Economics (Zemědělská ekonomika) 61, No. 11 (2016): 502–10. http://dx.doi.org/10.17221/159/2014-agricecon.

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17

Phadnis, Arya. "Implementation of Prediction of Crop Using SVM Algorithm." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 3812–16. http://dx.doi.org/10.22214/ijraset.2023.52265.

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Abstract: Crop forecasting is the process of predicting the yield or production of crops for a given period based on historical data, weather and other relevant factors. The prediction can be used to inform crop planting, harvesting and marketing decisions. Machine learning and artificial intelligence techniques are increasingly being used to improve the accuracy of crop forecasting. These techniques use algorithms to analyze large amounts of data, such as weather patterns, soil conditions, and crop history, to predict future crop yields. Crop prediction models can be used by farmers, agribusi

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18

Gardner, A. S., I. M. D. Maclean, K. J. Gaston, and L. Bütikofer. "Forecasting future crop suitability with microclimate data." Agricultural Systems 190 (May 2021): 103084. http://dx.doi.org/10.1016/j.agsy.2021.103084.

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19

SAITO, Genya. "Crop Yield Forecasting Using Remote Sensing Technique." JOURNAL OF THE BREWING SOCIETY OF JAPAN 86, no. 1 (1991): 2–7. http://dx.doi.org/10.6013/jbrewsocjapan1988.86.2.

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20

Mayer, D. G., and R. A. Stephenson. "Statistical forecasting of the Australian macadamia crop." Acta Horticulturae, no. 1109 (February 2016): 265–70. http://dx.doi.org/10.17660/actahortic.2016.1109.43.

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21

Singh, R. K., T. R. Singh, and U. Kaushal. "Note on the Crop Yield Forecasting Methods." Asian Journal of Agricultural Research 13, no. 1 (2018): 1–5. http://dx.doi.org/10.3923/ajar.2019.1.5.

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22

Saengseedam, Panudet, and Nantachai Kantanantha. "Spatio-temporal model for crop yield forecasting." Journal of Applied Statistics 44, no. 3 (2016): 427–40. http://dx.doi.org/10.1080/02664763.2016.1174197.

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23

S, Saritha, and Abel Thangaraja G. "Survival and Comparative study on Different Artificial Intelligence Techniques for Crop Yield Prediction." International Journal of Computer Communication and Informatics 5, no. 1 (2023): 1–14. http://dx.doi.org/10.34256/ijcci2311.

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Agriculture is an essential, important sector in the wide-reaching context. Farming helps to satisfy the basic need of food for every living being. Agriculture is considered the broadest economic sector. The crop yield is a significant part of food security and improves the drastic manner by human population. The quality and quantity of the yield touch the high rate of production. Farmers require timely advice to predict crop productivity. The strategic analysis also helps to increase crop production to meet the growing food demand. The forecasting of crop yield is a process of forecasting cro

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24

Guenthner, Joseph F. "Forecasting Annual Vegetable Plantings." HortTechnology 2, no. 1 (1992): 89–91. http://dx.doi.org/10.21273/horttech.2.1.89.

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Vegetable producers and marketers make business decisions based on supply estimates. The U.S. Dept. of Agriculture provides estimates of planting intentions for field crops but not for most vegetable crops. This study developed models that can be used to forecast vegetable crop plantings. Multiple linear regression analysis was used to determine the factors that influence plantings of potatoes and onions. Field crop planting intentions, industry structure, lagged values of plantings, prices received, price volatility, and the price of sugar beets were found to be significant factors. The model

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25

PANWAR, SANJEEV, ANIL KUMAR, K. N. SINGH, et al. "Forecasting of crop yield using weather parameters - two step nonlinear regression model approach." Indian Journal of Agricultural Sciences 88, no. 10 (2023): 1597–99. http://dx.doi.org/10.56093/ijas.v88i10.84230.

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Concept of the paper is firstly to remove the trend of crop yield and then to develop the forecasting models using detrended yield. Not much work is available or development of forecast models or modelling due to their non-linear behaviour. For that, in this paper, methodology developed for forecasting using nonlinear growth models, which will help in forecasting yield, pest and disease incidences etc with high accuracy. Crop yield forecast models for wheat crop have been developed (using non-linear growth models, linear models and weather indices approach with weekly weather data) for differe

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26

Gautam, Ratnesh, and Anand K. Sinha. "Time series analysis of reference crop evapotranspiration for Bokaro District, Jharkhand, India." Journal of Water and Land Development 30, no. 1 (2016): 51–56. http://dx.doi.org/10.1515/jwld-2016-0021.

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AbstractEvapotranspiration is the one of the major role playing element in water cycle. More accurate measurement and forecasting of Evapotranspiration would enable more efficient water resources management. This study, is therefore, particularly focused on evapotranspiration modelling and forecasting, since forecasting would provide better information for optimal water resources management. There are numerous techniques of evapotranspiration forecasting that include autoregressive (AR) and moving average (MA), autoregressive moving average (ARMA), autoregressive integrated moving average (ARI

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27

Mirpulatov, Islombek, Mikhail Gasanov, and Sergey Matveev. "Soil Dynamics and Crop Yield Modeling Using the MONICA Crop Simulation Model and Time Series Forecasting Methods." Agronomy 13, no. 8 (2023): 2185. http://dx.doi.org/10.3390/agronomy13082185.

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Crop simulation models are an important tool for assessing agroecosystem performance and the impact of agrotechnologies on soil cover condition. However, the high uncertainty and labor intensiveness of long-term weather forecasting limits the applicability of such models. A possible solution may be to use time series forecasting models (SARIMAX and Prophet) and artificial neural-network-based technologies (Neural Prophet). This work compares the applicability of these methods for modeling soil condition dynamics and agroecosystem performance using the MONICA simulation model for Voronic Cherno

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28

Bolton, Douglas K., and Mark A. Friedl. "Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics." Agricultural and Forest Meteorology 173 (May 2013): 74–84. http://dx.doi.org/10.1016/j.agrformet.2013.01.007.

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29

de Wit, A. J. W., and C. A. van Diepen. "Crop growth modelling and crop yield forecasting using satellite-derived meteorological inputs." International Journal of Applied Earth Observation and Geoinformation 10, no. 4 (2008): 414–25. http://dx.doi.org/10.1016/j.jag.2007.10.004.

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30

Zhu, Wenjun, Lysa Porth, and Ken Seng Tan. "A credibility-based yield forecasting model for crop reinsurance pricing and weather risk management." Agricultural Finance Review 79, no. 1 (2019): 2–26. http://dx.doi.org/10.1108/afr-08-2017-0064.

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Purpose The purpose of this paper is to propose an improved reinsurance pricing framework, which includes a crop yield forecasting model that integrates weather variables and crop production information from different geographically correlated regions using a new credibility estimator, and closed form reinsurance pricing formulas. A yield restatement approach to account for changing crop mix through time is also demonstrated. Design/methodology/approach The new crop yield forecasting model is empirically analyzed based on detailed farm-level data from Manitoba, Canada, covering 216 crop variet

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31

Kiran, D. Sai. "Crop Prediction Using Sensors and Machine Learning." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 5248–55. https://doi.org/10.22214/ijraset.2025.69641.

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Abstract: The agricultural industry is rapidly embracing emerging technologies to enhance crop forecasting and resource utilization. This project suggests an intelligent crop forecasting system based on IoT and machine learning for sustainable agriculture. Crop selection is usually done based on guesswork by the farmers, resulting in low harvest and wasted resources. Crop yield forecasting before harvesting is also a severe problem in developing countries. The system relies on Arduino-based hardware interfaced with sensors for measuring temperature, humidity, rain, soil moisture, and water lev

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32

Kalichkin, V. K., O. K. Alsova, K. Yu Maksimovich, and N. V. Vasilyeva. "Crop Contamination Forecasting Based on Machine-Learning Approaches." Russian Agricultural Sciences 48, no. 2 (2022): 115–22. http://dx.doi.org/10.3103/s1068367422020069.

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33

Ivanyo, Yaroslav M., Sofia A. Petrova, Margarita N. Barsukova, and Yuliana V. Stolopova. "Parametric programming problem with crop yield forecasting models." Journal Of Applied Informatics 16, no. 96 (2021): 131–43. http://dx.doi.org/10.37791/2687-0649-2021-16-6-131-143.

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The paper considers factor models that allow predicting the yield of agricultural crops. It is shown that the main climatic parameters that affect the effective feature are the air temperature and precipitation during the initial growing season. In this case, the factors of heat supply and moisture supply can represent values for both a month and another interval close to this duration. In addition to air temperature and precipitation, the yield of grain crops is affected by time. Models can reflect the relationship of the effective feature with factors at the level of experimental fields, agr

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34

T. S. G. Peiris. "FORECASTING THE CROP YIELD OF A COCONUT ESTATE." CORD 5, no. 02 (1989): 34. http://dx.doi.org/10.37833/cord.v5i02.226.

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Seasonal Autoregressive Integrated Moving Average (ARIMA) process of (0,1,2) x (0,1,1) x 6 that best fits a set of crop‑wise coconut yield data, in Bandirippuwa, Lunuwila is identified without using variance stabilization transformation. In this process the present value of the series may be described as a linear function of the past observation of the series and past disturbances. The physical factors such as rainfall, temperature, day length etc. are not required for this method, however the past crop figures in the estate is needed. While such model is useful for short term forecasting, i

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35

Laiskhanov, Sh U., M. N. Poshanov, A. A. Tokbergenova, K. B. Zulpykharov, and Zh M. Smanov. "FORECASTING CROP PRODUCTIVITY FOR FOOD SECURITY: A REVIEW." BULLETIN of the Korkyt Ata Kyzylorda University 1 (2024): 144–53. http://dx.doi.org/10.52081/bkaku.2024.v68.i1.137.

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Ауылшаруашылық дақылдардың өнімділігін болжау деректері Біріккен Ұлттар Ұйымының Тұрақты даму саласындағы "Нөлдік аштық" (SDG2) және "Құрлықтағы өмір" (SDG2) сияқты мақсаттарын жүзеге асыруға септігін тигізері сөзсіз. Сондықтан, әлемнің көптеген елдерінде ауыл шаруашылығы мен ауылдық статистиканы жақсартудың жаһандық стратегиясына арналған ерте ескерту жүйелерін әзірлеу бойынша зерттеулер жүргізілуде. Бұл мақалада, ғылыми материалдарды іздестірудің халықаралық жүйелері арқылы тақырып бойынша жарияланған ғылыми еңбектерге шолу жасалып, ауылшаруашылық дақылдардың өнімділігін болжау мәселесіне жә

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36

Toreti, A., A. Maiorano, G. De Sanctis, et al. "Using reanalysis in crop monitoring and forecasting systems." Agricultural Systems 168 (January 2019): 144–53. http://dx.doi.org/10.1016/j.agsy.2018.07.001.

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37

Dharmaraja, S., Vidyottama Jain, Priyanka Anjoy, and Hukum Chandra. "Empirical Analysis for Crop Yield Forecasting in India." Agricultural Research 9, no. 1 (2019): 132–38. http://dx.doi.org/10.1007/s40003-019-00413-x.

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38

Matis, J. H., T. Saito, W. E. Grant, W. C. Iwig, and J. T. Ritchie. "A Markov chain approach to crop yield forecasting." Agricultural Systems 18, no. 3 (1985): 171–87. http://dx.doi.org/10.1016/0308-521x(85)90030-7.

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39

Ghaly, Hanan. "FORECASTING WHEAT CROP PRODUCTION IN THE DESERT GOVERNORATES." Arab Universities Journal of Agricultural Sciences 24, no. 2 (2016): 387–98. http://dx.doi.org/10.21608/ajs.2016.14333.

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MAKSIMOVICH, KIRILL, OLGA ALSOVA, VLADIMIR KALICHKIN, and DMITRY FEDOROV. "Crop weed infestation forecasting using data mining methods." Turkish Journal of Agriculture and Forestry 47, no. 5 (2023): 662–68. http://dx.doi.org/10.55730/1300-011x.3118.

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41

Tripathi, Astha. "Forecasting Agricultural Crop Profitability and Yield in India." International Journal for Research in Applied Science and Engineering Technology 12, no. 2 (2024): 308–15. http://dx.doi.org/10.22214/ijraset.2024.58316.

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Abstract: "India's heavy reliance on agriculture underscores the importance of accurately estimating agricultural production, considering the interplay of organic, economic, and seasonal variables, particularly amid a growing population. Predicting crop yields is crucial for farmers' planning, covering storage and marketing strategies. However, this task is complex and requires foresight. Data mining techniques emerge as a potent tool, leveraging extensive datasets to extract invaluable insights. By employing methods like Random Forest, this research offers a swift yet comprehensive examinatio

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42

Patankar, Vasant N. "Knowledge Series: An Operational Approach to Crop Forecasting." NMIMS Management Review 14, no. 2 (2002): 52–66. https://doi.org/10.1177/0971102320020208.

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Balaji, Ramdas Magar, Dhanorkar Gajanan, Nalawade Nilesh, and Jakkewad Shrikant. "Harnessing Statistical Models for Enhanced Crop Production Forecasting." International Journal of Advance and Applied Research S6, no. 19 (2025): 58–65. https://doi.org/10.5281/zenodo.15100868.

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<em>An essential component to be used towards securing food needs and optimizing input distribution is providing proper crop forecasting. With this issue, the development of statistical crop- yield predictors from environmental, climatic,<strong> </strong>and agronomic sources in terms of both correlation analysis and regression method usage is explained within this context. The method in question makes an excellent indicator and quantified contributor to factors contributing to variations in agricultural produce. Dhanorkar[4] developed a mathematical model for blood diffusion.  Using his

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44

Kolotii, A., N. Kussul, A. Shelestov, et al. "Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W3 (April 28, 2015): 39–44. http://dx.doi.org/10.5194/isprsarchives-xl-7-w3-39-2015.

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Winter wheat crop yield forecasting at national, regional and local scales is an extremely important task. This paper aims at assessing the efficiency (in terms of prediction error minimization) of satellite and biophysical model based predictors assimilation into winter wheat crop yield forecasting models at different scales (region, county and field) for one of the regions in central part of Ukraine. Vegetation index NDVI, as well as different biophysical parameters (LAI and fAPAR) derived from satellite data and WOFOST crop growth model are considered as predictors of winter wheat crop yiel

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45

Lalić, B., A. Firanj Sremac, L. Dekić, J. Eitzinger, and D. Perišić. "Seasonal forecasting of green water components and crop yields of winter wheat in Serbia and Austria." Journal of Agricultural Science 156, no. 5 (2017): 645–57. http://dx.doi.org/10.1017/s0021859617000788.

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AbstractA probabilistic crop forecast based on ensembles of crop model output (CMO) estimates offers a myriad of possible realizations and probabilistic forecasts of green water components (precipitation and evapotranspiration), crop yields and green water footprints (GWFs) on monthly or seasonal scales. The present paper presents part of the results of an ongoing study related to the application of ensemble forecasting concepts for agricultural production. The methodology used to produce the ensemble CMO using the ensemble seasonal weather forecasts as the crop model input meteorological data

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Branislava, Lalic, Firanj Sremac Ana, Dekic Ljiljana, Eitzinger Josef, and Perisic Dusanka. "Seasonal forecasting of green water components and crop yields of winter wheat in Serbia and Austria." Journal of Agricultural Science 156, no. 6 (2017): 645–57. https://doi.org/10.1017/S0021859617000788.

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A probabilistic crop forecast based on ensembles of crop model output (CMO) estimates offers a myriad of possible realizations and probabilistic forecasts of green water components (precipitation and evapotranspiration), crop yields and green water footprints (GWFs) on monthly or seasonal scales. The present paper presents part of the results of an ongoing study related to the application of ensemble forecasting concepts for agricultural production. The methodology used to produce the ensemble CMO using the ensemble seasonal weather forecasts as the crop model input meteorological data without

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47

Malviya, Akash, and Prof Dilip Singh Solanki. "Crop Yield Prediction Using Deep Neural Networks." International Journal for Research in Applied Science and Engineering Technology 10, no. 8 (2022): 657–65. http://dx.doi.org/10.22214/ijraset.2022.46226.

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Abstract: Agriculture is undergoing a metamorphosis due to several environmantal and scoal factors. Due to challenges such as global warming, intermittent rainfall patterns and eroding nutrient values of soil, crop yileds have become more upredictable in the last decade. This has resulted in famines, armer suicides and deaths due to hunger. Thus, one of the key objectives of the world health organization is to provide food security globally and also help the agriculture community as a whole whith special emhpasis on low income group countries. This has made crop yield forecasting extremely imp

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SHAIMERDENOVA, A., LG AGAPITOVA, AV BOBROVA, et al. "DEVELOPMENT OF OPTIMAL CROP PRODUCTION MODEL CONSIDERING EXISTING NATURAL-CLIMATIC RISKS INCREASING CROP YIELDS." SABRAO Journal of Breeding and Genetics 55, no. 3 (2023): 778–95. http://dx.doi.org/10.54910/sabrao2023.55.3.15.

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Contemporary agriculture is a fertile ground for the effective use of economic and mathematical models, which can be evaluated to unwind several problems with characteristic optimization features: multiple solution opportunities and freedom of choice, limited production resources, and efficiency valuation. The presented study aims to develop a model of optimal crop production structure under the existing weather risks in the agricultural management system. The article reviews the basic theoretical concepts in optimizing the production structure of agricultural enterprises, examines the specifi

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Makhmudova, N. R., K. K. Shadmanov, N. T. Kodirova, N. H. Samigova, and D. Z. Narzullaev. "Econometric studies in forecasting increased yield productivity of agricultural crops." E3S Web of Conferences 494 (2024): 04040. http://dx.doi.org/10.1051/e3sconf/202449404040.

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The article, based on econometric research, examines the relevance of econometric methods of planning and forecasting, as well as methods for compiling econometric models for quantitative analysis and forecasting of agricultural crop yields. In addition, using methods of analysis and forecasting of econometric modeling based on experimental data, the work examined the trend in the dynamics of agricultural crop yields and made a forecast for the future using compiled linear trend equations, as well as provided feedback and suggestions on the results of the study.

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Challinor, Andrew Juan. "Assessing crop genetic resources for adaptation using ensemble climate and crop yield forecasting." IOP Conference Series: Earth and Environmental Science 6, no. 37 (2009): 372011. http://dx.doi.org/10.1088/1755-1307/6/37/372011.

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