Relevant bibliographies by topics / Crop yield

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Crop yield'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Crop yield"

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Peterson, Todd Andrews, Charles A. Shapiro, and A. Dale Flowerday. "Rainfall and previous crop effects on crop yields." American Journal of Alternative Agriculture 5, no. 1 (1990): 33–37. http://dx.doi.org/10.1017/s0889189300003209.

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AbstractAfield study was conducted between 1972 and 1982 to compare the effects of previous crop on row crop yields under rainfed conditions in eastern Nebraska. The objectives were to determine the effects of fallow and three previous crops: corn (Lea. maysLJ, soybeans /Glycine max (L.) Mem], and grain sorghum /Sorghum bicolor (L.) Moench], on the growth and grain yield of the same crops. The study was conducted on a Sharpsburg silty clay loam (fine, montmorillonitic, mesicf Typic Argiudoll). Corn grain yield was most variable (C. V. 23.4percent) compared to soybean (C. V. 13.6percent) or gra
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. R, Saravanan, and Arulselvan Gnanamonickam . A. "Crop Yield Prediction using Machine Learning." International Journal of Research Publication and Reviews 5, no. 10 (2024): 2433–39. http://dx.doi.org/10.55248/gengpi.5.1024.2825.

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Eisenhut, Marion, and Andreas P. M. Weber. "Improving crop yield." Science 363, no. 6422 (2019): 32–33. http://dx.doi.org/10.1126/science.aav8979.

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Brown, Alastair. "Crop-yield drivers." Nature Climate Change 4, no. 12 (2014): 1050. http://dx.doi.org/10.1038/nclimate2458.

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Parker, Joyce E., David W. Crowder, Sanford D. Eigenbrode, and William E. Snyder. "Trap crop diversity enhances crop yield." Agriculture, Ecosystems & Environment 232 (September 2016): 254–62. http://dx.doi.org/10.1016/j.agee.2016.08.011.

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Bisht, P. S., R. Puniya, P. C. Pandey, and D. K. Singh. "Grain yield and yield components of rice as influenced by different crop establishment methods." International Rice Research Notes 32, no. 2 (2007): 33–34. https://doi.org/10.5281/zenodo.6955835.

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This article 'Grain yield and yield components of rice as influenced by different crop establishment methods' appeared in the International Rice Research Notes series, created by the International Rice Research Institute (IRRI) to expedite communication among scientists concerned with the development of improved technology for rice and rice-based systems. The series is a mechanism to help scientists keep each other informed of current rice research findings. The concise scientific notes are meant to encourage rice scientists to communicate with one another to obtain details on the research rep
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Nalawade, Viraj, Bhagyashree Kadam, Chetan Jadhav, Gaurav Pabale, and Pradeep Kokane. "Crop Advisor: Intelligent Crop Recommendation System." Indian Journal of Agriculture Engineering 5, no. 1 (2025): 1–6. https://doi.org/10.54105/ijae.a1525.05010525.

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Agriculture has long been a cornerstone of the Indian economy, crucial in sustaining livelihoods and contributing to national growth. By 2024, the sector will contribute approximately 18-20% of India's GDP and employ nearly half of the population. It also ensures food security for over 1.4 billion people. However, crop yields per hectare continue to lag international standards, which has been a significant factor contributing to the rising suicide rates among farmers. This paper proposes a machine learning-based Crop Regulating System to assist farmers. The system takes inputs such as historic
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Viraj, Nalawade. "Crop Advisor: Intelligent Crop Recommendation System." Indian Journal of Agriculture Engineering (IJAE) 5, no. 1 (2025): 1–6. https://doi.org/10.54105/ijae.A1525.05010525.

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<strong>Abstract: </strong>Agriculture has long been a cornerstone of the Indian economy, crucial in sustaining livelihoods and contributing to national growth. By 2024, the sector will contribute approximately 18-20% of India's GDP and employ nearly half of the population. It also ensures food security for over 1.4 billion people. However, crop yields per hectare continue to lag international standards, which has been a significant factor contributing to the rising suicide rates among farmers. This paper proposes a machine learning-based Crop Regulating System to assist farmers. The system ta
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Deng, Xiaohui, Barry J. Barnett, Yingzhuo Yu, Gerrit Hoogenboom, and Axel Garcia y. Garcia. "Alternative Crop Insurance Indexes." Journal of Agricultural and Applied Economics 40, no. 1 (2008): 223–37. http://dx.doi.org/10.1017/s1074070800023567.

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Three index-based crop insurance contracts are evaluated for representative south Georgia corn farms. The insurance contracts considered are based on indexes of historical county yields, yields predicted from a cooling degree-day production model, and yields predicted from a crop-simulation model. For some of the representative farms, the predicted yield index contracts provide yield risk protection comparable to the contract based on historical county yields, especially at lower levels of risk aversion. The impact of constraints on index insurance choice variables is considered and important
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Deng, Xiaohui, Barry J. Barnett, Gerrit Hoogenboom, Yingzhuo Yu, and Axel Garcia y. Garcia. "Alternative Crop Insurance Indexes." Journal of Agricultural and Applied Economics 40, no. 01 (2008): 223–37. http://dx.doi.org/10.1017/s1074070800028078.

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Three index-based crop insurance contracts are evaluated for representative south Georgia corn farms. The insurance contracts considered are based on indexes of historical county yields, yields predicted from a cooling degree-day production model, and yields predicted from a crop-simulation model. For some of the representative farms, the predicted yield index contracts provide yield risk protection comparable to the contract based on historical county yields, especially at lower levels of risk aversion. The impact of constraints on index insurance choice variables is considered and important
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Dissertations / Theses on the topic "Crop yield"

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Zhen, Chen. "Celestial satellite and earthly crop yield: informational content of satellite-based crop yield forecasts." Thesis, Montana State University, 2001. http://etd.lib.montana.edu/etd/2001/zhen/ZhenC2001.pdf.

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Since the late 70s, burgeoning efforts have been allocated to study the potential of monitoring crop conditions and forecasting crop yields via remote sensing from the satellite. An overwhelming majority of these studies shows that remote sensing from the satellite express high predictive power in crop forecasting. In this thesis, using satellite images to forecast wheat yield from 1989 to 2000 in six Montana Crop Reporting Districts (CRD), several statistical improvements were achieved over extant crop forecasting models. First, different weights were allowed for satellite images obtained at
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Husaker, Douglas, and Dale Bucks. "Crop Yield Variability in Irrigated Wheat." College of Agriculture, University of Arizona (Tucson, AZ), 1986. http://hdl.handle.net/10150/200484.

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Optimum design and management of irrigated wheat production is limited by the scarcity of information available on yield variability. The purpose of this study was to evaluate the spatial variability in soil-water parameters and the effects compared to grain yield response under level-basin irrigation. Three levels of seasonal irrigation water and two border lengths were used. Grain yields were found to increase significantly with the amount of water applied and soil water depletion (estimate of crop evapotranspiration), although yield variability was greater with reduced or deficit irrigation
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Ramirez, Almeyda Jacqueline <1985&gt. "Lignocellulosic Crops in Europe: Integrating Crop Yield Potentials with Land Potentials." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amsdottorato.unibo.it/7854/1/Tesi_J.Ramirez_2017_Lignocellulosic%20crops%20potentials%20in%20EU.pdf.

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Given the ambitious EU targets to further decarbonise the economy, it can be expected that the demand for lignocellulosic biomass will continue to grow. Provisioning of part of this biomass by dedicated biomass crops becomes an option. This study presents integrated approach for crop allocation based on land availability and crop requirements. The model analysis to investigate the potential extension of unused land and its suitability for lignocellulosic crops was carried out in 37 European countries at the NUTS3 level. The CAPRI model predicts future land use changes and was used as a basic
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Chouinard, Hayley Helene. "Reduction of yield variance through crop insurance." Thesis, Montana State University, 1994. http://etd.lib.montana.edu/etd/1994/chouinard/ChouinardH1994.pdf.

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The variance of a producer's yield provides uncertainty and may be considered the risk a producer faces. crop insurance may provide protection against yield variability. If yields are necessarily low, an insured producer may receive an indemnity payment. Currently, crop insurance is based on each individual's yield. If the individual's yield falls below a specified level, the individual will receive an indemnity. An alternative crop insurance program bases indemnities on . an area yield. If the yield of the predetermined area falls below a specific level, all insured producers will receive an
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Kreps, Tyler Leigh Hite Diane. "Crop yield response to drought in Alabama." Auburn, Ala, 2009. http://hdl.handle.net/10415/1880.

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Gayam, Narsi Reddy. "Risk in agriculture : a study of crop yield distributions and crop insurance." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/35537.

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Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2006.<br>Includes bibliographical references (leaves 52-53).<br>Agriculture is a business fraught with risk. Crop production depends on climatic, geographical, biological, political, and economic factors, which introduce risks that are quantifiable given the appropriate mathematical and statistical methodologies. Accurate information about the nature of historical crop yields is an important modeling input that helps farmers, agribusinesses, and governmental bodies in managing risk and establish
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Assefa, Yared. "Time series and spatial analysis of crop yield." Thesis, Kansas State University, 2012. http://hdl.handle.net/2097/15142.

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Master of Science<br>Department of Statistics<br>Juan Du<br>Space and time are often vital components of research data sets. Accounting for and utilizing the space and time information in statistical models become beneficial when the response variable in question is proved to have a space and time dependence. This work focuses on the modeling and analysis of crop yield over space and time. Specifically, two different yield data sets were used. The first yield and environmental data set was collected across selected counties in Kansas from yield performance tests conducted for multiple years. T
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Kantanantha, Nantachai. "Crop decision planning under yield and price uncertainties." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/24676.

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Thesis (Ph.D.)--Industrial and Systems Engineering, Georgia Institute of Technology, 2007.<br>Committee Co-Chair: Griffin, Paul; Committee Co-Chair: Serban, Nicoleta; Committee Member: Liang, Steven; Committee Member: Sharp, Gunter; Committee Member: Tsui, Kwok-Leung
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Stephens, David J. "Crop yield forecasting over large areas in Australia." Thesis, Stephens, David J (1995) Crop yield forecasting over large areas in Australia. PhD thesis, Murdoch University, 1995. https://researchrepository.murdoch.edu.au/id/eprint/51647/.

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Inter-annual variations in crop yield are intricately linked to fluctuations in the weather. Accurate yield forecasts prior to harvest are possible if crop-weather relationships are integrated into models that are responsive to the major yield determining factors. A network of meteorological stations was selected across the Australian wheat belt and monthly rainfall regressed with wheat yields from the surrounding shires. Autumn rains that permit an early sowing and finishing rains after July are important for higher yields. As the rainfall distribution becomes more winter dominant in natu
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Al-Shammari, Dhahi Turki Jadah. "Remote sensing applications for crop type mapping and crop yield prediction for digital agriculture." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/29771.

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This thesis addresses important topics in agricultural modelling research. Chapter 1 describes the importance of land productivity and the pressure on the agricultural sector to provide food. In chapter 2, a summer crop type mapping model has been developed to map major cotton fields in-season in the Murray Darling Basin (MDB) in Australia. In chapter 3, a robust crop classification model has been designed to classify two major crops (cereals and canola) in the MDB in Australia. chapter 4 focused on exploring changes in prediction quality with changes in the spatial resolution of predictors an
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Books on the topic "Crop yield"

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Smith, Donald L., and Chantal Hamel, eds. Crop Yield. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-58554-8.

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1953-, Smith Donald L., and Hamel Chantal 1956-, eds. Crop yield: Physiology and processes. Springer, 1999.

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Steduto, P. Crop yield response to water. Food and Agriculture Organization of the United Nations, 2012.

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J, Boote K., American Society of Agronomy, Crop Science Society of America., and Soil Science Society of America., eds. Physiology and determination of crop yield. American Society of Agronomy, 1994.

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Muhammad, Afzal. Narratio botanica: Concerning the yield of crops. Shah Enterprises, 1986.

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1951-, Walker Andrew J., ed. An introduction to the physiology of crop yield. Longman Scientific & Technical, 1989.

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Leblanc, Michel. Agrometeorological crop yield assessment in Somalia. FEWS Project, 1989.

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D, Rimon, ed. Optimal yield management. Avebury, 1988.

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Kirda, C., P. Moutonnet, C. Hera, and D. R. Nielsen, eds. Crop Yield Response to Deficit Irrigation. Springer Netherlands, 1999. http://dx.doi.org/10.1007/978-94-011-4752-1.

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Kahlown, Muhammad Akram. Waterlogging, salinity and crop yield relationships. MONA Reclamation Experimental Project, WAPDA, 1998.

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Book chapters on the topic "Crop yield"

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Hay, R. K. M. "Physiological Control of Growth and Yield in Wheat: Analysis and Synthesis." In Crop Yield. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-58554-8_1.

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Thomas, T. H. "Sugar Beet." In Crop Yield. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-58554-8_10.

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Vos, J. "Potato." In Crop Yield. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-58554-8_11.

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Hall, A. E. "Cowpea." In Crop Yield. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-58554-8_12.

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Zhang, F., and D. L. Smith. "Soybean [Glycine max (L.) Merr.] Physiology and Symbiotic Dinitrogen Fixation." In Crop Yield. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-58554-8_13.

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Caradus, J. R., and M. J. M. Hay. "Physiological Control of Growth and Yield in White Clover." In Crop Yield. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-58554-8_14.

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Volenec, J. J. "Physiological Control of Alfalfa Growth and Yield." In Crop Yield. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-58554-8_15.

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Overman, A. R., and D. M. Wilson. "Physiological Control of Forage Grass Yield and Growth." In Crop Yield. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-58554-8_16.

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Peltonen-Sainio, P. "Growth and Development of Oat with Special Reference to Source-Sink Interaction and Productivity." In Crop Yield. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-58554-8_2.

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Smith, D. L., M. Dijak, P. Bulman, B. L. Ma, and C. Hamel. "Barley: Physiology of Yield." In Crop Yield. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-58554-8_3.

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Conference papers on the topic "Crop yield"

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Karthik, Potnuru, Bolloju Sanjith, Betha Charan Satya Raj, Gujjula Dhanush Reddy, and Bhavani Vasantha. "Crop Yield Prediction." In 2024 4th International Conference on Intelligent Technologies (CONIT). IEEE, 2024. http://dx.doi.org/10.1109/conit61985.2024.10626796.

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Singh, Nitin, Shivansh Kandhoua, and Payal Thakur. "AI-Driven Crop Yield Prediction." In 2024 Second International Conference on Advanced Computing & Communication Technologies (ICACCTech). IEEE, 2024. https://doi.org/10.1109/icacctech65084.2024.00096.

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Bagane, Pooja, Obsa Amenu, Lahitanshu Das, Omkar Potdukhe, Manraj Singh Gandhi, and Sonali Kothari. "Crop Yield Recommendation Using Machine Learning." In 2024 19th International Workshop on Semantic and Social Media Adaptation & Personalization (SMAP). IEEE, 2024. https://doi.org/10.1109/smap63474.2024.00031.

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S, Iniyan, Pidikiti Keerthi, and Shruti Pawar. "Corn Crop Yield Prediction using Deep Learning." In 2024 3rd International Conference on Automation, Computing and Renewable Systems (ICACRS). IEEE, 2024. https://doi.org/10.1109/icacrs62842.2024.10841731.

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P, Abinaya, Deepika J, Fathimunisa D, and Deepa R. "Crop Yield Prediction Using Machine Learning Algorithms." In 2025 2nd International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE). IEEE, 2025. https://doi.org/10.1109/rmkmate64874.2025.11042701.

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Shanmugasundaram, C., C. Umamaheswari, A. Vijayalakshmi, and Prabha Elizabeth Varghese. "Crop for Est - Crop Forecasting and Estimation. Crop Yield Estimation and Profitability Analysis for Precision Agriculture." In 2024 International Conference on System, Computation, Automation and Networking (ICSCAN). IEEE, 2024. https://doi.org/10.1109/icscan62807.2024.10893947.

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Sathvik, Jammula Durga Bala, Monish Mohanty, N. Sushma, Panchami Raghav, Amudha J, and Maria John. "Spectral – Vegetative Indices fusion for Crop Yield Analysis." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10723980.

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Nossam, Sri Chakradhar, Rishi Anirudh Katakam, Gopa Pulastya, and Manju Venugopalan. "Enhanced Crop Yield Prediction using Machine Learning Techniques." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10724901.

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Gupta, Rajat, Tushar Shrikant Padmawar, Daksh Kumar, Deepak Ray, and Payal Kadam. "Significance of Machine Learning in Crop Yield Prediction." In 2024 2nd World Conference on Communication & Computing (WCONF). IEEE, 2024. http://dx.doi.org/10.1109/wconf61366.2024.10692141.

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Prem Kumar, S., Shaik Sahifa, B. N. Saadhana, M. Sai Sahithi, and D. Pranathi Ketura. "Crop Selection and Yield Prediction using Intelligent Algorithms." In 2024 International Conference on Expert Clouds and Applications (ICOECA). IEEE, 2024. http://dx.doi.org/10.1109/icoeca62351.2024.00081.

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Reports on the topic "Crop yield"

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Ndoye, Aïssatou, Khadim Dia, and Racine Ly. AAgWa Crop Production Forecasts Brief Series - Issue N.06. AKADEMIYA2063, 2023. http://dx.doi.org/10.54067/acpf.06.

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The Africa Agriculture Watch (AAgWa) Crop Production Forecasts by AKADEMIYA2063 aim to provide more accurate and timely statistics about harvest and yield levels for nine crops across 47 African countries. Developed at AKADEMIYA2063, the Africa Crop Production (AfCP) model is an artificial intelligence (AI) based forecasting model applied to remotely sensed bio-geophysical data to produce estimates of expected crop yields and harvests at the beginning of every growing season.
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Ndoye, Aïssatou, Khadim Dia, and Racine Ly. AAgWa Crop Production Forecasts Brief Series - Issue N.01. AKADEMIYA2063, 2022. http://dx.doi.org/10.54067/acpf.01.

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The Africa Agriculture Watch (AAgWa) Crop Production Forecasts by AKADEMIYA2063 aim to provide more accurate and timely statistics about harvest and yield levels for nine key crops across nearly 50 African countries. Developed at AKADEMIYA2063, the Africa Crop Production (AfCP) model is an artificial intelligence (AI) based forecasting model applied to remotely sensed geo-biophysical data to produce estimates of expected crop yields and harvests at the beginning of every growing season.
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Raitzer, David, and Joeffrey Drouard. Empirically Estimated Impacts of Climate Change on Global Crop Production via Increasing Precipitation–Evapotranspiration Extremes. Asian Development Bank, 2024. https://doi.org/10.22617/wps240589-2.

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To assess climate change effects on crop yields, remote sensing-derived yield and agrometeorological reanalysis data are used to construct a panel at 0.1-degree resolution for 2003–2015. Regressions controlling for grid cell-specific intercepts and time trends, temperature, rainfall, and cloudiness estimate the subregional relationships between yields and precipitation-evapotranspiration extremes for rice, wheat, and maize. Results imply that climate change will cause global yield reductions for all crops, with losses highest for wheat and maize, especially in South Asia and Southern Africa.
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Wright, Lynn L. US Woody Crop Yield Potential Database Documentation with Referenced Yield Summary Tables. Office of Scientific and Technical Information (OSTI), 2014. http://dx.doi.org/10.2172/1111447.

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Helmers, Matt, Xiaobo Zhou, Carl Pederson, and Greg Brenneman. Impact of Drainage Water Management on Crop Yield. Iowa State University, Digital Repository, 2013. http://dx.doi.org/10.31274/farmprogressreports-180814-1902.

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Al-Kaisi, Mahdi. Long-term Tillage and Crop Rotation Effects on Yield. Iowa State University, Digital Repository, 2012. http://dx.doi.org/10.31274/farmprogressreports-180814-1157.

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Ndoye, Aissatou, Khadim Dia, and Racine Ly. The AAgWa Crop Production Forecasts Brief Series - Issue N.02. AKADEMIYA2063, 2023. http://dx.doi.org/10.54067/acpf.02.

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The Africa Agriculture Watch (AAgWa) Crop Production Brief 2, produced by AKADEMIYA2063, aims to provide more accurate and timely statistics on millet production in Gambia using the Africa Food Crop Production (AfCP) model. The AfCP developed at AKADEMIYA2063 is an artificial intelligence (AI) based forecasting model used to produce yield and harvest forecasts at the beginning of each growing season for nine crops in 47 African countries.
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Frenkel, Haim, John Hanks, and A. Mantell. Crop Yield and Water Use under Irrigation with Saline Water. United States Department of Agriculture, 1987. http://dx.doi.org/10.32747/1987.7695596.bard.

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Bentley, Jennifer A., and Brian J. Lang. 2010 Iowa Corn Silage Yield Trial and Rye Cover Crop Demonstration. Iowa State University, 2011. http://dx.doi.org/10.31274/ans_air-180814-154.

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Thoreson, Dale, and Brian Lang. 2009 Iowa Corn Silage Yield Trial and Rye Cover Crop Demonstration. Iowa State University, 2010. http://dx.doi.org/10.31274/ans_air-180814-967.

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