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: 9 June 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 (March 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 grain sorghum (C. V. 9.5 percent) yields. Corn was also the most sensitive crop to previous crop effects. The range of treatment yields for each crop was 47 percent, 22 percent, and 11 percent of the overall means for corn, soybean, and sorghum, respectively. Previous crop affected yields for all crops, but the effects were not consistent across years. All crops produced highest yield following fallow. Yields of corn, soybean, and grain sorghum following fallow were 74, 25, and 10 percent higher than their respective monoculture yields. In years of average precipitation, a corn-soybean sequence produced the greatest yield. In years having above- or below-normal precipitation, a grain sorghum-soybean sequence produced the highest yield.
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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 (October 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 (January 3, 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 (November 26, 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 (December 1, 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 reported.
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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 (May 30, 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 historical and current yield data, weather conditions, soil quality and fertiliser usage from farmers and predicts weather impact, rainfall, and disease effect to predict crop yield before sowing. Also, the system takes inputs such as current market data, sowed land, market import/export data, historical retail data, and consumer data for market demand analysis. Machine learning algorithms analyze this data to predict the market demand and the yield for a chosen crop. After that machine learning algorithms like Regression Forest (RF) and Support Vector Machine (SVM) were used to provide Decision support to Farmers. Regression models like Support Vector Machines (SVM) and Random Forests (RF), Multiple Linear Regression (MLR) and classification models like K-Nearest Neighbors (KNN) are utilized for Crop Yield Prediction. Time series models such as Auto Regressive Integrated Moving Average (ARIMA), and Genetics Algorithms (GAs) are used for Market Demand Analysis.
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Viraj, Nalawade. "Crop Advisor: Intelligent Crop Recommendation System." Indian Journal of Agriculture Engineering (IJAE) 5, no. 1 (May 30, 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 takes inputs such as historical and current yield data, weather conditions, soil quality and fertiliser usage from farmers and predicts weather impact, rainfall, and disease effect to predict crop yield before sowing. Also, the system takes inputs such as current market data, sowed land, market import/export data, historical retail data, and consumer data for market demand analysis. Machine learning algorithms analyze this data to predict the market demand and the yield for a chosen crop. After that machine learning algorithms like Regression Forest (RF) and Support Vector Machine (SVM) were used to provide Decision support to Farmers. Regression models like Support Vector Machines (SVM) and Random Forests (RF), Multiple Linear Regression (MLR) and classification models like K-Nearest Neighbors (KNN) are utilized for Crop Yield Prediction. Time series models such as AutoRegressive Integrated Moving Average (ARIMA), and Genetics Algorithms (GAs) are used for Market Demand Analysis.
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Husain, Dr Mohammad, and Dr Rafi Ahmad Khan. "Date Palm Crop Yield Estimation – A Framework." International Journal of Innovative Research in Computer Science & Technology 7, no. 6 (November 2019): 143–46. http://dx.doi.org/10.21276/ijircst.2019.7.6.1.

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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 (April 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 interactions among constrained, conditionally optimized, choice variables are analyzed.
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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 different points of time, accounting for the likely heterogeneous contributions of various crop phenological stages to the final crop yield. Second, crop acreage information was directly modeled. This, to some extent, alleviates the low-resolution problem of existing satellite imagery. Third, jackknife out-of-sample forecasts were generated to formally measure the well-known instability problem of using satellite imagery in crop forecasting across seasons. In addition, the satellite-based crop yield forecasts were compared with those of the U.S. Department of Agriculture (USDA), whose forecasts were based on traditional methods. It is shown that although meaningful crop forecasts can be generated from the satellite imagery late season, the additional yield information that can be extracted from the satellite tends to be limited. Because in the major wheat producing CRDs, the USDA forecasts are already very accurate and little independent information is observed in the satellite-based forecasts. Results suggest the needs to pinpoint crop phenological stages and to calibrate region-specific crop forecasting model.
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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 irrigations. Variations in soil water content were responsible for about 22% of the variability in grain yield, indicating that other soil and crop- related factors had a significant influence on production. Spatial dependence was exhibited over a greater distance at the wetter compared with the drier irrigation regimes.
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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 input to assess the agricultural biomass potentials in Europe. It was then identified the total land resource with a post-modeling assessment for three different potentials to the year 2020 and 2030, according to sustainability criteria formulated in the Renewable Energy directive (RED). Furthermore, crop-specific suitability maps were generated for each crop based on the variability of biophysical factors such as climate, soil properties and topographical aspects. The yields and cost levels that can be reached in Europe with different perennial crops in different climatic, soil and management situations. The AquaCrop model developed by FAO was used and fed with phenological parameters per crop and detailed weather data to simulate the crop growth in all European Nuts 3 regions. Yield levels were simulated for a maximum and a water-limited yield situation and further converted to match with low, medium and high input management systems. The costs production was assessed with an Activity Based Costing (ABC) model, developed to assess the roadside Net Present Value (NPV) cost of biomass. The yield, crop suitability and cost simulation results were then combined to identify the best performing crop-management mix per region.
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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 indemnity. This thesis examines the yield variability reduction received by purchasing various forms of area yield and individual yield crop insurance and the actuarially fair premium costs associated with them. When a producer purchases insurance two decisions are made. First, the producer selects a trigger level which determines the critical yield which generates an indemnity payment. Second, the producer may be able to select a coverage level which is the amount of acreage covered by the contract. Each contract examined allows different levels for the trigger and coverage levels. The variance reduction provided from each contract is the variance of the yield without insurance less the variance of the yield with an insurance contract. The results indicate most producers receive some variance reduction from the area yield contracts. And, producers who have yields which are closely correlated with the area yield receive more variance reduction from the area yield insurance than from the individual yield insurance contracts. However, the area yield contracts which provide on average more yield variance reduction than the individual yield contracts, also have much higher actuarially fair premium costs. The area yield insurance contracts should be considered as an alternative to individual yield insurance, but the premium costs must be evaluated also.
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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 establishing the proper policies for such things as crop insurance. Explicitly or implicitly, nearly all farm decisions relate in some way to the expectation of crop yield. Historically, crop yields are assumed to be normally distributed for a statistical population and for a sample within a crop year. This thesis examines the assumption of normality of crop yields using data collected from India involving sugarcane and soybeans. The null hypothesis (crop yields are normally distributed) was tested using the Lilliefors method combined with intensive qualitative analysis of the data. Results show that in all cases considered in this thesis, crop yields are not normally distributed.<br>(cont.) This result has important implications for managing risk involving sugarcane and soybeans grown in India. The last section of this thesis examines the impact of crop yield non normality on various insurance programs, which typically assume that all crop yields are normally distributed and that the probability of crop failure can be calculated given available data.<br>by Narsi Reddy Gayam.<br>M.Eng.in Logistics
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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. The second yield data set was a survey data set collected by USDA across the US from 1900-2009. The objectives of our study were to investigate crop yield trends in space and time, quantify the variability in yield explained by genetics and space-time (environment) factors, and study how spatio-temporal information could be incorporated and also utilized in modeling and forecasting yield. Based on the format of these data sets, trend of irrigated and dryland crops was analyzed by employing time series statistical techniques. Some traditional linear regressions and smoothing techniques are first used to obtain the yield function. These models were then improved by incorporating time and space information either as explanatory variables or as auto- or cross- correlations adjusted in the residual covariance structures. In addition, a multivariate time series modeling approach was conducted to demonstrate how the space and time correlation information can be utilized to model and forecast yield and related variables. The conclusion from this research clearly emphasizes the importance of space and time components of data sets in research analysis. That is partly because they can often adjust (make up) for those underlying variables and factor effects that are not measured or not well understood.
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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 nature, both crop yield variability and the usefulness of early winter rainfall decreases. Waterlogging has a large negative effect in the south-west of Western Australia, such that the rainfall distribution is more important than the amount in this region. A national sowing date survey determined that regional sowing dates have become earlier during the 1980’s and that these vary considerably, especially to the north-east. In Western Australia, earlier sowing combined with higher nitrogen inputs from fertilizers and legumes caused a significant upward trend in recent yields. Trends have been smaller in other states. Yields were also regressed with broad scale atmospheric indicators. Up to a year in advance of harvest, changes in the amplitude of the trough in the upper level westerlies (South Pacific) precede major anomalies in yields. Trends in the Southern Oscillation Index (SOI) around sowing time account for half the variance in the national yield, due to a persistence in following rainfall anomalies. Agrometeorological index models that combine the features of simulation and regression are shown to be the most appropriate models for yield forecasting. At a shire level they account for an average 55% of the yield variance in Western Australia, but 60 to 80% of the variation in eastern states yields. Satellite spectral data also resolved similar amounts of yield variance when sensor calibration bias was removed. With a mean regional index determined by station weighting, crop-weather models account for 87 to 92% of the variance of state and national yields. Tests with operational model forecasts equalled, or were more accurate than, official forecasts in 4 out of 5 years. Seasonal outlooks incorporated into model calculations brought further gains in accuracy in extreme years. Overall, the broad scale extent of yield anomalies across the Australian wheat belt is highlighted. Extreme yields, which are of most interest to the grain industry, are inseparably coupled to the ENSO phenomenon and the broad scale atmospheric circulation. Crop-weather models adjust rapidly to these anomalies in the weather and should be applied in an operational environment to provide early indications of crop prospects.
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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 and the predictions. More specifically, this study investigated whether inputs should be resampled prior to modelling, or the modelling implemented first with the aggregation of predictions happening as a final step. In chapter 5, a new vegetation index is proposed that exploits the three red-edge bands provided by the Sentinel-2 satellite to capture changes in the transition region between the photosynthetically affected region (red region) and the Near-Infrared region (NIR region) affected by cell structure and leaf layers. Chapter 6 was conducted to test the potential of integration of two mechanistic-type model products (biomass and soil moisture) in the DDMs models. Chapter 7 was dedicated to discussing each technique used in this thesis and the outcomes of each technique, and the relationships between these outcomes. This thesis addressed the topics and questioned asked at the beginning of this research and the outcomes are listed in each chapter.
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Books on the topic "Crop yield"

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Smith, Donald L., and Chantal Hamel, eds. Crop Yield. Berlin, Heidelberg: 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. Berlin: Springer, 1999.

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Steduto, P. Crop yield response to water. Rome: 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. Madison, Wis: American Society of Agronomy, 1994.

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

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

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Leblanc, Michel. Agrometeorological crop yield assessment in Somalia. [Mogadishu, Somali Democratic Republic]: FEWS Project, 1989.

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Kirda, C., P. Moutonnet, C. Hera, and D. R. Nielsen, eds. Crop Yield Response to Deficit Irrigation. Dordrecht: 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. [Lahore]: MONA Reclamation Experimental Project, WAPDA, 1998.

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D, Rimon, ed. Optimal yield management. Aldershot, England: Avebury, 1988.

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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, 1–38. Berlin, Heidelberg: 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, 311–31. Berlin, Heidelberg: 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, 333–54. Berlin, Heidelberg: 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, 355–73. Berlin, Heidelberg: 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, 375–99. Berlin, Heidelberg: 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, 401–23. Berlin, Heidelberg: 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, 425–42. Berlin, Heidelberg: 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, 443–73. Berlin, Heidelberg: 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, 39–66. Berlin, Heidelberg: 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, 67–107. Berlin, Heidelberg: 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), 1–5. 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), 564–71. 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), 122–26. 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), 1407–13. IEEE, 2024. https://doi.org/10.1109/icacrs62842.2024.10841731.

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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), 1–7. 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), 1–7. 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), 1–6. 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), 1–7. 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), 420–25. IEEE, 2024. http://dx.doi.org/10.1109/icoeca62351.2024.00081.

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Vaishnavi, D., R. Bavithra, M. Rufina Marssha, and S. Sowmiya. "Agriculture Crop Yield Forecasting using Deep Learning Techniques." In 2024 5th International Conference on Image Processing and Capsule Networks (ICIPCN), 534–38. IEEE, 2024. http://dx.doi.org/10.1109/icipcn63822.2024.00093.

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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, February 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, December 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, December 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), January 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. Ames: 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. Ames: 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, February 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, July 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. Ames (Iowa): Iowa State University, January 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. Ames (Iowa): Iowa State University, January 2010. http://dx.doi.org/10.31274/ans_air-180814-967.

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