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: 11 March 2023

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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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Angus, J. F., J. A. Kirkegaard, J. R. Hunt, M. H. Ryan, L. Ohlander, and M. B. Peoples. "Break crops and rotations for wheat." Crop and Pasture Science 66, no. 6 (2015): 523. http://dx.doi.org/10.1071/cp14252.

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Wheat crops usually yield more when grown after another species than when grown after wheat. Quantifying the yield increase and explaining the factors that affect the increase will assist farmers to decide on crop sequences. This review quantifies the yield increase, based on >900 comparisons of wheat growing after a break crop with wheat after wheat. The mean increase in wheat yield varied with species of break crop, ranging from 0.5 t ha–1 after oats to 1.2 t ha–1 after grain legumes. Based on overlapping experiments, the observed ranking of break-crop species in terms of mean yield response of the following wheat crop was: oats < canola ≈ mustard ≈ flax < field peas ≈ faba beans ≈ chickpeas ≈ lentils ≈ lupins. The mean additional wheat yield after oats or oilseed break crops was independent of the yield level of the following wheat crop. The wheat yield response to legume break crops was not clearly independent of yield level and was relatively greater at high yields. The yield of wheat after two successive break crops was 0.1–0.3 t ha–1 greater than after a single break crop. The additional yield of a second wheat crop after a single break crop ranged from 20% of the effect on a first wheat crop after canola, to 60% after legumes. The mean yield effect on a third wheat crop was negligible, except in persistently dry conditions. The variability of the break-crop effect on the yield of a second wheat crop was larger than of a first wheat crop, particularly following canola. We discuss the responses in relation to mechanisms by which break crops affect soil and following crops. By quantifying the magnitude and persistence of break-crop effects, we aim to provide a basis for the decision to grow continuous cereal crops, strategic rotations or tactically selected break crops. In many wheat-growing areas, the large potential yield increases due to break crops are not fully exploited. Research into quantifying the net benefits of break crops, determining the situations where the benefits are greatest, and improving the benefits of break crops promises to improve the efficiency of wheat-based cropping systems.
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Evans, J., G. Scott, D. Lemerle, A. Kaiser, B. Orchard, G. M. Murray, and E. L. Armstrong. "Impact of legume 'break' crops on the yield and grain quality of wheat and relationship with soil mineral N and crop N content." Australian Journal of Agricultural Research 54, no. 8 (2003): 777. http://dx.doi.org/10.1071/ar02224.

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The effect of annual 'break' crops on the yield and protein content of wheat was investigated over 3 seasons on a Red Kandasol on the south-western slopes of New South Wales. The 'break' crops included lupin and pea grown for grain, pea and vetch managed for silage, clovers managed for silage or hay, and vetch and clovers managed for green manuring. Wheat was sown for 2 years following the legume year, or canola and wheat followed the legumes. Averaged over 3 experiments the yields of first crop wheat following pea or vetch silage crops were comparable with those after grain pea. Yields following clover forage conservation crops or green manures exceeded those after grain pea by at least 0.41 t/ha; average yield increase after clover green manure was 0.93 t/ha. In one experiment, yields of second crop wheat were greater, by up to 0.37 t/ha, after forage conservation or green manure legume 'breaks' than after grain legumes. In 2 experiments, second crop wheat yields were greater after a first crop of canola than a first crop of wheat. Compared with continuous wheat yield, aggregate mean wheat yield increases were 3.5–4 t/ha following grain legumes, pea, and vetch silage crops, but 5.3–6.3 t/ha following clover forage conservation and green manure crops. However, the relative effects of legume treatments on wheat yield were significantly seasonally dependent. Yield and grain protein variation in wheat after legumes was significantly correlated with variation in mineral N at wheat establishment. However, in one experiment, yield was correlated only with variation in mineral N below the 20-cm soil depth, whereas protein was correlated only with variation in mineral N above the 20 cm soil depth. Yield increases in first crop wheat did not occur at the expense of grain protein.
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Li, Zhonghe, Chesheng Zhan, Shi Hu, Like Ning, Lanfang Wu, and Hai Guo. "Evaluation of global gridded crop models (GGCMs) for the simulation of major grain crop yields in China." Hydrology Research 53, no. 3 (March 1, 2022): 353–69. http://dx.doi.org/10.2166/nh.2022.087.

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Abstract Multimodel ensembles are powerful tools for evaluating agricultural production. Multimodel simulation results provided by the Global Gridded Crop Model Intercomparison (GGCMI) facilitate the evaluation of the grain production situation in China. With census crop yield data, the performance of nine global gridded crop models (GGCMs) in China was evaluated, and the yield gaps of four crops (maize, rice, soybean, and wheat) were estimated. The results showed that GGCMs better simulated maize yields than those of other crops in the northeast, north, northwest, east, and center. GEPIC (CLM-CROP) performed best in simulating maize (wheat) yield in the north, east, and northwest (southwest and south), due to reasonable parameter (cultivar and phenology parameters) settings. Because the rice phenology parameters were calibrated against phenological observation networks and a simple nitrogen limitation index was introduced, ORCHIDEE-CROP performed well in rice yield simulation and soybean yield simulation (center and southwest). Among four crops, wheat has the largest yield gap (7.3–14.1%), in which the poor soil of northwest (14.1%) exposes wheat to relatively high nutritional stress. Thus, in northwest China, optimizing nitrogen management in wheat production can effectively mitigate the negative impact of climate change on crop production.
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Li, Wei, Philippe Ciais, Elke Stehfest, Detlef van Vuuren, Alexander Popp, Almut Arneth, Fulvio Di Fulvio, et al. "Mapping the yields of lignocellulosic bioenergy crops from observations at the global scale." Earth System Science Data 12, no. 2 (April 2, 2020): 789–804. http://dx.doi.org/10.5194/essd-12-789-2020.

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Abstract. Most scenarios from integrated assessment models (IAMs) that project greenhouse gas emissions include the use of bioenergy as a means to reduce CO2 emissions or even to achieve negative emissions (together with CCS – carbon capture and storage). The potential amount of CO2 that can be removed from the atmosphere depends, among others, on the yields of bioenergy crops, the land available to grow these crops and the efficiency with which CO2 produced by combustion is captured. While bioenergy crop yields can be simulated by models, estimates of the spatial distribution of bioenergy yields under current technology based on a large number of observations are currently lacking. In this study, a random-forest (RF) algorithm is used to upscale a bioenergy yield dataset of 3963 observations covering Miscanthus, switchgrass, eucalypt, poplar and willow using climatic and soil conditions as explanatory variables. The results are global yield maps of five important lignocellulosic bioenergy crops under current technology, climate and atmospheric CO2 conditions at a 0.5∘×0.5∘ spatial resolution. We also provide a combined “best bioenergy crop” yield map by selecting one of the five crop types with the highest yield in each of the grid cells, eucalypt and Miscanthus in most cases. The global median yield of the best crop is 16.3 t DM ha−1 yr−1 (DM – dry matter). High yields mainly occur in the Amazon region and southeastern Asia. We further compare our empirically derived maps with yield maps used in three IAMs and find that the median yields in our maps are > 50 % higher than those in the IAM maps. Our estimates of gridded bioenergy crop yields can be used to provide bioenergy yields for IAMs, to evaluate land surface models or to identify the most suitable lands for future bioenergy crop plantations. The 0.5∘×0.5∘ global maps for yields of different bioenergy crops and the best crop and for the best crop composition generated from this study can be download from https://doi.org/10.5281/zenodo.3274254 (Li, 2019).
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Zscheischler, Jakob, Rene Orth, and Sonia I. Seneviratne. "Bivariate return periods of temperature and precipitation explain a large fraction of European crop yields." Biogeosciences 14, no. 13 (July 11, 2017): 3309–20. http://dx.doi.org/10.5194/bg-14-3309-2017.

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Abstract. Crops are vital for human society. Crop yields vary with climate and it is important to understand how climate and crop yields are linked to ensure future food security. Temperature and precipitation are among the key driving factors of crop yield variability. Previous studies have investigated mostly linear relationships between temperature and precipitation and crop yield variability. Other research has highlighted the adverse impacts of climate extremes, such as drought and heat waves, on crop yields. Impacts are, however, often non-linearly related to multivariate climate conditions. Here we derive bivariate return periods of climate conditions as indicators for climate variability along different temperature–precipitation gradients. We show that in Europe, linear models based on bivariate return periods of specific climate conditions explain on average significantly more crop yield variability (42 %) than models relying directly on temperature and precipitation as predictors (36 %). Our results demonstrate that most often crop yields increase along a gradient from hot and dry to cold and wet conditions, with lower yields associated with hot and dry periods. The majority of crops are most sensitive to climate conditions in summer and to maximum temperatures. The use of bivariate return periods allows the integration of non-linear impacts into climate–crop yield analysis. This offers new avenues to study the link between climate and crop yield variability and suggests that they are possibly more strongly related than what is inferred from conventional linear models.
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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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Tsolmon, Nyamdavaa, and Friedel K. Jürgen. "Tuber yield parameters in organic potato production with green manures as preceding crop, catch crop and with farmyard manure." Mongolian Journal of Agricultural Sciences 17, no. 1 (January 3, 2017): 21–26. http://dx.doi.org/10.5564/mjas.v17i1.722.

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The effect of different preceding crops, catch crops and manure application on the agronomic performance of potato was studied in two consequential years in an organic farming system. Within the study the effect of three different preceding crops: viz. lucerne, field pea and spring barley; incorporated catch crops as green manure: non-legume or mixture; and farmyard manure (30 tones ha-1) are tested on subsequent potato yield and tuber size distribution. The catch crop treatments were studied in comparison to control bare fallow. The subsequent crop response to preceding crops was negligible since there was no indication of a greater tuber yields (fresh tuber, marketable and dry matter) after legume pre-crops compared to barley. Catch crops and manure effects both slightly increased tuber dry matter yield from 4.9 tones ha-1 to 5.2 tones ha-1 in 2010 only, on the contrary dry matter yield was not affected by catch crop and manure in 2011. The significant interaction effect was found between year and catch crop for fresh and dry matter tuber yield and non-standard small sized tubers. Catch crops had a positive effect on potato yield only in 2010 when mineral nitrogen availability was low. The catch crops significantly (P < 0.01) increased the percentage of large sized tubers (> 65 mm in diameter); however catch crops even negatively affected potato medium sized tuber yield and quality. Significant (P < 0.01) interaction effect was found between year and catch crop for small sized tubers, also.
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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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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.
Includes bibliographical references (leaves 52-53).
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.
(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.
by Narsi Reddy Gayam.
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
Department of Statistics
Juan Du
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.
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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J, Petr, ed. Weather and yield. Amsterdam: Elsevier, 1991.

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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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Crop evolution, adaptation, and yield. Cambridge: Cambridge University Press, 1993.

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

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V, Černý, and Hruška L, eds. Yield formation in the main field crops. Amsterdam: Elsevier, 1988.

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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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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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Shelestov, Andrii, Leonid Shumilo, Hanna Yailymova, and Sophia Drozd. "Crop Yield Forecasting for Major Crops in Ukraine." In 2021 IEEE International Conference on Information and Telecommunication Technologies and Radio Electronics (UkrMiCo). IEEE, 2021. http://dx.doi.org/10.1109/ukrmico52950.2021.9716672.

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Borin, A. A., A. E. Loshchinina, V. V. Evseev, and A. V. Kazidubov. "TILLAGE AND HERBICIDES, THEIR INFLUENCE ON THE WEED COMPONENT OF AGROPHYTOCENOSIS AND CROP YIELD." In Agrobiotechnology-2021. Publishing house of RGAU - MSHA, 2021. http://dx.doi.org/10.26897/978-5-9675-1855-3-2021-3.

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In stationary field crop rotation, treatment systems of different intensity of impact on the soil in combination with the use of herbicides were studied. An increase in weediness of crops by flat-cutting and shallow tillage was revealed in comparison with moldboard. The use of herbicides made it possible to reduce the weediness of crops, which contributed to an increase in the yield of crops in crop rotation.
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Belenkov, A. I., S. V. Zhelezova, and D. V. Bereza. "Yield of crops of grain crop rotation depending on basic tillage." In Растениеводство и луговодство. Тимирязевская сельскохозяйственная академия, 2020. http://dx.doi.org/10.26897/978-5-9675-1762-4-2020-171.

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The article considers the issues of the influence of the main tillage on the yield of crops of grain-tilled crop rotation: vetch-oat mixture - winter wheat - potatoes - barley. It was found that in addition to the reception of soil cultivation, the productivity of agrocenoses was also influenced by meteorological conditions.
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Zhisheng Qing and Dale E. Linvill. "Integrating Crop Models into GIS to Predict Regional Crop Yield." In 2002 Chicago, IL July 28-31, 2002. St. Joseph, MI: American Society of Agricultural and Biological Engineers, 2002. http://dx.doi.org/10.13031/2013.9698.

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Taylor, S. Elwynn, and Richard E. Carlson. "Weather and Yield Trends." In Proceedings of the 1995 Integrated Crop Management Conference. Iowa State University, Digital Press, 1996. http://dx.doi.org/10.31274/icm-180809-546.

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Gandhi, Niketa, Leisa J. Armstrong, and Owaiz Petkar. "PredictingRice crop yield using Bayesian networks." In 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2016. http://dx.doi.org/10.1109/icacci.2016.7732143.

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Erden, Hakan, and Gozde Toreyen. "Parcel Based Crop Production Yield Model." In 2015 Fourth International Conference on Agro-Geoinformatics. IEEE, 2015. http://dx.doi.org/10.1109/agro-geoinformatics.2015.7248147.

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Rananavare, Laxmi B., and Sanjay Chitnis. "Crop Yield Prediction Using Temporal Data." In 2022 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT). IEEE, 2022. http://dx.doi.org/10.1109/conecct55679.2022.9865791.

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Raskar, Sagar, Aditya Abhang, Nachiket Pethe, and Vahida Attar. "Crop Yield Prediction and Recommendation System." In 2022 IEEE Pune Section International Conference (PuneCon). IEEE, 2022. http://dx.doi.org/10.1109/punecon55413.2022.10014957.

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Bodapati, Nagaeswari, Jidugu Himavaishnavi, Velineni Rohitha, D. Lakshmi Jagadeeswari, and Priya Bhavana. "Analyzing Crop Yield Using Machine Learning." In 2022 International Conference on Electronics and Renewable Systems (ICEARS). IEEE, 2022. http://dx.doi.org/10.1109/icears53579.2022.9752242.

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Reports on the topic "Crop yield"

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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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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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Harman, Gary E., and Ilan Chet. Enhancing Crop Yield through Colonization of the Rhizosphere with Beneficial Microbes. United States Department of Agriculture, December 2001. http://dx.doi.org/10.32747/2001.7580684.bard.

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At the start of this project, fungi in the genus Trichoderma were known to be potent biocontrol agents, and their primary mechanism was considered to via direct effects upon the target fungi. Due in large part to the efforts of the two PIs, we now know that this view is far too limited; while Trichoderma spp. do indeed have direct effects on pathogenic fungi, they have very far reaching effects directly upon plants. Indeed, these fungi must be considered as opportunistic plant symbionts; they provide a number of benefits to plants and themselves are favored by large numbers of healthy roots. Research under this BARD grant has demonstrated that These fungi induce resistance mechanisms in plants. They increase root development and depth of rooting; Bradyrhizobium enhances this effect in soybean. They enhance uptake of plant nutrients. They have abilities to solubilize nutrients, such as oxidized metals and insoluble phosphorus compounds by a variety of different mechanisms and biochemicals. This is a marked expansion of our knowledge of the abilities of these organisms. This knowledge has direct implications for understanding of basic plant responses and abilities, and already is being used to improve plant productivity and reduce pollution of the environment.
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Al-Kaisi, Mahdi. Long-Term Tillage and Crop Rotation Effect on Yield and Soil Carbon. Ames: Iowa State University, Digital Repository, 2009. http://dx.doi.org/10.31274/farmprogressreports-180814-1117.

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Al-Kaisi, Mahdi. Long-term Tillage and Crop Rotation Effect on Yield and Soil Carbon. Ames: Iowa State University, Digital Repository, 2009. http://dx.doi.org/10.31274/farmprogressreports-180814-1206.

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Al-Kaisi, Mahdi. Long-term Tillage and Crop Rotation Effect on Yield and Soil Carbon. Ames: Iowa State University, Digital Repository, 2010. http://dx.doi.org/10.31274/farmprogressreports-180814-1225.

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