Relevant bibliographies by topics / Yield of agricultural crops

Contents

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

Academic literature on the topic 'Yield of agricultural crops'

Author: Grafiati
Published: 28 May 2022
Last updated: 31 July 2025

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Journal articles on the topic "Yield of agricultural crops"

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Dusaev, Kh B., and A. H. Dusaeva. "Analysis of production of main crops in Orenburg region." Normirovanie i oplata truda v sel'skom hozyajstve (Rationing and remuneration of labor in agriculture), no. 7 (July 1, 2021): 12–31. http://dx.doi.org/10.33920/sel-06-2107-02.

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The article analyzes the regional production of major agricultural crops for 2000–2018, taking into account the areas of their crops and planting, gross harvest and yield in farms of all categories and agricultural organizations. During the study period, a gradual reduction in the acreage of grain and leguminous crops in the region was revealed, with stabilization at the level of 2.7 million hectares, with a wide range of variations in gross yields and yields depending on the weather and climatic conditions of cultivation. There was a reduction in the area, a decrease in gross collections and
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S, Arunkumar, Harish B, Divakar S, and UmaDevi G. "Profitability and Yield Prediction on Agricultural Crops of India." International Journal of Research Publication and Reviews 4, no. 4 (2023): 4506–14. http://dx.doi.org/10.55248/gengpi.234.4.38180.

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Ivano, Yaroslav, and Sof'ya Petrova. "MODELING MULTILEVEL DYNAMICS OF AGRICULTURAL CROPS YIELD." Bulletin of KSAU, no. 12 (January 29, 2025): 66–77. https://doi.org/10.36718/1819-4036-2024-12-66-77.

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The objective of the study is to develop an algorithm for modeling crop yields based on the dynamic-stochastic and cyclical properties of long-term characteristic series. Tasks: identifying the properties of time series of agricultural crop bioproductivity based on their consideration as multi-level structures with cyclical fluctuations; using the properties of variability of long-term characteristic series to build forecasting or stochastic assessment models; implementing the modeling algorithm using the example of grain and leguminous crop yields in Russia and wheat bioproductivity in the US
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Buklagin, D. S. "Agricultural crop yield forecasting methods." Machinery and Equipment for Rural Area, no. 12 (December 20, 2020): 25–28. http://dx.doi.org/10.33267/2072-9642-2020-12-25-28.

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The main areas of the development and use of digital technologies and systems for forecasting the yield of agricultural crops based on satellite data are described. Proposals are given for the development of research in the field of the use of space technologies and their widespread use in agriculture.
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Ushachev, Ivan Grigorevich, Andrei Viktorovich Kolesnikov, and Marina Viacheslavovna KHarina. "FORECAST OF PRODUCTION VOLUMES OF THE MAIN TYPES OF AGRICULTURAL PRODUCTS IN RUSSIA FOR 2025." AIC: economics, management, no. 3 (March 1, 2025): 3–15. https://doi.org/10.33305/253-3.

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The article presents the forecast of the main types of agricultural products production in Russia in 2025, made in line with the methodology developed in Agrarian policy and Agro-industrial complex development department of FSBSI FRC VNIIESH. The main forecast indicators are: areas and agricultural crops yields, livestock and productivity of animals, gross production. As a result of agricultural crops yields analysis from 1980 years onwards, the multidirectional dynamics of yield changes by regions of Russia and types of crops has been established. Favorable years for grain crops are not the s
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Gusmini, Gabriele, and Todd C. Wehner. "Fifty-five Years of Yield Improvement for Cucumber, Melon, and Watermelon in the United States." HortTechnology 18, no. 1 (2008): 9–12. http://dx.doi.org/10.21273/horttech.18.1.9.

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Yield data for the major cucurbit crops in the United States have been collected and summarized. Yield trends are presented for cucumber (Cucumis sativus; processing and fresh-market), melon (Cucumis melo; muskmelon and honeydew), and watermelon (Citrullus lanatus) for the period 1951–2005. Data have been obtained from the U.S. Department of Agriculture, as originally reported by six of its units: Agricultural Marketing Service, Agricultural Research Service, Bureau of Agricultural Economics, Economic Statistics Service, National Agricultural Statistics Service, and Statistical Reporting Servi
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Breskina, Galina M., Nina P. Masyutenko, Natalia A. Chuyan, and Artyom V. Klimov. "CROP GROWTH, DEVELOPMENT AND YIELD APPLYING AGRICULTURAL BIOTECHNOLOGIES." Land Reclamation and Hydraulic Engineering 15, no. 2 (2025): 297–318. https://doi.org/10.31774/2712-9357-2025-15-2-297-318.

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Purpose: to study the influence of agrobiotechnology on the growth, development, crop structure and biological yield of crops in the grain-row crop rotation link. Materials and methods. The studies were carried out in scientific and production experiment in 2022–2024 on slightly eroded typical chernozem (Kursk region, Medvensky district, Panino village). In the experiment, microbiological preparations (MBP) “Trichoplant, SK” and “Biogor-Zh” used to treat seeds, soil, crops and by-products were applied. Experiment options: 1) Control (generally accepted technology); 2) Chopped by-products (CP)
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Dusaev, Kh B., and A. Kh Dusaeva. "The production of major agricultural crops in the Orenburg region." Normirovanie i oplata truda v sel'skom hozyajstve (Rationing and remuneration of labor in agriculture), no. 1 (2022): 22–40. http://dx.doi.org/10.33920/sel-06-2201-02.

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A detailed analysis of the production of the main agricultural crops in the Orenburg region for a long-term period, taking into account of their sown areas, gross yields and yields in farms of all categories has been provided in the article. The purpose of the research was to analyze the production of the main agricultural crops in the Orenburg region. Based on the conducted research, transformational changes and trends in the regional production of grain, potatoes and vegetable crops have been identified. During the study period, a gradual reduction in the acreage of grain and leguminous crop
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Kirubanand, VB, V. Rohini, and V. Laxmankumar. "Internet of Things in Agriculture to Revolutionize Traditional Agricultural Industry." ITM Web of Conferences 37 (2021): 01018. http://dx.doi.org/10.1051/itmconf/20213701018.

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Agriculture is familiarly called “Farming”. Agriculture is the basic art to cultivate food which is a necessary need to every living individual. Agriculture needs the practice of science for cultivating the soil factors and growing crops. In traditional farming, it includes more labor work and less yield quantity. This demerit can be overcome by the modern farming techniques which makes use of the advanced technology and focuses on maximizing the yield and maintaining the quality. Earlier the farmers used to figure out the type of the soil based on their suspicion and they would never think of
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Parra-Londono, Sebastian, Jaime Andres Tigreros, and Carlos Alberto Montoya-Correa. "Colombian Crop Resilience: Evaluating National Yield Stability for Fruit and Vegetable Systems." Agriculture 14, no. 9 (2024): 1546. http://dx.doi.org/10.3390/agriculture14091546.

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In recent years the yield of fruits and vegetables has been decreasing, threatening Colombia’s food security. Analysis of crop production data may lead to identifying cropping systems that have shown better adaptability to changes in climatic and non-climatic factors associated with agricultural production. The open database AGRONET keeps data on the agricultural activities conducted in Colombia, allowing us to find the information organized by crops, regions and years. Aiming to identify resilient crop systems in Colombia, agricultural data on fruits and vegetables were analyzed. First, trend
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Dissertations / Theses on the topic "Yield of agricultural crops"

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Clark, Nathan J. "Investigating the relationship between yield risk and agri-environmental indicators." Lexington, Ky. : [University of Kentucky Libraries], 2002. http://lib.uky.edu/ETD/ukyagec2002t00040/Clark.pdf.

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Thesis (M.S.)--University of Kentucky, 2002.<br>Title from document title page. Document formatted into pages; contains viii, 51 p. : ill, maps. Includes abstract. Includes bibliographical references (p. 49-50).
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Ghane, Ehsan. "Evaluation of two technologies for enhancement of crop yield and drainage water quality." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1406114668.

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Mahama, George Yakubu. "Impact of cover crops and nitrogen application on nitrous oxide fluxes and grain yield of sorghum and maize." Diss., Kansas State University, 2015. http://hdl.handle.net/2097/18939.

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Doctor of Philosophy<br>Department of Agronomy<br>P. V. Vara Prasad<br>Leguminous cover crops systems have been envisaged as a critical component of sustainable agriculture due to their potential to increase soil productivity through cycling of carbon (C) and nitrogen (N) in agricultural systems. The objectives of this study were to evaluate the performance of leguminous summer cover crops; cowpea [Vigna unguiculata (L.) Walp.], pigeon pea [Cajanus cajan (L.) Millsp], sunn hemp (Crotalaria juncea L.) and double-cropped grain crops; grain sorghum [Sorghum bicolor (L.) Moench] and soybean [Glyci
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Hafner, Timothy. "The Effects of Within-Row Spacings & Cultivars on the Yield of Bush Snap Beans (Phaseolus vulgaris L.)." TopSCHOLAR®, 1990. https://digitalcommons.wku.edu/theses/2428.

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The effects of within-row spacings and cultivars on the yield of bush snap beans (Phaseolus vulgaris L.) were studied in two experiments. In each experiment, two cultivars and four within-row spacings (8, 15, 23 and 30 cm) were arranged in a split-plot with four replications. Spacing treatments were the whole plots and cultivars were the split plots. In 1988, the two cultivars were 'Blue Lake 274' and 'White Half Runner'. In 1989, the cultivars were 'Blue Lake 274' and 'Kentucky Wonder 125'. All rows were spaced 91 cm apart. Pods were harvested and oven dried. The data were subjected to analys
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Narasimhan, Balaji. "Development of indices for agricultural drought monitoring using a spatially distributed hydrologic model." Diss., Texas A&M University, 2004. http://hdl.handle.net/1969.1/2727.

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Farming communities in the United States and around the world lose billions of dollars every year due to drought. Drought Indices such as the Palmer Drought Severity Index (PDSI) and Standardized Precipitation Index (SPI) are widely used by the government agencies to assess and respond to drought. These drought indices are currently monitored at a large spatial resolution (several thousand km2). Further, these drought indices are primarily based on precipitation deficits and are thus good indicators for monitoring large scale meteorological drought. However, agricultural drought depends on s
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Stringer, Carol Elizabeth. "EVALUATING HEMP (CANNABIS SATIVA) AS A FORAGE BASED ON YIELD, NUTRITIVE ANALYSIS, AND MORPHOLOGICAL COMPOSITION." UKnowledge, 2018. https://uknowledge.uky.edu/pss_etds/104.

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This experiment examined the forage potential of hemp (Cannabis sativa) and kenaf (Hibiscus cannabinus). The objectives were to evaluate yield and forage nutritive value (i.e. NDF, ADF, ADL, IVTD, and CP) fluctuations over the course of a growing season based on planting date, morphological composition, and management. Three types of hemp (grain, fiber, and a dual- purpose type) and kenaf were planted on two dates and were sampled approximately every two weeks throughout the growing season at the University of Kentucky (UK) Research Farm in Lexington, KY. Subsamples were separated into morphol
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Galadima, A., S. H. Husman, and J. C. Silvertooth. "Plant Population Effect on Yield and Fiber Quality of Three Upland Cotton Varieties at Maricopa Agricultural Center, 2002." College of Agriculture, University of Arizona (Tucson, AZ), 2003. http://hdl.handle.net/10150/197724.

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A field experiment was conducted at the University of Arizona Maricopa Agricultural Center (MAC – 1100 ft. elevation) in 2002 to evaluate plant population relationships with conventional row spacing under a range of high population conditions with new Upland cotton (Gossypium hirsutum L.) varieties. The varieties, which included AG3601, DP458BR, and STV4892BR, were each planted to six densities of 15,000, 30,000, 45,000, 60,000, 75,000, and 90,000. Inseason plant measurement data revealed crop vigor and fruit retention levels were well within the optimum threshold for all varieties and populat
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Monteiro, Leonardo Amaral. "Sugarcane yield gap in Brazil: a crop modelling approach." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/11/11152/tde-08032016-142721/.

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Currently, the cropping area is around 10 million hectares, in which the sugarcane fields are expanding for marginal regions, mainly where grains and pasture were previously cultivated. From that, the objectives of this study were: to calibrate and evaluate a sugarcane yield model using data from 12 fields conducted under high technology field conditions; to evaluate the performance of a gridded system (NASA/POWER) to increase the spatial density of the weather stations in Brazil, to be employed as input data of crop simulation models; to map, in micro-region scale, the potential (Yp), the bes
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Santoro, Maria Julia. "CORN GRAIN YIELD COMPONENTS AND NUTRIENT ACCUMULATION IN RESPONSE TO NITROGEN, PLANT DENSITY AND HYBRID." UKnowledge, 2018. https://uknowledge.uky.edu/pss_etds/100.

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Modern maize hybrids exhibit higher yields, increased biomass production, stress tolerance and greater nitrogen (N) use efficiency. Increased biomass accumulation can influence nutrient uptake and lead to increased nutrient removal. Hybrids were tested at seeding rates (SR) of 74000 (low) and 148000 (supraoptimal) plants ha-1 and at N rates of 0 (deficient) and 390 (non-limiting) kg N ha-1. Plants were sampled at V7, V14, R3, R5 and R6 and separated into vegetative and reproductive fractions for determination of dry matter and N accumulation. Grain yield was harvested at R6. The high SR and hi
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Langenhoven, Petrus. "Yield and quality response of hydroponically grown tomatoes (Lycopersicon esculentum Mill.) to nitrogen source and growth medium." Thesis, Stellenbosch : Stellenbosch University, 2004. http://hdl.handle.net/10019.1/50232.

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Dissertation (PhD)--Stellenbosch University, 2004.<br>ENGLISH ABSTRACT: Pine sawdust-shavings (Pinus spp.) is at present a very popular soilless substrate in South African greenhouses. Growers use fresh pine sawdust-shavings as a substrate, which is biologically highly unstable. The greenhouse industry is looking at alternative organic substrates such as coco peat, which already went through a decomposition process and is more stable. A biological inactive substrate such as sand was included to compare microorganism activity with organic substrates. The main objective of this study was t
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Books on the topic "Yield of agricultural crops"

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V, Scholtz Richard, ed. Mathematical models of crop growth and yield. Marcel Dekker, 2002.

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

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Bradbury, Derek. Crops in Europe: Statistical systems for measuring area, production, and yield of non-cereal crops. Office for Official Publications of the European Communities, 1997.

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Fielder, Lonnie L. Measurement of price, yield, and revenue variability for Louisiana crops. Dept. of Agricultural Economics and Agribusiness, Louisiana Agricultural Experiment Station, Louisiana State University Agricultural Center, 1985.

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Gurian-Sherman, Doug. Failure to yield: Evaluating the performance of genetically engineered crops. Union of Concerned Scientists, 2009.

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

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bālaśelṭān, Ethiopia YaMāʻekalāwi stātistiks, ed. Time series data on area, production, and yield of major crops, 1979/80-1985/86 (1972-1978 E.C.). The Authority, 1987.

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Ash, Mark S. Regional crop yield response for U.S. grains. U.S. Dept. of Agriculture, Economic Research Service, 1987.

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Sakamoto, Clarence M. The water satisfaction index for estimating crop yield and harvested/planted area ratio in Botswana. Republic of Botswana, Dept. of Meteorological Services, Ministry of Works, Transport, and Communications, 1990.

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India. Directorate of Economics and Statistics., ed. District-wise area & production of crops, 1997-98 & 1998-99. Directorate of Economics & Statistics, Dept. of Agriculture & Cooperation, Ministry of Agriculture, Govt. of India, 2001.

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Book chapters on the topic "Yield of agricultural crops"

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Singh, V. K., B. S. Dwivedi, S. S. Rathore, R. P. Mishra, T. Satyanarayana, and K. Majumdar. "Timing Potassium Applications to Synchronize with Plant Demand." In Improving Potassium Recommendations for Agricultural Crops. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59197-7_13.

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AbstractPotassium (K) demand by crops is almost as high as that of nitrogen (N) and plays a crucial role in many plant metabolic processes. Insufficient K application results in soil K mining, deficiency symptoms in crops, and decreased crop yields and quality. Crop K demands vary with crop types, growth patterns, nutrient needs at different physiological stages, and productivity. Science-based K application in crops needs to follow 4R Nutrient Stewardship to ensure high yield, improved farm income, and optimum nutrient use efficiency. Studies around the world report widespread K deficiency, r
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Smith, Hendrik J., Gerhardus Trytsman, and Andre A. Nel. "On-farm experimentation for scaling-out conservation agriculture using an innovation systems approach in the north west province, South Africa." In Conservation agriculture in Africa: climate smart agricultural development. CABI, 2022. http://dx.doi.org/10.1079/9781789245745.0026.

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Abstract A project under the Farmer Innovation Programme (FIP) that aimed to adapt Conservation Agriculture (CA) among grain farmers in South Africa was implemented in a commercial farming area of the North West Province. The following on-farm, collaborative-managed trials produced key findings concerning: (i) plant population densities (high versus low) under CA; (ii) conventional crop systems versus CA crop systems; (iii) the testing and screening of cover crops; (iv) green fallow systems for soil restoration; and (v) livestock integration. Key results from these trials were that the yield o
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Stanford, George. "Nitrogen Requirements of Crops for Maximum Yield." In Agricultural Anhydrous Ammonia Technology and Use. American Society of Agronomy, Soil Science Society of America, 2015. http://dx.doi.org/10.2134/1966.nh3agricultural.c13.

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Strauss, Johann A. "Economic and yield comparisons of different crop and crop-pasture production systems." In Conservation agriculture in Africa: climate smart agricultural development. CABI, 2022. http://dx.doi.org/10.1079/9781789245745.0011.

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Abstract Over the past 15 years the adoption rate of Conservation Agriculture (CA) in southern South Africa has increased at a fast rate, although the adoption of the three pillars of CA was to varying degrees. The adoption of CA happened in the absence of any policy support framework directed to CA. The market drove the adaptation rate with a handful of local producers being the first to adopt no-till (NT) strategies. Long-term field experiments demonstrate that the effects of crop rotation include increased yields from the main wheat crop so that two-thirds of the present total wheat product
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Kumar, Kukatlapalli Pradeep, S. Babu Kumar, Amarthya Dutta Gupta, Kevin Johnson, and Meghan Mary Michael. "Agricultural Crop-Yield Prediction." In Data-Driven Farming. Auerbach Publications, 2024. http://dx.doi.org/10.1201/9781003485179-9.

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Mandal, Pankaj, Pronobesh Ghosh, Rittick Mondal, Harshita Shand, and Amit Kumar Mandal. "Rhizosphere Biology for Higher Growth and Yield." In Agricultural Crop Improvement. CRC Press, 2024. https://doi.org/10.1201/9781032630366-16.

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Postiglione, Paolo, Roberto Benedetti, and Federica Piersimoni. "Spatial Prediction of Agricultural Crop Yield." In Agricultural Survey Methods. John Wiley & Sons, Ltd, 2010. http://dx.doi.org/10.1002/9780470665480.ch22.

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Bell, Michael J., Michael L. Thompson, and Philip W. Moody. "Using Soil Tests to Evaluate Plant Availability of Potassium in Soils." In Improving Potassium Recommendations for Agricultural Crops. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59197-7_8.

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AbstractThe purpose of this chapter is to describe how bioavailable soil K is assessed or predicted by soil tests. Soil testing commonly refers to the collection of a sample of soil representative of a field or agronomic management unit and, by way of extraction using chemical reagents, determination of the quantity of a nutrient that can be related to plant uptake or yield. Normally only a small fraction of the total quantity of the nutrient present in the soil is extracted during the procedure, but if that amount can be correlated with actual crop uptake or overall crop productivity, then th
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White, Philip J., Michael J. Bell, Ivica Djalovic, Philippe Hinsinger, and Zed Rengel. "Potassium Use Efficiency of Plants." In Improving Potassium Recommendations for Agricultural Crops. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59197-7_5.

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AbstractThere are many terms used to define aspects of potassium (K) use efficiency of plants. The terms used most frequently in an agricultural context are (1) agronomic K use efficiency (KUE), which is defined as yield per unit K available to a crop and is numerically equal to the product of (2) the K uptake efficiency (KUpE) of the crop, which is defined as crop K content per unit K available and (3) its K utilization efficiency (KUtE), which is defined as yield per unit crop K content. There is considerable genetic variation between and within plant species in KUE, KUpE, and KUtE. Root sys
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Volenec, Jeffrey J., Sylvie M. Brouder, and T. Scott Murrell. "Broadening the Objectives of Future Potassium Recommendations." In Improving Potassium Recommendations for Agricultural Crops. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59197-7_14.

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AbstractPotassium (K) fertilizer recommendations for annual crops in the USA are generally founded in soil test results. The goal of this chapter is to highlight additional plant-related traits that may impact crop responses to K fertilization. This includes the role of tissue testing, the influence of luxury consumption, genetic improvement of K use efficiency, genotype × environment × management interactions on K uptake and yield, response to foliar K fertilization, intraplant K cycling, fungal associations and K uptake, the influence of K on crop quality, and the role of K in abiotic stress
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Conference papers on the topic "Yield of agricultural crops"

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Nayak, Anvesha, Pramathi Vummadi, Apoorva Raj, Nasam Saimani, and Suresh Jamadagni. "Predictive Modelling of Agricultural Factors to Maximize Crop Yield." In 17th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013369400003890.

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Sastry, YNVS Bhaskara, V. Deeban Chakravathy, Chittooru Venkat Charith, A. Pandiaraj, and P. Nancy. "Application of Intelligence in Agricultural with crop yield prediction." In 2025 International Conference on Computational Robotics, Testing and Engineering Evaluation (ICCRTEE). IEEE, 2025. https://doi.org/10.1109/iccrtee64519.2025.11052931.

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Sudhakar, R., N. V. Pallavi, G. Pushpa, A. Poorna Sasank Reddy, and M. Purushotham. "Crop Yield Prediction Based on the Characteristics of Agricultural Environment." In 2025 International Conference on Computational Robotics, Testing and Engineering Evaluation (ICCRTEE). IEEE, 2025. https://doi.org/10.1109/iccrtee64519.2025.11052989.

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Ramachandran, A. Ganesh, S. K. Saravanan, M. Bhanumathi, M. Sangeetha, and F. Mary Harin Fernandez. "Computer Vision for Agricultural Automation - Algorithmic Solutions for Crop Yield Predictions." In 2024 International Conference on Recent Advances in Science and Engineering Technology (ICRASET). IEEE, 2024. https://doi.org/10.1109/icraset63057.2024.10895915.

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Gupta, Namrata S., Dr Sirajbhai Nagalpara, Natvar S. Patel, and Shri Parvin V. Ami. "Optimizing Agricultural Productivity: Machine Learning Approaches for Enhanced Crop Yield Forecasting." In 2025 2nd International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE). IEEE, 2025. https://doi.org/10.1109/rmkmate64874.2025.11042845.

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Boincean, Boris. "Agricultura ecologică în contextul dezvoltării durabile a Republicii Moldova." In Scientific International Symposium "Plant Protection – Achievements and Perspectives". Institute of Genetics, Physiology and Plant Protection, Republic of Moldova, 2023. http://dx.doi.org/10.53040/ppap2023.63.

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Industrial model of agricultural intensification was oriented mainly towards higher level of yields and profit without taking in consideration the negative ecological and social consequences. The discrepancy in prices for nonrenewable sources of energy and their derivates, from one side, and relatively low prices for agricultural goods, from the other side, together with many other challenges faced by modern agriculture, have allowed to realize the unsustainability of the dominant concept of industrial intensification. Organic agriculture is one of the alternatives to conventional agriculture.
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Manolache, Alexandra. "Study on the influence of climate change on agricultural crops." In Economic growth in the face of global challenges. Consolidation of national economies and reduction of social inequalities: International Scientific-Practical Conference, XVIIIth edition. National Institute for Economic Research, 2024. https://doi.org/10.36004/nier.cecg.ii.2024.18.36.

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Climate change is having a significant impact on agricultural production around the world, affecting crops, cultivated area and yield. These climate changes include changes in temperature and precipitation, extreme weather events, and changes in regional weather patterns. Climate change brings major challenges for agriculture, with a significant negative impact on farmland, productivity and food security. In this regard, it is necessary to adapt through sustainable agricultural practices, the use of advanced technologies and the implementation of climate risk management strategies, to support
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Manolache, Alexandra Marina. "Study on the influence of climate change on agricultural crops." In Economic growth in the face of global challenges. Consolidation of national economies and reduction of social inequalities: International Scientific-Practical Conference, XVIIIth edition. National Institute for Economic Research, 2024. https://doi.org/10.36004/nier.cecg.i.2024.18.36.

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Climate change is having a significant impact on agricultural production around the world, affecting crops, cultivated area and yield. These climate changes include changes in temperature and precipitation, extreme weather events, and changes in regional weather patterns. Climate change brings major challenges for agriculture, with a significant negative impact on farmland, productivity and food security. In this regard, it is necessary to adapt through sustainable agricultural practices, the use of advanced technologies and the implementation of climate risk management strategies, to support
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Taranenko, V. V., and R. S. Sharifullin. "INFLUENCE OF FOLIAGE APPLICATION ON SUNFLOWER YIELD AND QUALITY." In «Breeding, seed production, cultivation technology and processing of agricultural crops». Federal State Budgetary Scientific Institution Federal Scientific Rice Centre, 2021. http://dx.doi.org/10.33775/conf-2021-292-296.

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Garkusha, S. V., S. A. Tesheva, and D. A. Pischenko. "SEED PRODUCTION AS A MAIN FACTOR OF INCREASING RICE YIELD." In «Breeding, seed production, cultivation technology and processing of agricultural crops». Federal State Budgetary Scientific Institution Federal Scientific Rice Centre, 2021. http://dx.doi.org/10.33775/conf-2021-172-175.

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Reports on the topic "Yield of agricultural crops"

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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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Beckman, Jayson, Fengxia Dong, Maros Ivanic, Jonas Jägermeyr, and Nelson Villoria. Climate-induced yield changes and TFP. Economic Research Service, U.S. Department of Agriculture, 2024. http://dx.doi.org/10.32747/2024.8534117.ers.

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Increasing agricultural productivity is vital to ensure that global food demand can be met. However, the impact of a changing climate on temperatures and precipitation could potentially influence agricultural productivity by affecting crop yields. This report combines the latest estimates of yield changes from the Agricultural Model Intercomparison and Improvement Project with projections of future productivity changes in the form of total factor productivity (TFP) to gain a better understanding of the future of agricultural production (and thus of food supply). Yield estimates are used from a
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Hertel, Thomas, Marshall Burke, and David Lobell. The Poverty Implications of Climate-Induced Crop Yield Changes by 2030. GTAP Working Paper, 2010. http://dx.doi.org/10.21642/gtap.wp59.

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Accumulating evidence suggests that agricultural production could be greatly affected by climate change, but there remains little quantitative understanding of how these agricultural impacts would affect economic livelihoods in poor countries. Here we consider three scenarios of agricultural impacts of climate change by 2030 (impacts resulting in low, medium, or high productivity) and evaluate the resulting changes in global commodity prices, national economic welfare, and the incidence of poverty in a set of 15 developing countries. Although the small price changes under the medium scenario a
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Sands, Ronald (Ronald D. )., Birgit Meade, James L. Seale, Sherman Robinson, and Riley Seeger. Scenarios of global food consumption. Economic Research Service, U.S. Department of Agriculture, 2023. http://dx.doi.org/10.32747/2023.8134356.ers.

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The global land base is under increasing pressure to provide food for a growing population. This report describes how increasing population, income, and agricultural productivity may affect global production and consumption of crops and food products by 2050. Results show that in an income-driven food demand scenario, production of world crop calories increases by 47 percent from 2011 to 2050. Demand for food calories and crop calories increases over time in all scenarios, with most of the adjustment through increases in crop yield (intensification). The amount of cropland also increases (exte
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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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Cavigelli, Michel. Mid-Atlantic Corn and Soybean Yields Show Great Variability in Response to Precipitation during Critical Growth Stages. USDA Northeast Climate Hub, 2018. http://dx.doi.org/10.32747/2018.6892663.ch.

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The mid-Atlantic coastal plain, where the FSP&#x0D; is located, has higher than average variability&#x0D; in corn and soybean yields compared to the&#x0D; most productive agricultural regions in the US.&#x0D; Precipitation is the primary driver of crop yield&#x0D; variability in this region and drought is often&#x0D; responsible for low yields.
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Eshed, Y., and Z. B. Lippman. Fine tuning the shoot and inflorescence architectures for improved tomato yield. United States-Israel Binational Agricultural Research and Development Fund, 2022. http://dx.doi.org/10.32747/2022.8134148.bard.

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In this project, we are determining the contribution of different types of variations, in gene function and in gene regulation, to altered shoot architecture first, and to field performance in the next stage. We are using tomato as a target, but also as a model for many other crops. Our focus is on two different components of yield associated traits - shoot architecture and organization of the inflorescence. Our focus was on two types of regulators; 1) genes involved in florigen - antiflogen balance and the way they impact the shoot, and 2) genes involved in inflorescence branching and it this
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Zamir, Dani, and Steven Tanksley. Fine Mapping and Genetic Interactions of Nearly-Isogenic Allelic Series Representing Yield and Quality QTLs Derived from Wild Tomato Species. United States Department of Agriculture, 2002. http://dx.doi.org/10.32747/2002.7586460.bard.

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Wild germplasm represents a rich source of QTLs capable of enhancing productivity of crop plants. Using the molecular linkage map of tomato in conjunction with novel population structures, we have identified QTLs from five Lycopersicon species that improve key yield and quality associated traits of processing tomatoes. In this research we employed multi-testing sites for fine mapping analysis of the different components of the affected traits combined with genetic interaction studies. Our results demonstrate that 'exotic libraries', which comprise of marker-defined genomic regions taken from w
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Asfaw, Etenesh. “Dehqan” Farms: Uzbekistan’s Underserved 70 Percent. TOSHKENT SHAHRIDAGI XALQARO VESTMINSTER UNIVERSITETI, 2020. https://doi.org/10.70735/vzxc8585.

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Support targeted to the small Dehqan farms would increase national agricultural productivity, supporting livelihoods in rural areas. Seventy percent of Uzbekistan’s agricultural output comes from the Dehqan farms that represent 95 percent of all farms and operate only one-fifth of all farmland. Dehqan farms have unrestricted production and marketing choices, stronger tenure security, and higher crop productivity compared with large farms. Dehqan farms, however, experience global yield gaps, have difficulty entering modern markets and own little incomes. Uzbekistan’s agriculture sector needs to
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