Academic literature on the topic 'Crop'

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Journal articles on the topic "Crop"

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Hill, Catherine M. "Crop Foraging, Crop Losses, and Crop Raiding." Annual Review of Anthropology 47, no. 1 (2018): 377–94. http://dx.doi.org/10.1146/annurev-anthro-102317-050022.

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Crop foraging or crop raiding concerns wildlife foraging and farmers’ reactions and responses to it. To understand crop foraging and its value to wildlife or its implications for humans requires a cross-disciplinary approach that considers the behavior and ecology of wild animals engaging in this behavior; the types and levels of competition for resources between people and wildlife; people's perceptions of and attitudes toward wildlife, including animals that forage on crops; and discourse about animals and their behaviors and how these discourses can be used for expressing dissent and distre
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Matthews, G. A. "Crop production and crop protection." Crop Protection 14, no. 8 (1995): 689–90. http://dx.doi.org/10.1016/0261-2194(95)90011-x.

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Sharma, Abhishek, Alokit Sharma, Ankita Arya, and Asit Joshi. "Crop Recommendation System." International Journal of Research Publication and Reviews 5, no. 5 (2024): 1095–98. http://dx.doi.org/10.55248/gengpi.5.0524.1124.

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Łukowiak, R., W. Grzebisz, and P. Barłóg. "Magnesium management in the soil-crop system – a crop rotation approach." Plant, Soil and Environment 62, No. 9 (2016): 395–401. http://dx.doi.org/10.17221/390/2016-pse.

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Christianson, Kersten. "Crop." Northern Review 46 (December 12, 2017): 176. http://dx.doi.org/10.22584/nr46.2017.018.

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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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Glassner, A. "Crop art. 2 [Crop graphical language]." IEEE Computer Graphics and Applications 24, no. 6 (2004): 78–85. http://dx.doi.org/10.1109/mcg.2004.48.

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Nalawade, Viraj, Bhagyashree Kadam, Chetan Jadhav, Gaurav Pabale, and Pradeep Kokane. "Crop Advisor: Intelligent Crop Recommendation System." Indian Journal of Agriculture Engineering 5, no. 1 (2025): 1–6. https://doi.org/10.54105/ijae.a1525.05010525.

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

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<strong>Abstract: </strong>Agriculture has long been a cornerstone of the Indian economy, crucial in sustaining livelihoods and contributing to national growth. By 2024, the sector will contribute approximately 18-20% of India's GDP and employ nearly half of the population. It also ensures food security for over 1.4 billion people. However, crop yields per hectare continue to lag international standards, which has been a significant factor contributing to the rising suicide rates among farmers. This paper proposes a machine learning-based Crop Regulating System to assist farmers. The system ta
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Dr. R. D. Mehta, Dr R. D. Mehta. "Crop Insurance in India." International Journal of Scientific Research 2, no. 1 (2012): 13–15. http://dx.doi.org/10.15373/22778179/jan2013/5.

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Dissertations / Theses on the topic "Crop"

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Kufimfutu, Bakelana ba. "Crop planting pattern effects on crop and weed growth /." The Ohio State University, 1992. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487778663287222.

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Åström, Petter. "Cash crops vs food crops : A case study of household's crop choices in Babati District." Thesis, Södertörn University College, School of Life Sciences, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:sh:diva-2605.

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<p>According to earlier research farmer's crop orientation in developing countries mainly depends on farm size, large-scale farmers prefer cash crop while small-scale farmers prefer subsistence crops. The first aim of this study is to see if this hypothesis can be applied on six households in Babati District in rural Tanzania. The second aim is to investigate if other factors than farm size affect crop portfolio choice and the final aim is to see if those crop portfolio models can be improved. A case-study research design and qualitative interviews are used. The primary data is based on a fiel
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Subedi-Chalise, Kopila. "Impacts of Crop Residue and Cover Crops on Soil Hydrological Properties, Soil Water Storage and Water Use Efficiency of Soybean Crop." Thesis, South Dakota State University, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10265200.

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<p> Cover crops and crop residue play a multifunctional role in improving soil hydrological properties, soil water storage and water use efficiency (WUE). This study was conducted to better understand the role of crop residue and cover crop on soil properties and soil water dynamics. The study was conducted at the USDA-ARS North Central Agricultural Research Laboratory, located in Brookings, South Dakota. Two residue removal treatments that include low residue removal (LRR) and high residue removal (HRR) were established in 2000 with randomized complete block design under no-till corn (Zea may
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Ramirez, Almeyda Jacqueline <1985&gt. "Lignocellulosic Crops in Europe: Integrating Crop Yield Potentials with Land Potentials." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amsdottorato.unibo.it/7854/1/Tesi_J.Ramirez_2017_Lignocellulosic%20crops%20potentials%20in%20EU.pdf.

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Given the ambitious EU targets to further decarbonise the economy, it can be expected that the demand for lignocellulosic biomass will continue to grow. Provisioning of part of this biomass by dedicated biomass crops becomes an option. This study presents integrated approach for crop allocation based on land availability and crop requirements. The model analysis to investigate the potential extension of unused land and its suitability for lignocellulosic crops was carried out in 37 European countries at the NUTS3 level. The CAPRI model predicts future land use changes and was used as a basic
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Murphy, Donal Patrick Lee. "Crop structure and crop productivity in winter barley, (Hordeum sativum)." Thesis, University of Newcastle Upon Tyne, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.262202.

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Perkins, Seth A. "Crop model review and sweet sorghum crop model parameter development." Thesis, Kansas State University, 2012. http://hdl.handle.net/2097/14037.

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Master of Science<br>Department of Biological and Agricultural Engineering<br>Kyle Douglas-Mankin<br>Opportunities for alternative biofuel feedstocks are widespread for a number of reasons: increased environmental and economic concerns over corn production and processing, limitations in the use of corn-based ethanol to 57 billion L (15 billion gal) by the Energy Independence and Security Act (US Congress, 2007), and target requirements of 136 billion L (36 billion gal) of renewable fuel production by 2022. The objective of this study was to select the most promising among currently available
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Schmitz, Austin. "Row crop navigation by autonomous ground vehicle for crop scouting." Thesis, Kansas State University, 2017. http://hdl.handle.net/2097/36237.

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Master of Science<br>Department of Biological & Agricultural Engineering<br>Daniel Flippo<br>Robotic vehicles have the potential to play a key role in the future of agriculture. For this to happen designs that are cost effective, robust, and easy to use will be necessary. Robotic vehicles that can pest scout, monitor crop health, and potentially plant and harvest crops will provide new ways to increase production within agriculture. At this time, the use of robotic vehicles to plant and harvest crops poses many challenges including complexity and power consumption. The incorporation of sma
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Watt, J. "3D crop modelling." Thesis, University College London (University of London), 2013. http://discovery.ucl.ac.uk/1421425/.

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Crop models have become increasingly useful tools for understanding and implementing sus¬tainable agricultural techniques and as a way of accurately predicting crop yields for economists and policy decision makers. Using remotely sensed imagery can significantly reduce the effort required to obtain the in¬puts for crop models and can provide regular sets of observations throughout a growing season. Empirical models can be used to extract information regarding the crop from remotely sensed images but have well-documented limitations. Coupling a crop model with a radiative transfer model allows
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Potter, Mark. "Biochemical studies of tissue glucosinolates for improvement of canola (Brassica napus) as a disease break within the southern Australian cereal rotation /." Title page, contents and summary only, 1998. http://web4.library.adelaide.edu.au/theses/09PH/09php8678.pdf.

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Thesis (Ph.D) -- University of Adelaide, Depts. of Plant Science and Crop Protection, 1998.<br>Thesis (Ph.D.)--University of Adelaide, Depts. of Plant Science and Crop Protection, 1999? Bibliographical references: leaves 112-125.
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Arnet, Kevin Broc. "Cover crops in no-tillage crop rotations in eastern and western Kansas." Thesis, Manhattan, Kan. : Kansas State University, 2010. http://hdl.handle.net/2097/4086.

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Books on the topic "Crop"

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H, Latos Tomas, ed. Cover crops and crop yields. Nova Science Publishers, 2009.

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Rasmussen, Henry. Crop dusters: "props in the crops". Motorbooks International, 1986.

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Morrison, Yedda Mari. Crop. Kelsey St. Press, 2003.

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1958-, Oerke E. C., ed. Crop production and crop protection: Estimated losses in major food and cash crops. Elsevier, 1994.

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Fageria, N. K. Maximizing crop yields. Marcel Dekker, 1992.

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Alberto, Alvarez Luis, and Paraguay. Ministerio de Agricultura y Ganadería., eds. Rubros complementarios para la diversificación de cultivos, por zonas ecológicas, en la región oriental del Paraguay. Ministerio de Agricultura y Ganadería, 1993.

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Jacobi, Ingo. Crop production. Joint Presidency Committee, 2008.

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Hammond, Gerald. Snatch crop. Macmillan London, 1991.

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Ouda, Samiha, Abd El-Hafeez Zohry, and Tahany Noreldin. Crop Rotation. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05351-2.

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Abdullah, Siti Nor Akmar, Ho Chai-Ling, and Carol Wagstaff, eds. Crop Improvement. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65079-1.

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Book chapters on the topic "Crop"

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Gabrys, Beata, John L. Capinera, Jesusa C. Legaspi, et al. "Crop." In Encyclopedia of Entomology. Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-6359-6_10092.

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Christou, Paul, and Roxana Savin. "Crop crop/cropping Science crop/cropping science and Technology Crop Technology , Introduction." In Encyclopedia of Sustainability Science and Technology. Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4419-0851-3_936.

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Christou, Paul, and Roxana Savin. "Crop crop/cropping Science crop/cropping science and Technology Crop Technology , Introduction." In Sustainable Food Production. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-5797-8_936.

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Farre, Gemma, Sonia Gomez-Galera, Shaista Naqvi, et al. "Biotechnology crop/cropping biotechnology and Nutritional Improvement crop/cropping nutritional improvement of Crops crop/cropping." In Encyclopedia of Sustainability Science and Technology. Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4419-0851-3_160.

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Farre, Gemma, Sonia Gomez-Galera, Shaista Naqvi, et al. "Biotechnology crop/cropping biotechnology and Nutritional Improvement crop/cropping nutritional improvement of Crops crop/cropping." In Sustainable Food Production. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-5797-8_160.

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Newell-McGloughlin, Martina. "Transgenic Crops transgenic crop , Next Generation transgenic crop breeding next generation." In Encyclopedia of Sustainability Science and Technology. Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4419-0851-3_165.

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Gatehouse, Angharad M. R., Martin G. Edwards, Natalie Ferry, and Micheal D. K. Owen. "Transgenic Crops transgenic crop , Environmental Impact transgenic crop breeding environmental impact." In Encyclopedia of Sustainability Science and Technology. Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4419-0851-3_240.

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Roy, Stuart J., and Mark Tester. "Increasing Salinity Tolerance crop/cropping salinity tolerance of Crops crop/cropping." In Encyclopedia of Sustainability Science and Technology. Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4419-0851-3_429.

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Newell-McGloughlin, Martina. "Transgenic Crops transgenic crop , Next Generation transgenic crop breeding next generation." In Sustainable Food Production. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-5797-8_165.

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Gatehouse, Angharad M. R., Martin G. Edwards, Natalie Ferry, and Micheal D. K. Owen. "Transgenic Crops transgenic crop , Environmental Impact transgenic crop breeding environmental impact." In Sustainable Food Production. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-5797-8_240.

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Conference papers on the topic "Crop"

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

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

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Dobiasova, Ivana, Martin Minarik, and Jan Jan. "CHANGE OF THE GROWING SEASON IN THE CONTEXT OF CLIMATE CHANGE IN SLOVAKIA." In 24th SGEM International Multidisciplinary Scientific GeoConference 2024. STEF92 Technology, 2024. https://doi.org/10.5593/sgem2024v/4.2/s18.26.

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Climate change significantly affects agriculture in Slovakia by altering precipitation patterns, temperatures, and the length of the growing season. Common effects include drought, more intense rainfall, higher summer temperatures, and increased pest populations, all of which influence crop production and livestock breeding. Human activities have already impacted atmospheric properties such as temperature, precipitation, CO2 levels, and ground-level ozone, and this trend is expected to continue. While warmer climates may benefit some crops, the increased frequency of droughts, floods, and heat
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Chowdhury, Mahfuzulhoq, Mohammad Obaidur Rahman, and Shah Alam. "Proprietor: A Farmer Assistance Smartphone Application with Crop Planner, Crop Disease Help, Agri-expert Search, and Crop Suggestion Features." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10725364.

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Trout, Thomas, and Jim Gartung. "Use of Crop Canopy Size to Estimate Crop Coefficient for Vegetable Crops." In World Environmental and Water Resources Congress 2006. American Society of Civil Engineers, 2006. http://dx.doi.org/10.1061/40856(200)297.

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A, Ahila, Akshay BL, Arish Balaji KB, and Gagan KV. "Smart Agriculture: A Deep Learning Model for Plant Disease Diagnosis." In International Conference on Recent Trends in Computing & Communication Technologies (ICRCCT’2K24). International Journal of Advanced Trends in Engineering and Management, 2024. http://dx.doi.org/10.59544/jyem5589/icrcct24p138.

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Accurate early stage detection of crop diseases is essential for maintaining crop quality and yield by enabling timely and effective treatments. However, disease detection often requires specialized expertise in plant pathology and significant experience. Therefore, an automated crop disease detection system is invaluable in agriculture, as it supports the development of an early warning system. To build this system, we developed a stepwise disease detection model utilizing images of diseased and healthy plants, along with a CNN based algorithm incorporating five pre trained models. The detect
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Vorontsov, V. A. "Correct seeding of crops under various systems of basic tillage in the crop rotation." In Agrobiotechnology-2021. Publishing house of RGAU - MSHA, 2021. http://dx.doi.org/10.26897/978-5-9675-1855-3-2021-10.

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Studies conducted at the Tambov Research Institute of Agricultural Research have found that the systems of basic tillage had a significant impact on the contamination of cultivated crops in crop rotations. The use of a permanent surface tillage system in technological complexes of crop cultivation increases the contamination of crops. The traditional multi-depth dump and combined (dump-free) systems of basic tillage in crop rotations are the most effective agrotechnical techniques in clearing fields of weeds.
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Tuum, Caroline, Maire Nurmet, and Katrin Lemsalu. "Exploring farmers’ decision for crop insurance." In 23rd International Scientific Conference. “Economic Science for Rural Development 2022”. Latvia University of Life Sciences and Technologies. Faculty of Economics and Social Development, 2022. https://doi.org/10.22616/esrd.2022.56.056.

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The existence of adverse weather conditions increases the production risks of farms through crop yields in the crop sector. One of the measures to mitigate the risks from the weather in agriculture is to insure crops. In the Estonian insurance market, crop insurance is available to farmers. Government-based crop insurance subsidies apply to crop insurance premium, but so far, there is little demand for it among farmers. The aim of the paper is to identify the terms of crop insurance that play a role in deciding in favour of crop insurance. A survey examines the factors related to the insurance
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Shetty, Nishith S., Naman N. Karanth, and Vinay V. Hegde. "Enhancing Crop Productivity: Integrated Solutions for Crop Yield, Crop Recommendation, and Crop Disease Management." In 2023 7th International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS). IEEE, 2023. http://dx.doi.org/10.1109/csitss60515.2023.10334167.

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Buryakov, A. E., and O. V. Butkova. "ANALYSIS AND EVALUATION OF PRODUCTION AND SALES OF CROP PRODUCTS TO INCREASE THE EFFICIENCY OF AGRARIAN FORMATIONS." In INNOVATIVE TECHNOLOGIES IN SCIENCE AND EDUCATION. DSTU-Print, 2020. http://dx.doi.org/10.23947/itno.2020.166-170.

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The article presents the results of the analysis of the production and sale of crop products of an agro-industrial enterprise. As part of the study, the dynamics of production of the main types of products of the pilot enterprise was assessed. the calculation of the influence of factors on the gross yield of crop production and the influence of the structure of crops of agricultural crops on the volume of their production and on the amount of profit The analysis of profit and profitability of sales of crop production is carried out
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Reports on the topic "Crop"

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Nair, Ajay, Brandon H. Carpenter, Jennifer L. Tillman, and Dana L. Jokela. Integrating Cover Crops in High Tunnel Crop Production. Iowa State University, Digital Repository, 2014. http://dx.doi.org/10.31274/farmprogressreports-180814-2392.

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Wilson, Kelly R., Mary K. Hendrickson, Ryan Milhollin, J. Alan Weber, and Robert L. Myers. Is the U.S. cover crop seed industry ready to support projected adoption rates? A snapshot of the industry. University of Missouri - Columbia, 2024. https://doi.org/10.32469/10355/106442.

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"In recent years, interest in adoption of cover crops on U.S. farmland has surged in the public and private sectors, as well as with farmers. Cover crops are crops planted to cover the soil and reach different climate and soil health benefits, such as reducing erosion; increasing water availability; and providing weed, pest or disease control; and to enhance biodiversity on a farm (Clark 2015). To promote use of climate-smart practices such as cover crops, the federal government launched new funding programs, including the Partnerships for Climate-Smart Commodities program, which invested $3.1
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Havlovic, Bernard J. Crop Season. Iowa State University, Digital Repository, 2002. http://dx.doi.org/10.31274/farmprogressreports-180814-1114.

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Havlovic, Bernard J. Crop Season. Iowa State University, Digital Repository, 2001. http://dx.doi.org/10.31274/farmprogressreports-180814-2456.

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Ndoye, Aïssatou, Khadim Dia, and Racine Ly. AAgWa Crop Production Forecasts Brief Series - Issue N.04. AKADEMIYA2063, 2023. http://dx.doi.org/10.54067/acpf.04.

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The Africa Agriculture Watch (AAgWa) Crop Production Forecast Brief 4 by AKADEMIYA2063 seeks to provide accurate production levels in Burkina Faso using the Africa Food Crop Production (AfCP) model. The AfCP, developed by AKADEMIYA2063, is an artificial intelligence (AI) based predictive model applied to remotely sensed bio-geophysical data to estimate crop yields and harvests before the harvesting period for nine crops across 47 African countries.
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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.08. AKADEMIYA2063, 2023. http://dx.doi.org/10.54067/acpf.08.

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The Africa Agriculture Watch (AAgWa) Crop Production Forecast Brief 8 by AKADEMIYA2063 provides accurate and timely millet production statistics for Guinea using the Africa Crop Production (AfCP) model. The AfCP developed at AKADEMIYA2063 is an artificial intelligence (AI) based forecasting model applied to remotely sensed geo-biophysical data to produce estimates of crop yields and harvests at the beginning of every growing season for nine crops in 47 African countries.
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Ndoye, Aïssatou, Khadim Dia, and Racline Ly. AAgWa Crop Production Forecasts Brief Series - Issue N.05. AKADEMIYA2063, 2023. http://dx.doi.org/10.54067/acpf.05.

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The Africa Agriculture Watch (AAgWa) Crop Production Forecast Brief 5 by AKADEMIYA2063 provides timely and accurate statistics on millet production in Sierra Leone using the Africa Crop Production (AfCP) model. Developed at AKADEMIYA2063, the AfCP 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 for nine crops across 47 African countries.
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Ndoye, Aïssatou, Khadim Dia, and Racine Ly. AAgWa Crop Production Forecasts Brief Series - Issue N.09. AKADEMIYA2063, 2023. http://dx.doi.org/10.54067/acpf.09.

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The Africa Agriculture Watch (AAgWa) Crop Production Forecast Brief 9 by AKADEMIYA2063 aims to provide timely and accurate statistics of millet production in Guinea-Bissau using the Africa Crop Production (AfCP) model developed at AKADEMIYA2063. The AfCP is an artificial intelligence (AI) based forecasting model applied to remotely sensed geo-biophysical data to estimate crop yields and harvests at the beginning of every growing season for nine crops across 47 African countries.
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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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