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Journal articles on the topic 'Crop'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

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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2

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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3

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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4

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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5

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

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6

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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7

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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8

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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9

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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10

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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11

Attarod, P., and M. Aoki. "Measurements of the actual evapotranspiration and crop coefficients of summer and winter seasons crops in Japan." Plant, Soil and Environment 55, No. 3 (2009): 121–27. http://dx.doi.org/10.17221/324-pse.

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The main goal was to understand the trends of actual evapotranspiration (AET) and crop coefficient (<I>K<sub>c</sub></I>) in summer and winter seasons crops in Japan, maize, soybean, wheat and Italian rye-grass. Bowen ratio energy balance technique (BREB) was applied to measure the AET and heat flux between ground surface and atmosphere. Measurements were carried out using an automatic weather station (AWS) installed seasonally in the experimental farm of Tokyo University of Agriculture and Technology (TUAT). Penman-Monteith equation recommended by FAO was used to calcu

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12

Choudhary, Mukesh, Vishal Singh, Vignesh Muthusamy, and Shabir Hussain Wani. "Harnessing Crop Wild Relatives for Crop Improvement." LS: International Journal of Life Sciences 6, no. 2 (2017): 73. http://dx.doi.org/10.5958/2319-1198.2017.00009.4.

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13

KURATA, Kenji, and Genji OHARA. "Considerations on Crop Management and Crop Cognition." Journal of Agricultural Meteorology 44, no. 1 (1988): 33–36. http://dx.doi.org/10.2480/agrmet.44.33.

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14

Umaerus, Vilhelm. "Crop rotation in relation to crop protection." Netherlands Journal of Plant Pathology 98, S2 (1992): 241–49. http://dx.doi.org/10.1007/bf01974491.

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15

Smilovic, Mikhail, Tom Gleeson, and Jan Adamowski. "Crop kites: Determining crop-water production functions using crop coefficients and sensitivity indices." Advances in Water Resources 97 (November 2016): 193–204. http://dx.doi.org/10.1016/j.advwatres.2016.09.010.

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16

Zulauf, Carl, Gary Schnitkey, and Michael Langemeier. "Average Crop Revenue Election, Crop Insurance, and Supplemental Revenue Assistance: Interactions and Overlap for Illinois and Kansas Farm Program Crops." Journal of Agricultural and Applied Economics 42, no. 3 (2010): 501–15. http://dx.doi.org/10.1017/s1074070800003692.

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Farm-level data from Illinois and Kansas for the 1991-2007 crops are used to examine the interaction and overlap among crop revenue insurance, Supplemental Revenue Assistance (SURE), and Average Crop Revenue Election (ACRE). Compared with 75% Crop Revenue Coverage Insurance (75% CRCP), ACRE provides more payments and has a greater impact on minimum farm revenue for the Illinois farms. In contrast, for the Kansas farms, 75% CRCP has the greater impact. SURE's relative impact on the Illinois and Kansas farms depends on the metric. The overlap in payments from ACRE and 75% CRCP resulting from cov

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17

KUMAR, YOGENDRA. "Nanofertilizers for enhancing nutrient use efficiency, crop productivity and economic returns in winter season crops of Rajasthan." Annals of Plant and Soil Research 22, no. 4 (2020): 324–35. http://dx.doi.org/10.47815/apsr.2020.10001.

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The results of 600 on-farm trials with 8 crops conducted during winter season in different districts of Rajasthan have proved that the quantity of urea being applied by the farmers to supply nitrogen to the crops can be successfully reduced to half. The yields obtained with 50% less nitrogen plus 2 sprays of nano-nitrogen in standing crops gave yields higher than that applied in most of the 8 crops tested in these trials. Apart from this, effect of the Nano-Zn and Nano-Cu was also evaluated. As the deficiencies of these micronutrients were not universal like nitrogen, the significant responses

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18

Pronozin, A. Yu, M. K. Bragina, and E. A. Salina. "Crop pangenomes." Vavilov Journal of Genetics and Breeding 25, no. 1 (2021): 57–63. http://dx.doi.org/10.18699/vj21.007.

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Progress in genome sequencing, assembly and analysis allows for a deeper study of agricultural plants’ chromosome structures, gene identif ication and annotation. The published genomes of agricultural plants proved to be a valuable tool for studing gene functions and for marker-assisted and genomic selection. However, large structural genome changes, including gene copy number variations (CNVs) and gene presence/absence variations (PAVs), prevail in crops. These genomic variations play an important role in the functional set of genes and the gene composition in individuals of the same species

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19

Wright, Christopher. "Money Crop." CFA Institute Magazine 18, no. 1 (2007): 28–33. http://dx.doi.org/10.2469/cfm.v18.n1.4419.

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20

Anderson, Ray, and Andrew French. "Crop Evapotranspiration." Agronomy 9, no. 10 (2019): 614. http://dx.doi.org/10.3390/agronomy9100614.

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Evapotranspiration (ET) is one of the largest components of the water cycle, and accurately measuring and modeling ET is critical for improving and optimizing agricultural water management. However, parameterizing ET in croplands can be challenging due to the wide variety of irrigation strategies and techniques, crop varieties, and management approaches that employ traditional tabular ET and make crop coefficient approaches obsolete. This special issue of Agronomy highlights nine approaches to improve the measurement and modeling of ET across a range of spatial and temporal resolutions and dif

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21

Sirotenko, Oleg D. "Crop Modeling." Agronomy Journal 93, no. 3 (2001): 650–53. http://dx.doi.org/10.2134/agronj2001.933650ax.

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22

Garnier, Marido. "Crop-topic." Chimères N° 99, no. 2 (2021): 291–97. http://dx.doi.org/10.3917/chime.099.0291.

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23

Thomas, Jim. "Hay Crop." English Journal 74, no. 3 (1985): 93. http://dx.doi.org/10.2307/817127.

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24

Cockshull, K. E. "CROP ENVIRONMENTS." Acta Horticulturae, no. 312 (July 1992): 77–86. http://dx.doi.org/10.17660/actahortic.1992.312.9.

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25

Ramdas, Kamalini. "CROP CIRCLES." London Business School Review 27, no. 1 (2016): 12–13. http://dx.doi.org/10.1111/2057-1615.12087.

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26

Llewellin, Mary. "Top crop." Nursery World 2016, no. 19 (2016): 16–17. http://dx.doi.org/10.12968/nuwa.2016.19.16.

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27

Bray, Natasha. "Crop growth." Nature Reviews Neuroscience 22, no. 1 (2020): 5. http://dx.doi.org/10.1038/s41583-020-00411-x.

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28

Dubock, Adrian C. "Crop conundrum." Nutrition Reviews 67, no. 1 (2009): 17–20. http://dx.doi.org/10.1111/j.1753-4887.2008.00137.x.

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29

Nde, Rodrigue Kongne, Jean Louis Ebongue Kedieng Fendji, Blaise Omer Yenke, and Julius Schöning. "Crop selection." Smart Agricultural Technology 9 (December 2024): 100602. http://dx.doi.org/10.1016/j.atech.2024.100602.

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30

Bullock, D. G. "Crop rotation." Critical Reviews in Plant Sciences 11, no. 4 (1992): 309–26. http://dx.doi.org/10.1080/07352689209382349.

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31

Brzezina, Andrzej S. "Crop Diseases." Mycological Research 105, no. 2 (2001): 256. http://dx.doi.org/10.1017/s0953756200229870.

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32

Bullock, D. G. "Crop Rotation." Critical Reviews in Plant Sciences 11, no. 4 (1992): 309. http://dx.doi.org/10.1080/713608037.

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33

Wolinsky, Howard. "Crop shortages." EMBO reports 11, no. 7 (2010): 508–10. http://dx.doi.org/10.1038/embor.2010.87.

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34

James, Chris. "Crop invasion!" New Scientist 197, no. 2645 (2008): 25. http://dx.doi.org/10.1016/s0262-4079(08)60534-5.

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35

Sirotenko, Oleg D. "Crop Modeling." Agronomy Journal 93, no. 3 (2001): 650—a. http://dx.doi.org/10.2134/agronj2001.933650-ax.

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36

Poluektov, Ratmir A., and Alexandre G. Topaj. "Crop Modeling." Agronomy Journal 93, no. 3 (2001): 653–59. http://dx.doi.org/10.2134/agronj2001.933653x.

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37

A, Swathi. "CROP PREDICTION." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 02 (2024): 1–10. http://dx.doi.org/10.55041/ijsrem28605.

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Cereal crops such as rice, wheat, and different pulses account for the majority of India's food output. Predicting crop yields far ahead of harvest would assist policymakers and farmers in making informed decisions about agronomy, crop selection, and agricultural planning. Such forecasts will also assist related sectors in planning their logistical operations. The goal of the research is to create a machine learning model that can generate such predictions. The model is trained using a dataset that incorporates soil data from the previous decade, with features such as Ph value, temperature, an

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38

Higgs, Roger L., Arthur E. Peterson, and William H. Paulson. "Crop Rotations." Journal of Soil and Water Conservation 45, no. 1 (1990): 68–70. https://doi.org/10.1080/00224561.1990.12456432.

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39

Jin, Xiuliang, Wanneng Yang, John H. Doonan, and Clement Atzberger. "Crop phenotyping studies with application to crop monitoring." Crop Journal 10, no. 5 (2022): 1221–23. http://dx.doi.org/10.1016/j.cj.2022.09.001.

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40

Hmielowski, Tracy. "Crop Wild Relatives Special Section in Crop Science." CSA News 62, no. 6 (2017): 10–12. http://dx.doi.org/10.2134/csa2017.62.0617.

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41

宮川, 恒., and 敦司岡澤. "Emerging Technologies in Crop Protection and Crop Production." Journal of Pesticide Science 32, Special (2007): S62—S65. http://dx.doi.org/10.1584/jpestics.32s.s62.

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42

Yash Gupta and Garima Srivastava. "Crop recommendation system for growing best suitable crop." International Journal of Science and Research Archive 12, no. 1 (2024): 2928–36. http://dx.doi.org/10.30574/ijsra.2024.12.1.1111.

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Agriculture is critical to ensuring global food security and financial stability, but faces significant challenges such as climate change, resource scarcity and population growth. To address these issues, crop recommendation systems have proven to be valuable tools to help farmers decide which crops to plant. These systems aim to increase crop yields and make better use of resources. This report offers an in-depth look at these promising crops, covering their importance, opportunities, challenges and future prospects. By analyzing existing research and case studies, we hope to provide a cleare

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43

Adam, M., M. Corbeels, P. A. Leffelaar, H. Van Keulen, J. Wery, and F. Ewert. "Building crop models within different crop modelling frameworks." Agricultural Systems 113 (November 2012): 57–63. http://dx.doi.org/10.1016/j.agsy.2012.07.010.

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44

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

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AbstractAfield study was conducted between 1972 and 1982 to compare the effects of previous crop on row crop yields under rainfed conditions in eastern Nebraska. The objectives were to determine the effects of fallow and three previous crops: corn (Lea. maysLJ, soybeans /Glycine max (L.) Mem], and grain sorghum /Sorghum bicolor (L.) Moench], on the growth and grain yield of the same crops. The study was conducted on a Sharpsburg silty clay loam (fine, montmorillonitic, mesicf Typic Argiudoll). Corn grain yield was most variable (C. V. 23.4percent) compared to soybean (C. V. 13.6percent) or gra

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45

Bobst, Barry W., and Joe T. Davis. "Beef Cow Numbers, Crop Acreage, and Crop Policy." American Journal of Agricultural Economics 69, no. 4 (1987): 771–76. http://dx.doi.org/10.2307/1242187.

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46

Pester, Todd. "Increasing Crop Competitiveness to Weeds Through Crop Breeding." Journal of Crop Production 2, no. 1 (1999): 59–76. http://dx.doi.org/10.1300/j144v02n01_04.

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47

Yu, Jisang, and Daniel A. Sumner. "Effects of subsidized crop insurance on crop choices." Agricultural Economics 49, no. 4 (2018): 533–45. http://dx.doi.org/10.1111/agec.12434.

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48

Craufurd, Peter Q., Vincent Vadez, S. V. Krishna Jagadish, P. V. Vara Prasad, and M. Zaman-Allah. "Crop science experiments designed to inform crop modeling." Agricultural and Forest Meteorology 170 (March 2013): 8–18. http://dx.doi.org/10.1016/j.agrformet.2011.09.003.

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49

Hill, Catherine M. "Primate Crop Feeding Behavior, Crop Protection, and Conservation." International Journal of Primatology 38, no. 2 (2017): 385–400. http://dx.doi.org/10.1007/s10764-017-9951-3.

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50

Abernethy, Jonathon, Peter Beeson, Claire Boryan, Kevin Hunt, and Luca Sartore. "Preseason crop type prediction using crop sequence boundaries." Computers and Electronics in Agriculture 208 (May 2023): 107768. http://dx.doi.org/10.1016/j.compag.2023.107768.

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