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Journal articles on the topic 'Crop yield'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

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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2

. R, Saravanan, and Arulselvan Gnanamonickam . A. "Crop Yield Prediction using Machine Learning." International Journal of Research Publication and Reviews 5, no. 10 (2024): 2433–39. http://dx.doi.org/10.55248/gengpi.5.1024.2825.

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3

Eisenhut, Marion, and Andreas P. M. Weber. "Improving crop yield." Science 363, no. 6422 (2019): 32–33. http://dx.doi.org/10.1126/science.aav8979.

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4

Brown, Alastair. "Crop-yield drivers." Nature Climate Change 4, no. 12 (2014): 1050. http://dx.doi.org/10.1038/nclimate2458.

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5

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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6

Bisht, P. S., R. Puniya, P. C. Pandey, and D. K. Singh. "Grain yield and yield components of rice as influenced by different crop establishment methods." International Rice Research Notes 32, no. 2 (2007): 33–34. https://doi.org/10.5281/zenodo.6955835.

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This article 'Grain yield and yield components of rice as influenced by different crop establishment methods' appeared in the International Rice Research Notes series, created by the International Rice Research Institute (IRRI) to expedite communication among scientists concerned with the development of improved technology for rice and rice-based systems. The series is a mechanism to help scientists keep each other informed of current rice research findings. The concise scientific notes are meant to encourage rice scientists to communicate with one another to obtain details on the research rep

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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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Deng, Xiaohui, Barry J. Barnett, Yingzhuo Yu, Gerrit Hoogenboom, and Axel Garcia y. Garcia. "Alternative Crop Insurance Indexes." Journal of Agricultural and Applied Economics 40, no. 1 (2008): 223–37. http://dx.doi.org/10.1017/s1074070800023567.

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Three index-based crop insurance contracts are evaluated for representative south Georgia corn farms. The insurance contracts considered are based on indexes of historical county yields, yields predicted from a cooling degree-day production model, and yields predicted from a crop-simulation model. For some of the representative farms, the predicted yield index contracts provide yield risk protection comparable to the contract based on historical county yields, especially at lower levels of risk aversion. The impact of constraints on index insurance choice variables is considered and important

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10

Deng, Xiaohui, Barry J. Barnett, Gerrit Hoogenboom, Yingzhuo Yu, and Axel Garcia y. Garcia. "Alternative Crop Insurance Indexes." Journal of Agricultural and Applied Economics 40, no. 01 (2008): 223–37. http://dx.doi.org/10.1017/s1074070800028078.

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Three index-based crop insurance contracts are evaluated for representative south Georgia corn farms. The insurance contracts considered are based on indexes of historical county yields, yields predicted from a cooling degree-day production model, and yields predicted from a crop-simulation model. For some of the representative farms, the predicted yield index contracts provide yield risk protection comparable to the contract based on historical county yields, especially at lower levels of risk aversion. The impact of constraints on index insurance choice variables is considered and important

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11

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 (2019): 143–46. http://dx.doi.org/10.21276/ijircst.2019.7.6.1.

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12

Gupta, M. L., and R. C. Gautam. "Effect of Source and Rate of Phosphorus on Yield and Yield Attributes of Rice." International Rice Research Newsletter 13, no. 3 (1988): 27. https://doi.org/10.5281/zenodo.7136056.

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This article 'Effect of Source and Rate of Phosphorus on Yield and Yield Attributes of Rice' appeared in the International Rice Research Newsletter series, created by the International Rice Research Institute (IRRI). The primary objective of this publication was to expedite communication among scientists concerned with the development of improved technology for rice and for rice based cropping systems. This publication will report what scientists are doing to increase the production of rice in as much as this crop feeds the most densely populated and land scarce nations in the world.

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13

DAS, H. P., A. D. PUJARI, and A. CHOWDHURY. "Dependance of soybean yield on crop evapotranspiration, crop duration and rainfall." MAUSAM 49, no. 4 (2021): 503–6. http://dx.doi.org/10.54302/mausam.v49i4.3663.

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In the present study, data for four stations viz., Banswara, Bhopal, Parbhani and Rahuri for the years from 1990 to 1993 have been utilized to understand various aspects of evapotranspiration of the soybean crop. An attempt has also been made to find out the impact of rainfall and crop duration at different phases on the seed yield.  The yield was found to be significantly correlated with the rainfall during vegetative phase. Crop growth duration exert positive effect on the soybean yield and that a longer flowering period is favourable for higher yields.  The results a

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14

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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15

Ang, James B., Per G. Fredriksson, and Satyendra Kumar Gupta. "Crop Yield and Democracy." Land Economics 96, no. 2 (2020): 265–90. http://dx.doi.org/10.3368/le.96.2.265.

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16

Zörb, C., C. ‐M Geilfus, and K. ‐J Dietz. "Salinity and crop yield." Plant Biology 21, S1 (2018): 31–38. http://dx.doi.org/10.1111/plb.12884.

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17

Heagle, A. S. "Ozone and Crop Yield*." Annual Review of Phytopathology 27, no. 1 (1989): 397–423. http://dx.doi.org/10.1146/annurev.py.27.090189.002145.

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18

Ramirez, Octavio A., Sukant Misra, and James Field. "Crop‐Yield Distributions Revisited." American Journal of Agricultural Economics 85, no. 1 (2003): 108–20. http://dx.doi.org/10.1111/1467-8276.00106.

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19

Feng, Sifang, Zengchao Hao, Xuan Zhang, and Fanghua Hao. "Changes in climate-crop yield relationships affect risks of crop yield reduction." Agricultural and Forest Meteorology 304-305 (July 2021): 108401. http://dx.doi.org/10.1016/j.agrformet.2021.108401.

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20

Sonal, Chaudhari, Haroon Shahadatali Shaikh, Govind Parashuram Pandey, and Dhruvin Dharmesh Vadalia. "Agrilyst: The Crop Advisor." International Journal of Innovative Science and Research Technology 7, no. 4 (2022): 296–302. https://doi.org/10.5281/zenodo.6497488.

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As India is an agrarian country, its economy depends mostly on the growth of agricultural yields and agro-industrial products. Data mining is an emerging research area in crop yield analysis. Yield prediction is a very important issue in agriculture. Every farmer is interested in how much yield he can expect. Discuss the various related attributes such as location, pH from which soil alkalinity is determined. In addition, the percentage of nutrients such as nitrogen (N), phosphorus (P) and potassium (K). Nutritional value of the soil in this region, you can determine the amount of precipitatio

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21

Demydenko, O. V. "Dynamics of crop yields depending on fertilizer, cultivation method and crop rotation type." Agriculture and plant sciences: theory and practice, no. 2 (February 8, 2024): 32–45. http://dx.doi.org/10.54651/agri.2024.02.05.

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To analyse the series of dynamics of winter wheat, peas, sugar beet, sunflower and soybeans under different types of crop rotation, tillage and fertilization in a long-term stationary experiment. Methods. Statistical analysis (non-parametric statistics, ARIMA method, singular spectral method) in the central part of the Left-Bank Forest-Steppe. Results. The yield of winter wheat in a crop rotation with peas under systematic ploughing was 4.67–5.15 t/ha, surface tillage interrupted by ploughing for sugar beet – 5.0–5.05 t/ha, with constant surface tillage for all crops in the crop rotation – 4

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22

Matějková, Š., J. Kumhálová, and J. Lipavský. "Evaluation of crop yield under different nitrogen doses of mineral fertilization." Plant, Soil and Environment 56, No. 4 (2010): 163–67. http://dx.doi.org/10.17221/196/2009-pse.

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Yields of winter wheat, winter rape and oats were evaluated in the field; the field was divided into the site-specific zones and treated with variable doses of nitrogen fertilizer in years 2004–2006. Measurements of the yields were carried out with a yield monitor placed in a combine harvester. The measured data were processed into the yield maps by means of ArcGIS 9.2 software. Variable application of fertilizer should balance yield potential of the field. Generally, total yield variability on the field after the application of various doses of experimental fertilizer was similar in the years

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23

Nagender, T., D. Raji Reddy, G. Sreenivas, P. Leela Rani, and Ch Pallavi. "Study of Cotton crop yield optimization in Telangana using Crop Simulation models and GIS techniques." Agriculture Association of Textile Chemical and Critical Reviews 13, no. 2 (2025): 193–98. https://doi.org/10.21276/aatccreview.2025.13.02.192.

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To delineate the potential crop production zones of cotton and strategies to improve the lower potential zones in Telangana through experimental means is labororious and cumbersome, so the present study aimed at the application of calibrated and validated crop simulation models for delineation of potential production zones by using GIS techniques in Telangana State. Model application through seasonal analysis the optimum plant density and nitrogen level for the MRC 7201 cultivar was 1,11,111 plants ha -1 at 60 cm x 15 cm spacing with 120 kg N ha -1 The simulated average potential non-water-lim

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24

Zhang, X. G., and Y. K. Huang. "Effect of Seedlings Hill on Individual Rice Plant Yield and Yield Components." International Rice Research Newsletter 15, no. 4 (1990): 21–22. https://doi.org/10.5281/zenodo.7179491.

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This article 'Effect of Seedlings/Hill on Individual Rice Plant Yield and Yield Components' appeared in the International Rice Research Newsletter series, created by the International Rice Research Institute (IRRI). The primary objective of this publication was to expedite communication among scientists concerned with the development of improved technology for rice and for rice based cropping systems. This publication will report what scientists are doing to increase the production of rice in as much as this crop feeds the most densely populated and land scarce nations in the world.

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Sreekumar, S. G., V. G. Nair, and R. B. Asan. "Effect of Planting Overage Seedlings on Rice Duration, Yield, and Yield Attributes." International Rice Research Newsletter 13, no. 6 (1988): 29–30. https://doi.org/10.5281/zenodo.7146274.

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This article 'Effect of Planting Overage Seedlings on Rice Duration, Yield, and Yield Attributes' appeared in the International Rice Research Newsletter series, created by the International Rice Research Institute (IRRI). The primary objective of this publication was to expedite communication among scientists concerned with the development of improved technology for rice and for rice based cropping systems. This publication will report what scientists are doing to increase the production of rice in as much as this crop feeds the most densely populated and land scarce nations in the world.

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26

Wang, Yuhan, Qian Zhang, Feng Yu, et al. "Progress in Research on Deep Learning-Based Crop Yield Prediction." Agronomy 14, no. 10 (2024): 2264. http://dx.doi.org/10.3390/agronomy14102264.

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In recent years, crop yield prediction has become a research hotspot in the field of agricultural science, playing a decisive role in the economic development of every country. Therefore, accurate and timely prediction of crop yields is of great significance for the national formulation of relevant economic policies and provides a reasonable basis for agricultural decision-making. The results obtained through prediction can selectively observe the impact of factors such as crop growth cycles, soil changes, and rainfall distribution on crop yields, which is crucial for predicting crop yields. A

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27

Biddappa, Changulanda B., and Dr Srikanth V. "Crop Yield Prediction on Agriculture Using Machine Learning." International Journal of Research Publication and Reviews 5, no. 3 (2024): 165–67. http://dx.doi.org/10.55248/gengpi.5.0324.0803.

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28

Kucharik, Christopher J., Tanjona Ramiadantsoa, Jien Zhang, and Anthony R. Ives. "Spatiotemporal trends in crop yields, yield variability, and yield gaps across the USA." Crop Science 60, no. 4 (2020): 2085–101. http://dx.doi.org/10.1002/csc2.20089.

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29

Gong, Liyun, Miao Yu, Shouyong Jiang, Vassilis Cutsuridis, and Simon Pearson. "Deep Learning Based Prediction on Greenhouse Crop Yield Combined TCN and RNN." Sensors 21, no. 13 (2021): 4537. http://dx.doi.org/10.3390/s21134537.

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Currently, greenhouses are widely applied for plant growth, and environmental parameters can also be controlled in the modern greenhouse to guarantee the maximum crop yield. In order to optimally control greenhouses’ environmental parameters, one indispensable requirement is to accurately predict crop yields based on given environmental parameter settings. In addition, crop yield forecasting in greenhouses plays an important role in greenhouse farming planning and management, which allows cultivators and farmers to utilize the yield prediction results to make knowledgeable management and finan

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30

Abera, T., and M. Belissa. "Effects of precursor crops and management levels on the straw and grain yield of wheat at Horro highland, Western Oromiya." Acta Agronomica Hungarica 53, no. 3 (2005): 273–82. http://dx.doi.org/10.1556/aagr.53.2005.3.4.

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The sustainable production of wheat may be possible by integrating crop rotation with improved crop management practices. The maximum grain yield of wheat was observed when field pea was the precursor crop. The precursor crop and management levels showed a significant effect on the mean straw and grain yields of wheat. Field pea as precursor crop gave a better wheat grain yield with both improved and farmers' cultural practices. Both local and improved varieties gave a better response to management levels on the field pea precursor field. Local and improved varieties gave higher yields with in

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Nyéki, Anikó, and Miklós Neményi. "Crop Yield Prediction in Precision Agriculture." Agronomy 12, no. 10 (2022): 2460. http://dx.doi.org/10.3390/agronomy12102460.

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Predicting crop yields is one of the most challenging tasks in agriculture. It plays an essential role in decision making at global, regional, and field levels. Soil, meteorological, environmental, and crop parameters are used to predict crop yield. A wide variety of decision support models are used to extract significant crop features for prediction. In precision agriculture, monitoring (sensing technologies), management information systems, variable rate technologies, and responses to inter- and intravariability in cropping systems are all important. The benefits of precision agriculture inv

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Braunworth, William S., and Harry J. Mack. "Crop-Water Production Functions for Sweet Corn." Journal of the American Society for Horticultural Science 114, no. 2 (1989): 210–15. http://dx.doi.org/10.21273/jashs.114.2.210.

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Abstract Sweet corn (Zea mays L.) was irrigated using randomized complete block and line source experimental designs in 1984 and 1985 on a mixed, mesic Cumulic Ultic Haploxeroll soil. Irrigations were scheduled when ≈50% of the available water was depleted in the root zone of the 100% treatment to refill the root zone to 0% to 100% of field capacity (five irrigation levels). Four yield parameters were measured for all plots: yield of all ears before husking, yield of good husked ears, kernel yield (fresh), and total dry matter production of plants and ears. Maximum relative total unhusked ear

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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 (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 conditi

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Neill, D. E., and G. B. Follas. "Use of crop sensing technology in crop protection research." New Zealand Plant Protection 64 (January 8, 2011): 287. http://dx.doi.org/10.30843/nzpp.2011.64.5993.

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Crop sensing technology is a new tool being rapidly adopted by farmers as a key component of precision agriculture This technology uses sensors to calculate normalized difference vegetative index (NDVI) by emitting red and near infrared light towards the crop and measuring the crops reflectance NDVI is used to evaluate canopy greenness plant biomass and as an indicator of plant health and vigour The methodology relevance and benefits of using this technology in crop protection trials are currently unclear A handheld Greenseeker (Ntech Industries USA) was used to record NDVI on a range of trial

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35

TeKrony, Dennis M., and Dennis B. Egli. "DOES SEED VIGOR INFLUENCE CROP YIELD?" HortScience 26, no. 6 (1991): 797A—797. http://dx.doi.org/10.21273/hortsci.26.6.797a.

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Both seed viability and vigor directly affect the performance of seeds planted to regenerate the crop. Although seed quality can influence many aspects of performance (e.g., total emergence, rate of emergence), this presentation will primarily examine the relationship of seed vigor to one aspect of performance - crop yield. Reductions in yield can be indirectly related to low seed vigor if the low vigor seed results in plant populations that are below a critical level. Thus, we investigated the direct effects of seed vigor on yield in the absence of population differences for annual crops that

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36

Wimalasiri, Eranga M., Ebrahim Jahanshiri, Tengku Adhwa Syaherah Tengku Mohd Suhairi, et al. "Basic Soil Data Requirements for Process-Based Crop Models as a Basis for Crop Diversification." Sustainability 12, no. 18 (2020): 7781. http://dx.doi.org/10.3390/su12187781.

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Data from global soil databases are increasingly used for crop modelling, but the impact of such data on simulated crop yield has not been not extensively studied. Accurate yield estimation is particularly useful for yield mapping and crop diversification planning. In this article, available soil profile data across Sri Lanka were harmonised and compared with the data from two global soil databases (Soilgrids and Openlandmap). Their impact on simulated crop (rice) yield was studied using a pre-calibrated Agricultural Production Systems Simulator (APSIM) as an exemplar model. To identify the mo

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Chen, Hongbiao. "Research on Optimisation Models for Crop Planting Planning." Highlights in Science, Engineering and Technology 131 (March 25, 2025): 180–86. https://doi.org/10.54097/qjyty260.

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In response to the current problem of low crop yields due to generally low crop yields and plot utilisation, this paper aims to improve plot utilisation to increase crop yield. The increase in yield cannot be realised as there is no better solution provided at present. Crop yield growth is facing a bottleneck as there is no better solution to achieve yield increase. This paper takes yield maximisation as the core objective, and establishes a multi-objective linear programming model for the situation where more than the stagnant part of the wasted and wasted crops are sold at a reduced price of

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Lejiņš, Andris, and Biruta Lejiņa. "THE GRAIN CROP YIELD IN DIFFERENT CROP ROTATION AND EFFICIENCY OF HERBICIDES AND FUNGICIDES TREATMENT." Environment. Technology. Resources. Proceedings of the International Scientific and Practical Conference 1 (June 23, 2007): 125. http://dx.doi.org/10.17770/etr2007vol1.1729.

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Complex field experiments were carried out in Agricultural research institute in 1969. The field trials included five different crop rotation systems. In each 6-field rotation system the specific percentage of cereals (%) varied from 50 to 100%, perennial grass (clover+ timothy) - 16.7 to 33.3%. The highest winter rye yields were obtained from crop rotation systems with cereal proportion up to 66%. Including buckwheat in the crop rotation winter rye cultivation is highly productive in crop rotation systems with cereal proportion even up to 83%. Yield of winter rye in long-term monocultural sow

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Mekecha, Banchigize B., and Alexander V. Gorbatov. "Crop yield prediction in Ethiopia using gradient boosting regression." Proceedings of Tomsk State University of Control Systems and Radioelectronics 27, no. 3 (2024): 125–29. https://doi.org/10.21293/1818-0442-2024-27-3-125-129.

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Nowadays, machine learning algorithms and methods are used in multiple areas of studies to achieve practical and productive solutions. Agriculture is one of the industries where the impact issignificant, especially in the area of crop yield prediction and crop selection which is crucial for ensuring food security and improving agricultural practices. In a country like Ethiopia, where the economy is highly dependent on agriculture, and farming in particular, leveraging the powers of AI and machine learning is crucial. However, the use of these technologies in Ethiopian agriculture remains limit

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40

Vidal, Alain. "Looking back at the causes of 25-year crop yield stagnation in Europe." Notes Académiques de l'Académie d'agriculture de France / Academic Notes of the French Academy of Agriculture 17, no. 1 (2024): 1–10. http://dx.doi.org/10.58630/pubac.not.a619313.

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Across the world, but especially in Europe, staple crop yields have been stagnating since the mid-1990s, when breakpoints appeared in wheat, maize, and barley yield evolution. We look back here at recent evidence about the most likely causes for crop yield stagnation. If crop yields had followed the 1950-1990 trend in Europe, they would now be 20% higher than in the 1990s, when they started stagnating. Climate change reasonably explains one third of the difference. Recent research shows that increased crop diversity sustains crop yields more effectively than monoculture, but there is to date n

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41

Sufiyan, Ibrahim, K. D. Mohammed, and Magaji J.I. "ASSESSMENT OF CROP YIELD AND RAINFALL SIMULATION IN NASARAWA TOWN NASARAWA STATE NIGERIA." Journal CleanWAS 4, no. 2 (2020): 75–78. http://dx.doi.org/10.26480/jcleanwas.02.2020.75.78.

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Recent technology use simulation to predict the amount and total crop production and yield in a particular piece of land. Crop yield is termed as the growth of crop per unit area. This study calculates the crop yield for 20 years and uses simulation to produce 18 years of crop yields at different locations in Nasarawa Local Government Area of Nasarawa State Nigeria. the study applies the use of time series analysis of both Linear, quadratic and growth curve models to ascertain the crop yield. The result indicates that there is a high amount of rainfall in the preceding year from 2020 -2038 wit

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42

Mueller, Nathaniel D., and Seth Binder. "Closing Yield Gaps: Consequences for the Global Food Supply, Environmental Quality & Food Security." Daedalus 144, no. 4 (2015): 45–56. http://dx.doi.org/10.1162/daed_a_00353.

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The social, economic, and environmental costs of feeding a burgeoning and increasingly affluent human population will depend, in part, on how we increase crop production on under-yielding agricultural landscapes, and by how much. Such areas have a “yield gap” between the crop yields they achieve and the crop yields that could be achieved under more intensive management. Crop yield gaps have received increased attention in recent years due to concerns over land scarcity, stagnating crop yield trends in some important agricultural areas, and large projected increases in food demand. Recent analy

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Payne, A. B., K. B. Walsh, and P. P. Subedi. "Automating mango crop yield estimation." Acta Horticulturae, no. 1130 (December 2016): 581–88. http://dx.doi.org/10.17660/actahortic.2016.1130.87.

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44

Mahul, Olivier. "Optimum Area Yield Crop Insurance." American Journal of Agricultural Economics 81, no. 1 (1999): 75–82. http://dx.doi.org/10.2307/1244451.

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Márton, L. "Fertilisation, rainfall and crop yield." Acta Agronomica Hungarica 52, no. 2 (2004): 165–72. http://dx.doi.org/10.1556/aagr.52.2004.2.7.

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The effect of rainfall quantity and distribution and of N, P, K, Ca and Mg fertilisation on the yields of rye, potato, winter wheat and triticale were evaluated in the 42 years of a long-term mineral fertilisation experiment [soil (acidic, sandy, brown forest) × fertilisation (N, P, K, Ca, Mg) × rainfall (quantity, distribution) × crop (rye, potato, winter wheat, triticale)] set up in 1962 under fragile agro-ecological conditions in the Nyírlugos-Nyírség region of Eastern Hungary. The soil had the following agrochemical characteristics: pH (H2O) 5.9, pH (KCl) 4.7, hydrolytic acidity 8.4, hy1 0

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46

Ben-Ari, Tamara, and David Makowski. "Decomposing global crop yield variability." Environmental Research Letters 9, no. 11 (2014): 114011. http://dx.doi.org/10.1088/1748-9326/9/11/114011.

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Holden, J. H. W. "Crop Evolution, Adaptation and Yield." Outlook on Agriculture 23, no. 4 (1994): 305–6. http://dx.doi.org/10.1177/003072709402300412.

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Feldmann, Kenneth A. "Steroid regulation improves crop yield." Nature Biotechnology 24, no. 1 (2006): 46–47. http://dx.doi.org/10.1038/nbt0106-46.

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Norwood, F. Bailey, Matthew C. Roberts, and Jayson L. Lusk. "Reply: Ranking Crop Yield Models." American Journal of Agricultural Economics 88, no. 4 (2006): 1111–12. http://dx.doi.org/10.1111/j.1467-8276.2006.00920.x.

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Li, Chengyun, Xiahong He, Shusheng Zhu, et al. "Crop Diversity for Yield Increase." PLoS ONE 4, no. 11 (2009): e8049. http://dx.doi.org/10.1371/journal.pone.0008049.

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