Log in

Relevant bibliographies by topics / Crop yield / Journal articles

To see the other types of publications on this topic, follow the link: Crop yield.

Journal articles on the topic 'Crop yield'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 June 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Crop yield.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

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 (March 1990): 33–37. http://dx.doi.org/10.1017/s0889189300003209.

Full text

Abstract:

AbstractAfield study was conducted between 1972 and 1982 to compare the effects of previous crop on row crop yields under rainfed conditions in eastern Nebraska. The objectives were to determine the effects of fallow and three previous crops: corn (Lea. maysLJ, soybeans /Glycine max (L.) Mem], and grain sorghum /Sorghum bicolor (L.) Moench], on the growth and grain yield of the same crops. The study was conducted on a Sharpsburg silty clay loam (fine, montmorillonitic, mesicf Typic Argiudoll). Corn grain yield was most variable (C. V. 23.4percent) compared to soybean (C. V. 13.6percent) or grain sorghum (C. V. 9.5 percent) yields. Corn was also the most sensitive crop to previous crop effects. The range of treatment yields for each crop was 47 percent, 22 percent, and 11 percent of the overall means for corn, soybean, and sorghum, respectively. Previous crop affected yields for all crops, but the effects were not consistent across years. All crops produced highest yield following fallow. Yields of corn, soybean, and grain sorghum following fallow were 74, 25, and 10 percent higher than their respective monoculture yields. In years of average precipitation, a corn-soybean sequence produced the greatest yield. In years having above- or below-normal precipitation, a grain sorghum-soybean sequence produced the highest yield.

APA, Harvard, Vancouver, ISO, and other styles

2

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

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Eisenhut, Marion, and Andreas P. M. Weber. "Improving crop yield." Science 363, no. 6422 (January 3, 2019): 32–33. http://dx.doi.org/10.1126/science.aav8979.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Brown, Alastair. "Crop-yield drivers." Nature Climate Change 4, no. 12 (November 26, 2014): 1050. http://dx.doi.org/10.1038/nclimate2458.

Full text

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

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 (December 1, 2007): 33–34. https://doi.org/10.5281/zenodo.6955835.

Full text

Abstract:

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 reported.

APA, Harvard, Vancouver, ISO, and other styles

7

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 (May 30, 2025): 1–6. https://doi.org/10.54105/ijae.a1525.05010525.

Full text

Abstract:

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 historical and current yield data, weather conditions, soil quality and fertiliser usage from farmers and predicts weather impact, rainfall, and disease effect to predict crop yield before sowing. Also, the system takes inputs such as current market data, sowed land, market import/export data, historical retail data, and consumer data for market demand analysis. Machine learning algorithms analyze this data to predict the market demand and the yield for a chosen crop. After that machine learning algorithms like Regression Forest (RF) and Support Vector Machine (SVM) were used to provide Decision support to Farmers. Regression models like Support Vector Machines (SVM) and Random Forests (RF), Multiple Linear Regression (MLR) and classification models like K-Nearest Neighbors (KNN) are utilized for Crop Yield Prediction. Time series models such as Auto Regressive Integrated Moving Average (ARIMA), and Genetics Algorithms (GAs) are used for Market Demand Analysis.

APA, Harvard, Vancouver, ISO, and other styles

8

Viraj, Nalawade. "Crop Advisor: Intelligent Crop Recommendation System." Indian Journal of Agriculture Engineering (IJAE) 5, no. 1 (May 30, 2025): 1–6. https://doi.org/10.54105/ijae.A1525.05010525.

Full text

Abstract:

<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 takes inputs such as historical and current yield data, weather conditions, soil quality and fertiliser usage from farmers and predicts weather impact, rainfall, and disease effect to predict crop yield before sowing. Also, the system takes inputs such as current market data, sowed land, market import/export data, historical retail data, and consumer data for market demand analysis. Machine learning algorithms analyze this data to predict the market demand and the yield for a chosen crop. After that machine learning algorithms like Regression Forest (RF) and Support Vector Machine (SVM) were used to provide Decision support to Farmers. Regression models like Support Vector Machines (SVM) and Random Forests (RF), Multiple Linear Regression (MLR) and classification models like K-Nearest Neighbors (KNN) are utilized for Crop Yield Prediction. Time series models such as AutoRegressive Integrated Moving Average (ARIMA), and Genetics Algorithms (GAs) are used for Market Demand Analysis.

APA, Harvard, Vancouver, ISO, and other styles

9

Husain, Dr Mohammad, and Dr Rafi Ahmad Khan. "Date Palm Crop Yield Estimation – A Framework." International Journal of Innovative Research in Computer Science & Technology 7, no. 6 (November 2019): 143–46. http://dx.doi.org/10.21276/ijircst.2019.7.6.1.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

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 (April 2008): 223–37. http://dx.doi.org/10.1017/s1074070800023567.

Full text

Abstract:

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 interactions among constrained, conditionally optimized, choice variables are analyzed.

APA, Harvard, Vancouver, ISO, and other styles

11

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 (April 2008): 223–37. http://dx.doi.org/10.1017/s1074070800028078.

Full text

Abstract:

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 interactions among constrained, conditionally optimized, choice variables are analyzed.

APA, Harvard, Vancouver, ISO, and other styles

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 (June 1, 1988): 27. https://doi.org/10.5281/zenodo.7136056.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

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 (December 16, 2021): 503–6. http://dx.doi.org/10.54302/mausam.v49i4.3663.

Full text

Abstract:

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 also indicate that the soybean crop consume maximum water during the vegetative stage.

APA, Harvard, Vancouver, ISO, and other styles

14

A, Swathi. "CROP PREDICTION." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 02 (February 8, 2024): 1–10. http://dx.doi.org/10.55041/ijsrem28605.

Full text

Abstract:

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, and crop name, as well as labels such as crop yield. The model learns the link between the yield and variables such as soil type, location, and forecast using appropriate machine learning techniques.

APA, Harvard, Vancouver, ISO, and other styles

15

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

Full text

APA, Harvard, Vancouver, ISO, and other styles

16

Zörb, C., C. ‐M Geilfus, and K. ‐J Dietz. "Salinity and crop yield." Plant Biology 21, S1 (September 5, 2018): 31–38. http://dx.doi.org/10.1111/plb.12884.

Full text

APA, Harvard, Vancouver, ISO, and other styles

17

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

Full text

APA, Harvard, Vancouver, ISO, and other styles

18

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

Full text

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

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 (April 27, 2022): 296–302. https://doi.org/10.5281/zenodo.6497488.

Full text

Abstract:

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 precipitation in the region, the composition of the soil. All these data attributes will be analyzed, they will train the data with various suitable machine learning algorithms to build a model. The system comes with a model to predict crop yield precisely and accurately, and gives the end user the appropriate recommendations on the required fertilizer ratio based on the soil and atmospheric parameters of the land, which they improve to increase the yield and the income of the raise farmers.

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

Abstract:

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 – 4.50–4.64 t/ha. In the crop rotation with perennial grasses, the yield of winter wheat grain was the highest under surface tillage – 4.89–4.95 t/ha, and under no-till and ploughing, the yield of wheat was 4.73 and 4.50 t/ha, which is significantly lower (NIR0.5=0.25) compared to systematic ploughing. The yield of maize in a crop rotation with peas and grasses was the highest under ploughing: 9.45–10.0 t/ha and 11.3 t/ha, respectively. Under no-till tillage, corn grain yields were lower by 1.03 and 0.57 t/ha and 1.7 t/ha, respectively, in crop rotations, and under surface tillage, yields decreased to 8.53–8.85 t/ha. Under moldboardless cultivation, grain yields tended to decrease, but remained within the range of reliable values: yields decreased by 0.76 and 0.57 t/ha or 9.3% and 8.8%; under surface tillage – by 0.86 and 0.92 t/ha or 10.5% and 14.3% (reliable value). Conclusions. The autocorrelation in the series of soybean dynamics in 7–10–seed rotations indicates the absence of trend, and no clearly defined cycles of grain yield changes were found. In crop rotations with a short rotation (3–5 fallow crop rotations), a cycle at lag 4 (3 years) was found at the limit of reliability, and a less pronounced cycle at lag 14 (8 years). On average, for 3–10 crop rotations, yield trends are weakly expressed, and cyclicality is weakly expressed at lags 4 and 14 (3 and 8 years). The autocorrelation function of yield change indicates a high trendiness. The cyclicality is well expressed at lags 11–13 (7–8 years), and the change in the sign of autocorrelation is observed at lag 6 (3–4 years).

APA, Harvard, Vancouver, ISO, and other styles

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 (April 15, 2010): 163–67. http://dx.doi.org/10.17221/196/2009-pse.

Full text

Abstract:

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 2004 (11.3%), 2005 (14.7%) and 2006 (11.7%) in comparison with the year 2003 (25.02%). Variable application of nitrogen in the site-specific zones, created on the basis of the yield levels, decreased the yield variability in comparison with the uniform dose. Different doses of nitrogen fertilizer also enabled to increase utilization of production potential of the experimental field.

APA, Harvard, Vancouver, ISO, and other styles

23

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 (August 1, 1990): 21–22. https://doi.org/10.5281/zenodo.7179491.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

24

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 (December 1, 1988): 29–30. https://doi.org/10.5281/zenodo.7146274.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

25

Wang, Yuhan, Qian Zhang, Feng Yu, Na Zhang, Xining Zhang, Yuchen Li, Ming Wang, and Jinmeng Zhang. "Progress in Research on Deep Learning-Based Crop Yield Prediction." Agronomy 14, no. 10 (October 1, 2024): 2264. http://dx.doi.org/10.3390/agronomy14102264.

Full text

Abstract:

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. Although traditional machine learning methods can obtain an estimated crop yield value and to some extent reflect the current growth status of crops, their prediction accuracy is relatively low, with significant deviations from actual yields, and they fail to achieve satisfactory results. To address these issues, after in-depth research on the development and current status of crop yield prediction, and a comparative analysis of the advantages and problems of domestic and foreign yield prediction algorithms, this paper summarizes the methods of crop yield prediction based on deep learning. This includes analyzing and summarizing existing major prediction models, analyzing prediction methods for different crops, and finally providing relevant views and suggestions on the future development direction of applying deep learning to crop yield prediction research.

APA, Harvard, Vancouver, ISO, and other styles

26

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 (March 21, 2024): 165–67. http://dx.doi.org/10.55248/gengpi.5.0324.0803.

Full text

APA, Harvard, Vancouver, ISO, and other styles

27

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 (May 28, 2020): 2085–101. http://dx.doi.org/10.1002/csc2.20089.

Full text

APA, Harvard, Vancouver, ISO, and other styles

28

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 (July 1, 2021): 4537. http://dx.doi.org/10.3390/s21134537.

Full text

Abstract:

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 financial decisions. It is thus important to accurately predict the crop yield in a greenhouse considering the benefits that can be brought by accurate greenhouse crop yield prediction. In this work, we have developed a new greenhouse crop yield prediction technique, by combining two state-of-the-arts networks for temporal sequence processing—temporal convolutional network (TCN) and recurrent neural network (RNN). Comprehensive evaluations of the proposed algorithm have been made on multiple datasets obtained from multiple real greenhouse sites for tomato growing. Based on a statistical analysis of the root mean square errors (RMSEs) between the predicted and actual crop yields, it is shown that the proposed approach achieves more accurate yield prediction performance than both traditional machine learning methods and other classical deep neural networks. Moreover, the experimental study also shows that the historical yield information is the most important factor for accurately predicting future crop yields.

APA, Harvard, Vancouver, ISO, and other styles

29

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 (October 1, 2005): 273–82. http://dx.doi.org/10.1556/aagr.53.2005.3.4.

Full text

Abstract:

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 intensive management and chemical fertilizer application. Field pea as precursor crop gave a combined grain yield advantage of 32% relative to barley. Management practices produced a combined grain yield advantage of 16 to 73% when field pea was the precursor crop, compared to barley. The use of field pea as precursor crop with improved management practices is essential to maximize wheat yields. Better grain yields and higher net returns were achieved with field pea as precursor crop compared to barley. Using field pea as precursor crop is the most successful management option for sustainable wheat production.

APA, Harvard, Vancouver, ISO, and other styles

30

Nyéki, Anikó, and Miklós Neményi. "Crop Yield Prediction in Precision Agriculture." Agronomy 12, no. 10 (October 11, 2022): 2460. http://dx.doi.org/10.3390/agronomy12102460.

Full text

Abstract:

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 involve increasing crop yield and crop quality, while reducing the environmental impact. Simulations of crop yield help to understand the cumulative effects of water and nutrient deficiencies, pests, diseases, and other field conditions during the growing season. Farm and in situ observations (Internet of Things databases from sensors) together with existing databases provide the opportunity to both predict yields using “simpler” statistical methods or decision support systems that are already used as an extension, and also enable the potential use of artificial intelligence. In contrast, big data databases created using precision management tools and data collection capabilities are able to handle many parameters indefinitely in time and space, i.e., they can be used for the analysis of meteorology, technology, and soils, including characterizing different plant species.

APA, Harvard, Vancouver, ISO, and other styles

31

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 (March 1989): 210–15. http://dx.doi.org/10.21273/jashs.114.2.210.

Full text

Abstract:

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 yield and near-maximum evapotranspiration (ET) were obtained at 85% of maximum water applied, indicating that high yields can be maintained with deficit irrigation. Without irrigation, only 44% of maximum yield was obtained. Maximum water use efficiency (WUE), defined as the total unhusked ear yield in kg·ha−1·mm−1ET, occurred between 407 and 418 mm of ET. The maximum WUE corresponded to ≈313 mm water applied (WA); maximum yield, however, occurred within the range of 449 to 518 mm WA. Irrigation treatments to achieve maximum WUE were predicted to result in a 10% yield reduction.

APA, Harvard, Vancouver, ISO, and other styles

32

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

Full text

Abstract:

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 are harvested at three stages; during vegetative growth, early reproductive growth or at full reproductive maturity. Seed vigor affects vegetative growth and is frequently related to yield in crops that are harvested during vegetative growth or during early reproductive growth. However, there is usually no relationship between vigor and yield in crops harvested at full reproductive maturity because seed yields at full reproductive maturity are usually not closely associated with vegetative growth. The use of high vigor planting seed can be justified for all crops; however, to insure adequate plant populations over the wide range of field conditions which occur during emergence.

APA, Harvard, Vancouver, ISO, and other styles

33

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.

Full text

Abstract:

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 trials from 20082011 to establish whether crop sensing could replace visual assessments for disease and enable yield prediction NDVI readings were compared against other parameters measured in the trials such as disease scores green leaf area percentage and yields In some trials the NDVI followed similar trends to disease green leaf retention and yields However in other cases where clear treatment effects were recorded through visual or yield assessments there were no differences in NDVI between the treatments As NDVI can be affected by a number of factors it was concluded that crop sensing technology can be used as an additional objective measurement in conjunction with standard assessment practice but without further investigation cannot replace traditional assessment methods

APA, Harvard, Vancouver, ISO, and other styles

34

Zscheischler, Jakob, Rene Orth, and Sonia I. Seneviratne. "Bivariate return periods of temperature and precipitation explain a large fraction of European crop yields." Biogeosciences 14, no. 13 (July 11, 2017): 3309–20. http://dx.doi.org/10.5194/bg-14-3309-2017.

Full text

Abstract:

Abstract. Crops are vital for human society. Crop yields vary with climate and it is important to understand how climate and crop yields are linked to ensure future food security. Temperature and precipitation are among the key driving factors of crop yield variability. Previous studies have investigated mostly linear relationships between temperature and precipitation and crop yield variability. Other research has highlighted the adverse impacts of climate extremes, such as drought and heat waves, on crop yields. Impacts are, however, often non-linearly related to multivariate climate conditions. Here we derive bivariate return periods of climate conditions as indicators for climate variability along different temperature–precipitation gradients. We show that in Europe, linear models based on bivariate return periods of specific climate conditions explain on average significantly more crop yield variability (42 %) than models relying directly on temperature and precipitation as predictors (36 %). Our results demonstrate that most often crop yields increase along a gradient from hot and dry to cold and wet conditions, with lower yields associated with hot and dry periods. The majority of crops are most sensitive to climate conditions in summer and to maximum temperatures. The use of bivariate return periods allows the integration of non-linear impacts into climate–crop yield analysis. This offers new avenues to study the link between climate and crop yield variability and suggests that they are possibly more strongly related than what is inferred from conventional linear models.

APA, Harvard, Vancouver, ISO, and other styles

35

Wimalasiri, Eranga M., Ebrahim Jahanshiri, Tengku Adhwa Syaherah Tengku Mohd Suhairi, Hasika Udayangani, Ranjith B. Mapa, Asha S. Karunaratne, Lal P. Vidhanarachchi, and Sayed N. Azam-Ali. "Basic Soil Data Requirements for Process-Based Crop Models as a Basis for Crop Diversification." Sustainability 12, no. 18 (September 21, 2020): 7781. http://dx.doi.org/10.3390/su12187781.

Full text

Abstract:

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 most sensitive soil parameters, a global sensitivity analysis was performed for all parameters across three datasets. Different soil parameters in both global datasets showed significantly (p < 0.05) lower and higher values than observed values. However, simulated rice yields using global data were significantly (p < 0.05) higher than from observed soil. Due to the relatively lower sensitivity to the yield, all parameters except soil texture and bulk density can still be supplied from global databases when observed data are not available. To facilitate the wider application of digital soil data for yield simulations, particularly for neglected and underutilised crops, nation-wide soil maps for 9 parameters up to 100 cm depth were generated and made available online.

APA, Harvard, Vancouver, ISO, and other styles

36

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.

Full text

Abstract:

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 50% of the selling price, and finally seeks out the optimal planting scheme for the two different scenarios. The practical application and verification of the model not only proves its feasibility and effectiveness in actual agricultural production, but also further highlights the far-reaching significance of the research results for promoting agricultural modernisation, enhancing agricultural production efficiency and promoting sustainable agricultural development.

APA, Harvard, Vancouver, ISO, and other styles

37

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.

Full text

Abstract:

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 sowings decreases even up to 0.74h-1. Winter ryetreatment with herbicide Grodil increases its yield up to 0.40 ha'1. Foreplants of barley according to their good influence on barley yield (descending): buckwheat, oats, winter lye. Barley yield in long-term monocultural sowings decreases for up to 1.17 t ha-1.Oats in crop rotation systems with cereal proportion up to 83% had very low yield amount alterations after different foreplants. Essential oat yield decreasement was noticed in perennial monocultural sowings. The best foreplants for spring wheat are buckwheat and lupine. The highest yield of buckwheat is get from monocultural sowings, but using potatoes as buckwheat foreplant gives essential yield decreasement. Distribution of perennial weeds, especially quickgrass, is 7,4 times more in crop rotation systems with high cereal proportion than in systems where also buckwheat and potatoes are cultivated. Treatment of herbicides and fungicides is more effective in monocultural sowings than in crop rotational systems, however increasement of crop yield after pesticide treatment is less remarkable than if we follow right crop rotation and choose optimal foreplants for each culture. Latest results from years 2002 to 2004 are shown in this article and are considered to be an addition to previous publications.

APA, Harvard, Vancouver, ISO, and other styles

38

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.

Full text

Abstract:

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 limited, mainly due to the lack of well-organized and digital datasets and lack of technological advancements. The aim of this study is to increase the accuracy of crop yield prediction in Ethiopia and provide information that can help farmers and policymakers improve crop productivity. In this study, a crop yield prediction model was developed based on historical data that includes factors such as crop type, rainfall, temperature, Area cultivated, production, and pesticides. Among the algorithms considered in this study, GradientBoostingRegressor achieved the highest value of the R-square – 90% compared to others which indicates its best predictive ability. However, the study also acknowledges the contextual advantages of other algorithms, highlighting the importance of selecting models that are appropriate for specific data sets and purposes. The accuracy and efficiency of agricultural planning and resource allocation in Ethiopia can be greatly improved by using machine learning techniques for crop production prediction.

APA, Harvard, Vancouver, ISO, and other styles

39

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

40

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

Full text

APA, Harvard, Vancouver, ISO, and other styles

41

Márton, L. "Fertilisation, rainfall and crop yield." Acta Agronomica Hungarica 52, no. 2 (August 1, 2004): 165–72. http://dx.doi.org/10.1556/aagr.52.2004.2.7.

Full text

Abstract:

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 0.3, humus 0.7%, total N 34 mg kg-1, ammonium lactate (AL)-soluble P2O5 43 mg kg-1, AL-K2O 60 mg kg-1 in the ploughed layer. From 1962 to 1980 the experiment consisted of 2×16×4×4=512 plots and from 1980 of 32×4=128 plots in split-split-plot and factorial random block designs. The gross plot size was 10×5=50 m2. The average fertiliser rates in kg ha-1 year-1 were nitrogen 45, phosphorus 24 (P2O5), potassium 40 (K2O), magnesium 7.5 (MgO) until 1980 and nitrogen 75, phosphorus 90 (P2O5), potassium 90 (K2O), magnesium 140 (MgCO3) after 1980. The main results and conclusions were as follows: The rainfall quantities averaged over many years and in the experimental years, and during the growing season, averaged over many years and in the experimental years, were 567, 497, 509, 452 mm for rye and 586, 509, 518 and 467 mm for winter wheat. Rainfall deviations from the many years' average -3% and -13% in the experimental years and during the growing season for potato and 2% and -3% for triticale. During the vegetation period the relationships between rainfall quantity, NPKCaMg nutrition and yield could be characterised primarily by quadratic correlations. Maximum yields of 4.0 t ha-1 for rye, 21.0 t ha-1 for potato, 3.4 t ha-1 for winter wheat and 5.0-6.0 t ha-1 for triticale were recorded when the natural rainfall amounted to 430-500, 280-330, 449-495 and 550-600 mm, respectively. At values above and below these figures there was a considerable reduction in the yield. The results showed that the crop yields were strongly influenced (quadratic correlation) by interactions between N, P, K, Ca and Mg fertilisation and rainfall quantity and distribution.

APA, Harvard, Vancouver, ISO, and other styles

42

Ben-Ari, Tamara, and David Makowski. "Decomposing global crop yield variability." Environmental Research Letters 9, no. 11 (November 1, 2014): 114011. http://dx.doi.org/10.1088/1748-9326/9/11/114011.

Full text

APA, Harvard, Vancouver, ISO, and other styles

43

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

Full text

APA, Harvard, Vancouver, ISO, and other styles

44

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

Full text

APA, Harvard, Vancouver, ISO, and other styles

45

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

Full text

APA, Harvard, Vancouver, ISO, and other styles

46

Li, Chengyun, Xiahong He, Shusheng Zhu, Huiping Zhou, Yunyue Wang, Yan Li, Jing Yang, et al. "Crop Diversity for Yield Increase." PLoS ONE 4, no. 11 (November 26, 2009): e8049. http://dx.doi.org/10.1371/journal.pone.0008049.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

Natr, L. "Crop Evolution, Adaptation and Yield." Photosynthetica 34, no. 1 (March 1, 1998): 56. http://dx.doi.org/10.1023/a:1006889901899.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

Gent, Martin P. N. "Crop evolution, adaptation and yield." Field Crops Research 55, no. 3 (February 1998): 283–84. http://dx.doi.org/10.1016/s0378-4290(97)00080-4.

Full text

APA, Harvard, Vancouver, ISO, and other styles

49

de Vries, G. E. "Ozone complicates crop yield projection." Trends in Plant Science 5, no. 7 (July 2000): 276. http://dx.doi.org/10.1016/s1360-1385(00)01703-9.

Full text

APA, Harvard, Vancouver, ISO, and other styles

50

Yengoh, Genesis T., and Jonas Ardö. "Crop Yield Gaps in Cameroon." AMBIO 43, no. 2 (August 8, 2013): 175–90. http://dx.doi.org/10.1007/s13280-013-0428-0.

Full text

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!