Climatic impacts on winter wheat in Oklahoma and potential applications to climatic and crop yield prediction

2007 ◽  
Vol 52 (2) ◽  
pp. 117-126 ◽  
Author(s):  
J. Scott Greene ◽  
Erin Maxwell
Keyword(s):  
Winter Wheat ◽  
Crop Yield ◽  
Yield Prediction ◽  
Potential Applications ◽  
Climatic Impacts

scholarly journals Winter Wheat Yield Prediction Using Convolutional Neural Networks from Environmental and Phenological Data

2021 ◽  
Author(s):  
Amit Kumar Srivast ◽  
Nima Safaei ◽  
Saeed Khaki ◽  
Gina Lopez ◽  
Wenzhi Zeng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Winter Wheat ◽  
Crop Yield ◽  
Wheat Yield ◽  
Yield Prediction ◽  
Learning Models ◽  
Winter Wheat Yield ◽  
Machine Learning Models

Abstract Crop yield forecasting depends on many interactive factors including crop genotype, weather, soil, and management practices. This study analyzes the performance of machine learning and deep learning methods for winter wheat yield prediction using extensive datasets of weather, soil, and crop phenology. We propose a convolutional neural network (CNN) which uses the 1-dimentional convolution operation to capture the time dependencies of environmental variables. The proposed CNN, evaluated along with other machine learning models for winter wheat yield prediction in Germany, outperformed all other models tested. To address the seasonality, weekly features were used that explicitly take soil moisture and meteorological events into account. Our results indicated that nonlinear models such as deep learning models and XGboost are more effective in finding the functional relationship between the crop yield and input data compared to linear models and deep neural networks had a higher prediction accuracy than XGboost. One of the main limitations of machine learning models is their black box property. Therefore, we moved beyond prediction and performed feature selection, as it provides key results towards explaining yield prediction (variable importance by time). As such, our study indicates which variables have the most significant effect on winter wheat yield.

scholarly journals Prediction of Winter Wheat Yield Based on Multi-Source Data and Machine Learning in China

2020 ◽  
Vol 12 (2) ◽  
pp. 236 ◽  
Author(s):  
Jichong Han ◽  
Zhao Zhang ◽  
Juan Cao ◽  
Yuchuan Luo ◽  
Liangliang Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Winter Wheat ◽  
Crop Yield ◽  
Wheat Yield ◽  
Growth Period ◽  
Yield Prediction ◽  
Climate Data ◽  
Source Data ◽  
Winter Wheat Yield

Wheat is one of the main crops in China, and crop yield prediction is important for regional trade and national food security. There are increasing concerns with respect to how to integrate multi-source data and employ machine learning techniques to establish a simple, timely, and accurate crop yield prediction model at an administrative unit. Many previous studies were mainly focused on the whole crop growth period through expensive manual surveys, remote sensing, or climate data. However, the effect of selecting different time window on yield prediction was still unknown. Thus, we separated the whole growth period into four time windows and assessed their corresponding predictive ability by taking the major winter wheat production regions of China as an example in the study. Firstly we developed a modeling framework to integrate climate data, remote sensing data and soil data to predict winter wheat yield based on the Google Earth Engine (GEE) platform. The results show that the models can accurately predict yield 1~2 months before the harvesting dates at the county level in China with an R2 > 0.75 and yield error less than 10%. Support vector machine (SVM), Gaussian process regression (GPR), and random forest (RF) represent the top three best methods for predicting yields among the eight typical machine learning models tested in this study. In addition, we also found that different agricultural zones and temporal training settings affect prediction accuracy. The three models perform better as more winter wheat growing season information becomes available. Our findings highlight a potentially powerful tool to predict yield using multiple-source data and machine learning in other regions and for crops.

Analysis of Crop Yield Prediction of Kharif & Rabi Jowar Crops Using Data Mining Techniques

2017 ◽  
Vol 7 (11) ◽  
pp. 79
Author(s):  
Sujata Mulik
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Crop Production ◽  
Climatic Factors ◽  
Crop Productivity ◽  
Yield Prediction ◽  
Data Mining Techniques ◽  
Agriculture Sector ◽  
Using Data ◽  
Rabi Crops

Agriculture sector in India is facing rigorous problem to maximize crop productivity. More than 60 percent of the crop still depends on climatic factors like rainfall, temperature, humidity. This paper discusses the use of various Data Mining applications in agriculture sector. Data Mining is used to solve various problems in agriculture sector. It can be used it to solve yield prediction.  The problem of yield prediction is a major problem that remains to be solved based on available data. Data mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. In this paper we have focused on predicting crop yield productivity of kharif & Rabi Crops. 

scholarly journals Effects of disk tillage on soil condition, crop yield and weed infestation

2011 ◽  
Vol 48 (No. 1) ◽  
pp. 20-26
Author(s):  
M. Birkás ◽  
T. Szalai ◽  
C. Gyuricza ◽  
M. Gecse ◽  
K. Bordás
Keyword(s):  
Winter Wheat ◽  
Crop Yield ◽  
Field Experiments ◽  
Soil Layer ◽  
Soil Condition ◽  
Perennial Weeds ◽  
Weed Infestation ◽  
Set Up ◽  
Different Levels

This research was instigated by the fact that during the last decade annually repeated shallow disk tillage on the same field became frequent practice in Hungary. In order to study the changes of soil condition associated with disk tillage and to assess it is consequences, long-term tillage field experiments with different levels of nutrients were set up in 1991 (A) and in 1994 (B) on Chromic Luvisol at Gödöllö. The effects of disk tillage (D) and disk tillage combined with loosening (LD) on soil condition, on yield of maize and winter wheat, and on weed infestation were examined. The evaluation of soil condition measured by cone index and bulk density indicated that use of disking annually resulted in a dense soil layer below the disking depth (diskpan-compaction). It was found, that soil condition deteriorated by diskpan-compaction decreased the yield of maize significantly by 20 and 42% (w/w), and that of wheat by 13 and 15% (w/w) when compared to soils with no diskpan-compaction. Averaged over seven years, and three fertilizer levels, the cover % of the total, grass and perennial weeds on loosened soils were 73, 69 and 65% of soils contained diskpan-compaction.

Crop Yield Prediction Using Deep Neural Network

2020 ◽  
Author(s):  
Fatin Farhan Haque ◽  
Ahmed Abdelgawad ◽  
Venkata Prasanth Yanambaka ◽  
Kumar Yelamarthi
Keyword(s):  
Neural Network ◽  
Crop Yield ◽  
Deep Neural Network ◽  
Yield Prediction

Analysis of agricultural crop yield prediction using statistical techniques of machine learning

2021 ◽  
Author(s):  
Janmejay Pant ◽  
R.P. Pant ◽  
Manoj Kumar Singh ◽  
Devesh Pratap Singh ◽  
Himanshu Pant
Keyword(s):  
Machine Learning ◽  
Crop Yield ◽  
Statistical Techniques ◽  
Yield Prediction ◽  
Agricultural Crop

Perennial Vine Control in Multiple Cropping Systems on a Clay Soil

1989 ◽  
Vol 3 (2) ◽  
pp. 282-287 ◽  
Author(s):  
C. Dennis Elmore ◽  
Larry G. Heatherly ◽  
Richard A. Wesley
Keyword(s):  
Winter Wheat ◽  
Crop Yield ◽  
Clay Soil ◽  
Cropping Systems ◽  
Abundant Species ◽  
Corn Yield ◽  
Multiple Cropping ◽  
Established Population

An area of a shrink-swell clay soil (Tunica clay, Vertic Haplaquept) with an established population of redvine, trumpetcreeper, honeyvine milkweed, redberry moonseed, and maypop passionflower was treated with dicamba once in the fall of 1983. The effect on perennial vines was determined for the following 4 yr in three rotational cropping systems involving winter wheat, soybean, corn, and sorghum, all with and without irrigation. Dicamba reduced the population of perennial vines 80% over 4 yr. Redvine and trumpetcreeper, the first and second most abundant species, were reduced by over 83 and 76%, respectively. Yield of soybean increased 17% in 1985 and 1987 while corn yield increased 9% in 1986 with dicamba use. In 1984 no effects on crop yield were measured. This inconsistent crop yield reponse after dicamba treatment, even though perennial vines were suppressed, must be considered in evaluating the economics of using dicamba for perennial vine control.

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