scholarly journals Effects of fallow tillage on winter wheat yield and predictions under different precipitation types

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12602
Author(s):  
Yu Feng ◽  
Wen Lin ◽  
Shaobo Yu ◽  
Aixia Ren ◽  
Qiang Wang ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Meteorological Factors ◽  
Wheat Yield ◽  
Control Variable ◽  
Northern China ◽  
Small Error ◽  
Soil Water Storage ◽  
Growth Stages ◽  
Yield Prediction ◽  
Fallow Period

In northern China, precipitation that is primarily concentrated during the fallow period is insufficient for the growth stage, creates a moisture shortage, and leads to low, unstable yields. Yield prediction in the early growth stages significantly informs field management decisions for winter wheat (Triticum aestivum L.). A 10-year field experiment carried out in the Loess Plateau area tested how three tillage practices (deep ploughing (DP), subsoiling (SS), and no tillage (NT)) influenced cultivation and yield across different fallow periods. The experiment used the random forest (RF) algorithm to construct a prediction model of yields and yield components. Our results revealed that tillage during the fallow period was more effective than NT in improving yield in dryland wheat. Under drought condition, DP during the fallow period achieved a higher yield than SS, especially in drought years; DP was 16% higher than SS. RF was deemed fit for yield prediction across different precipitation years. An RF model was developed using meteorological factors for fixed variables and soil water storage after tillage during a fallow period for a control variable. Small error values existed in the prediction yield, spike number, and grains number per spike. Additionally, the relative error of crop yield under fallow tillage (5.24%) was smaller than that of NT (6.49%). The prediction error of relative meteorological yield was minimum and optimal, indicating that the model is suitable to explain the influence of meteorological factors on yield.

scholarly journals Use of Landsat images for yield evaluation within a small plot

2014 ◽  
Vol 60 (No. 11) ◽  
pp. 501-506 ◽  
Author(s):  
J. Kumhálová ◽  
F. Zemek ◽  
P. Novák ◽  
O. Brovkina ◽  
M. Mayerová
Keyword(s):  
Winter Wheat ◽  
Water Distribution ◽  
Vegetation Indices ◽  
Wheat Yield ◽  
Moisture Stress ◽  
Stress Index ◽  
Growth Stages ◽  
Landsat Images ◽  
Topographic Attributes ◽  
Small Plot

Many factors can influence crop yield. One of the most important factors is topography, which can play a crucial role especially in dry years. Plant variability can be monitored by many methods. This paper evaluates the suitability of vegetation indices derived from satellite Landsat 5 TM data in comparison with yield, curvature and topography wetness index over a relatively small field (11.5 ha). Imageries were chosen from the years 2006 and 2010, when oat was grown and from 2005 and 2011, when winter wheat was grown. These images were taken in June in the same growth stage for every crop. It was confirmed that derived indices from Landsat images can be used for comparison with yield and selected topographic attributes and it can explain yield variability, which can be influenced by water distribution during growth stages. Correlation coefficient between moisture stress index and winter wheat yield was –0.816 in the image acquisition date of 4. 6. 2011.

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.

Herbicides for Winter-hardy Wild Oat (Avena fatua) Control in Winter Wheat (Triticum aestivum)

1997 ◽  
Vol 11 (1) ◽  
pp. 35-38 ◽  
Author(s):  
Jeffrey A. Koscelny ◽  
Thomas F. Peeper
Keyword(s):  
Triticum Aestivum ◽  
Winter Wheat ◽  
Wheat Yield ◽  
Avena Fatua ◽  
Growth Stages ◽  
Wild Oat ◽  
Wheat Grain ◽  
Grain Yields

Diclofop at 840 g ai/ha, fenoxaprop at 90 g ai/ha, and imazamethabenz at 530 g ai/ha fall-applied controlled wild oat 96, 99, and 95% and increased wheat grain yields 26, 29, and 24%, respectively. These herbicides controlled wild oat over a wider range of growth stages than current labels indicate. The same treatments applied in March were less effective for wild oat control and did not increase wheat yield.

Effects of subsoil plastic film mulch on yield and water use of rainfed winter wheat

2018 ◽  
Vol 69 (12) ◽  
pp. 1197
Author(s):  
Zhang Mingming ◽  
Dong Baodi ◽  
Qiao Yunzhou ◽  
Yang Hong ◽  
Wang Yakai ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Grain Yield ◽  
Water Use Efficiency ◽  
Water Use ◽  
Crop Production ◽  
Wheat Yield ◽  
Limiting Factor ◽  
Growth Stages ◽  
Plastic Film ◽  
Use Efficiency

Water shortage is a limiting factor to crop production in North China. Mulching is a widely used approach to conserve soil water and improve crop yield. A 2-year field experiment was conducted at the Nanpi Eco-Agricultural Experimental Station of the Chinese Academy of Sciences in 2014–16, in which yields of winter wheat (Triticum aestivum L.) in a treatment with subsoil plastic film mulch were compared with non-mulch. The mulch treatment produced a 16.1% higher grain yield than the non-mulch treatment. The increase in grain yield was primarily due to a 10.1–10.9% increase in number of spikes per m2 and a 4.7–5.1% increase in number of grains per spike. Plants in the mulch treatment showed greater dry matter (DM) accumulation but similar harvest index. Yield improvement did not depend on increasing DM translocation, but was significantly related to DM accumulation at different growth stages. Increased DM accumulation before wintering, from jointing to heading and from anthesis to maturity, enhanced grain yield by promoting increased number of spikes and number of grains per spike. Soil evaporation was lower by 31.1% and transpiration increased by 28.0% in the mulch treatment, resulting in 8.9–9.4% higher water-use efficiency. Our results indicate that a subsoil plastic film mulch can effectively improve winter wheat yield and water-use efficiency under rain-fed conditions.

Variations of winter wheat growth stages under climate changes in northern China

2011 ◽  
Vol 35 (6) ◽  
pp. 623-631 ◽  
Author(s):  
Jian-Ying YANG ◽  
Xu-Rong MEI ◽  
Qin LIU ◽  
Chang-Rong YAN ◽  
Wen-Qing HE ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Climate Changes ◽  
Northern China ◽  
Growth Stages ◽  
Wheat Growth

scholarly journals Optimal Hyperspectral Characteristics Determination for Winter Wheat Yield Prediction

2018 ◽  
Vol 10 (12) ◽  
pp. 2015 ◽  
Author(s):  
Yao Zhang ◽  
Qiming Qin ◽  
Huazhong Ren ◽  
Yuanheng Sun ◽  
Minzan Li ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Spectral Characteristics ◽  
Wheat Yield ◽  
Crop Growth ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector ◽  
Yield Prediction ◽  
Final Yield ◽  
Yield Formation

Crop growth in different periods influences the final yield. This study started from the agronomic mechanism of yield formation and aimed to extract useful spectral characteristics in different phenological phases, which could directly describe the final yield and dynamic contributions of different phases to the yield formation. Hyperspectral information of the winter wheat canopy was acquired during three important phases (jointing stage, heading stage, and grain-filling stage). An enhanced 2D correlation spectral analysis method modified by mutual information was proposed to identify the sensitive wavebands. The selected wavebands performed well with good mechanism interpretation and close correlation with important crop growth parameters and main physiological activities related to yield formation. The quantitative contribution proportions of plant growth in three phases to the final yield were estimated by determining the coefficients of partial least square models based on full spectral information. They were then used as single-phase weight factors to merge the selected wavebands. The support vector machine model based on the weighted spectral dataset performed well in yield prediction with satisfactory accuracy and robustness. This result would provide rapid and accurate guidance for agricultural production and would be valuable for the processing of hyperspectral remote sensing data.

scholarly journals Identifying the Contributions of Multi-Source Data for Winter Wheat Yield Prediction in China

2020 ◽  
Vol 12 (5) ◽  
pp. 750 ◽  
Author(s):  
Juan Cao ◽  
Zhao Zhang ◽  
Fulu Tao ◽  
Liangliang Zhang ◽  
Yuchuan Luo ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Satellite Data ◽  
Large Scale ◽  
Wheat Yield ◽  
Yield Variability ◽  
Yield Prediction ◽  
Climate Data ◽  
Light Gradient ◽  
Source Data ◽  
Winter Wheat Yield

Wheat is a leading cereal grain throughout the world. Timely and reliable wheat yield prediction at a large scale is essential for the agricultural supply chain and global food security, especially in China as an important wheat producing and consuming country. The conventional approach using either climate or satellite data or both to build empirical and crop models has prevailed for decades. However, to what extent climate and satellite data can improve yield prediction is still unknown. In addition, socio-economic (SC) factors may also improve crop yield prediction, but their contributions need in-depth investigation, especially in regions with good irrigation conditions, sufficient fertilization, and pesticide application. Here, we performed the first attempt to predict wheat yield across China from 2001 to 2015 at the county-level by integrating multi-source data, including monthly climate data, satellite data (i.e., Vegetation indices (VIs)), and SC factors. The results show that incorporating all the datasets by using three machine learning methods (Ridge Regression (RR), Random Forest (RF), and Light Gradient Boosting (LightGBM)) can achieve the best performance in yield prediction (R2: 0.68~0.75), with the most individual contributions from climate (~0.53), followed by VIs (~0.45), and SC factors (~0.30). In addition, the combinations of VIs and climate data can capture inter-annual yield variability more effectively than other combinations (e.g., combinations of climate and SC, and combinations of VIs and SC), while combining SC with climate data can better capture spatial yield variability than others. Climate data can provide extra and unique information across the entire growing season, while the peak stage of VIs (Mar.~Apr.) do so. Furthermore, incorporating spatial information and soil proprieties into the benchmark models can improve wheat yield prediction by 0.06 and 0.12, respectively. The optimal wheat prediction can be achieved with approximately a two-month leading time before maturity. Our study develops timely and robust methods for winter wheat yield prediction at a large scale in China, which can be applied to other crops and regions.

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