scholarly journals A Satellite-Based Method for National Winter Wheat Yield Estimating in China

2021 ◽  
Vol 13 (22) ◽  
pp. 4680
Author(s):  
Yangyang Fu ◽  
Jianxi Huang ◽  
Yanjun Shen ◽  
Shaomin Liu ◽  
Yong Huang ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Harvest Index ◽  
Crop Production ◽  
Large Scale ◽  
Gross Primary Production ◽  
Wheat Yield ◽  
Model Performance ◽  
Production Model ◽  
Yield Data ◽  
The Mean

Satellite-based models have tremendous potential for monitoring crop production because satellite data can provide temporally and spatially continuous crop growth information at large scale. This study used a satellite-based vegetation production model (i.e., eddy covariance light use efficiency, EC-LUE) to estimate national winter wheat gross primary production, and then combined this model with the harvest index (ratio of aboveground biomass to yield) to convert the estimated winter wheat production to yield. Specifically, considering the spatial differences of the harvest index, we used a cross-validation method to invert the harvest index of winter wheat among counties, municipalities and provinces. Using the field-surveyed and statistical yield data, we evaluated the model performance, and found the model could explain more than 50% of the spatial variations of the yield both in field-surveyed regions and most administrative units. Overall, the mean absolute percentage errors of the yield are less than 20% in most counties, municipalities and provinces, and the mean absolute percentage errors for the production of winter wheat at the national scale is 4.06%. This study demonstrates that a satellite-based model is an alternative method for crop yield estimation on a larger scale.

Investigation of the two-decade environmental impact of large-scale agricultural cultivation and its impact on climate change in the Lajta-Project

2021 ◽  
Author(s):  
András Polgár ◽  
Karolina Horváth ◽  
Imre Mészáros ◽  
Adrienn Horváth ◽  
András Bidló ◽  
...  
Keyword(s):  
Climate Change ◽  
Life Cycle ◽  
Environmental Impact ◽  
Winter Wheat ◽  
Environmental Impacts ◽  
Crop Production ◽  
Large Scale ◽  
Winter Barley ◽  
Cultivated Plants ◽  
Agricultural Activity

<p>Crop production is applied on about half of Hungary’s land area, which amounts to approximately 4.5 million hectares. The agricultural activity has significant environmental impacts.</p><p>Our work aims the time series investigation of the impacts of large-scale agricultural cultivation<strong> </strong>on environment and primarily on climate change in<strong> </strong>the test area by applying environmental life cycle assessment (LCA) method.</p><p>The investigated area of Lajta Project can be found in the triangle formed by the settlements Mosonszolnok, Jánossomorja and Várbalog, in the north-western corner of Hungary, in Győr-Moson-Sopron county. The area has intense agri-environment characteristics, almost entirely lacking of grasslands and meadows.</p><p>We were looking for the answer to the question “To what extent does agricultural activity on this area impact the environment and how can it contribute to climate change during a given period?” The selection of the plants included in the analysis was justified by their significant growing area. We analysed the cultivation data of 5 crops: canola, winter barley, winter wheat, green maize and maize. Material flows of arable crop production technologies were defined in time series by the agricultural parcel register data. These covered the size of the area actually cultivated, the operational processes, records on seeds, fertilizer and pesticide use and harvest data by parcels. The examined environmental inventory database contained also the fuel consumption and lubricating oil usage of machine operations, and the water usage of chemical utilization.</p><p>In the life cycle modelling of cultivation, we examined 13 years of maize, 20 years of green maize, 20 years of winter barley, 18 years of winter wheat and 15 years of canola data calculated on 1 ha unit using GaBi life cycle analysis software.</p><p>In addition, we also calculated by an average cultivation model for all cultivated plants with reference data to 1 ha and 1 year period.</p><p>We applied methods and models in our life cycle impact assessment. According to the values of the impact categories, we set up the following increasing environmental ranking of plant cultivation: (1) canola has minimum environmental impacts followed by (2) green maize and (3) maize with slightly higher values, (4) winter barley has 6 times higher values preceded by (5) winter wheat with a slight difference. The previous environmental ranking of the specific cultivated plants’ contribution was also confirmed as regards the overall environmental impact: canola (1.0%) – green maize (4.9%) – maize (7.1%) – winter barley (43.1%) – winter wheat (44.0%).</p><p>Environmental impact category indicator results cumulated to total cultivation periods and total crop growing areas (quantitative approach) display the specific environmental footprints by crops. Increasing environmental ranking of environmental impacts resulted from cultivating the sample area is the following: (1) canola – (2) maize – (3) green maize – (4) winter barley – (5) winter wheat. The slight difference resulted in the rankings in quantitative approach according to the rankings of territorial approach on the investigated area is due to the diversity of cultivation time factor and the crop-growing parameter of the specific crops.</p><p>Acknowledgement: Our research was supported by the „Lajta-Project”.</p>

scholarly journals Impact of extreme weather conditions on European crop production in 2018

2020 ◽  
Vol 375 (1810) ◽  
pp. 20190510 ◽  
Author(s):  
Damien Beillouin ◽  
Bernhard Schauberger ◽  
Ana Bastos ◽  
Phillipe Ciais ◽  
David Makowski
Keyword(s):  
Machine Learning ◽  
Crop Production ◽  
Large Scale ◽  
Extreme Weather ◽  
Severe Drought ◽  
Yield Losses ◽  
Learning Models ◽  
Yield Data ◽  
Continental Scale ◽  
Machine Learning Models

Extreme weather increases the risk of large-scale crop failure. The mechanisms involved are complex and intertwined, hence undermining the identification of simple adaptation levers to help improve the resilience of agricultural production. Based on more than 82 000 yield data reported at the regional level in 17 European countries, we assess how climate affected the yields of nine crop species. Using machine learning models, we analyzed historical yield data since 1901 and then focus on 2018, which has experienced a multiplicity and a diversity of atypical extreme climatic conditions. Machine learning models explain up to 65% of historical yield anomalies. We find that both extremes in temperature and precipitation are associated with negative yield anomalies, but with varying impacts in different parts of Europe. In 2018, Northern and Eastern Europe experienced multiple and simultaneous crop failures—among the highest observed in recent decades. These yield losses were associated with extremely low rainfalls in combination with high temperatures between March and August 2018. However, the higher than usual yields recorded in Southern Europe—caused by favourable spring rainfall conditions—nearly offset the large decrease in Northern European crop production. Our results outline the importance of considering single and compound climate extremes to analyse the causes of yield losses in Europe. We found no clear upward or downward trend in the frequency of extreme yield losses for any of the considered crops between 1990 and 2018. This article is part of the theme issue ‘Impacts of the 2018 severe drought and heatwave in Europe: from site to continental scale'.

scholarly journals Climate-Induced Yield Losses for Winter Wheat in Henan Province, North China and Their Relationship with Circulation Anomalies

Water ◽  
2021 ◽  
Vol 13 (23) ◽  
pp. 3341
Author(s):  
Hui Zheng ◽  
Jin Huang ◽  
Jiadong Chen
Keyword(s):  
Winter Wheat ◽  
Large Scale ◽  
North China ◽  
Southern Oscillation ◽  
Assessment Method ◽  
Henan Province ◽  
Western Hemisphere ◽  
Yield Losses ◽  
Yield Data ◽  
Atmospheric Circulation Indices

Risk analysis using climate-induced yield losses (CIYL) extracted from long-term yield data have been recognized in China, but the research focusing on the time-series characteristics of risk and the circulation signals behind yield losses still remains incomplete. To address these challenges, a case study on winter wheat production in Henan province, north China was conducted by using annual series of yield in 17 cities during 1988–2017 and monthly series of 15 types of large-scale oceanic-atmospheric circulation indices (LOACI). A comprehensive risk assessment method was established by combining the intensity, frequency, and variability of CIYL and principal component analysis (PCA). The results showed that the westernmost Henan was identified as the area of higher-risk. PCA and Mann–Kendall trend tests indicated that the southern, northern, eastern, and western areas in Henan province were classified as having different annual CIYL variations in these four sub-regions; the decreasing trend of CIYL in northern area was the most notable. Since the 2000s, a significant decline in CIYL was found in each sub-region. It should be noted that the key LOACI, which includes Tropical Northern Atlantic Index (TNA), Western Hemisphere warm pool (WHWP), and Southern oscillation index (SOI), indicated significant CIYL anomalies in some months. Furthermore, the regional yield simulation results using linear regression for the independent variables of year and various LOACI were satisfactory, with the average relative error ranging from 3.48% to 6.87%.

scholarly journals Evaluations on the potential productivity of winter wheat based on agro-ecological zone in the world

2015 ◽  
Vol XL-7/W3 ◽  
pp. 115-119
Author(s):  
H. Wang ◽  
Q. Li ◽  
X. Du ◽  
L. Zhao ◽  
Y. Lu ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Western Europe ◽  
Evaluation Model ◽  
Wheat Yield ◽  
The United States ◽  
Ecological Zone ◽  
Yield Data ◽  
Potential Productivity ◽  
Realistic Potential ◽  
The World

Wheat is the most widely grown crop globally and an essential source of calories in human diets. Maintaining and increasing global wheat production is therefore strongly linked to food security. In this paper, the evaluation model of winter wheat potential productivity was proposed based on agro-ecological zone and the historical winter wheat yield data in recent 30 years (1983-2011) obtained from FAO. And the potential productions of winter wheat in the world were investigated. The results showed that the realistic potential productivity of winter wheat in Western Europe was highest and it was more than 7500 kg/hm2. The realistic potential productivity of winter wheat in North China Plain were also higher, which was about 6000 kg/hm2. However, the realistic potential productivity of winter wheat in the United States which is the main winter wheat producing country were not high, only about 3000 kg/hm2. In addition to these regions which were the main winter wheat producing areas, the realistic potential productivity in other regions of the world were very low and mainly less than 1500 kg/hm2, like in southwest region of Russia. The gaps between potential productivity and realistic productivity of winter wheat in Kazakhstan and India were biggest, and the percentages of the gap in realistic productivity of winter wheat in Kazakhstan and India were more than 40%. In Russia, the gap between potential productivity and realistic productivity of winter wheat was lowest and the percentage of the gap in realistic productivity of winter wheat in Russia was only 10%.

scholarly journals Simulation of Phosphorus Chemistry, Uptake and Utilisation by Winter Wheat

Plants ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 404 ◽  
Author(s):  
Lianhai Wu ◽  
Martin Blackwell ◽  
Sarah Dunham ◽  
Javier Hernández-Allica ◽  
Steve P. McGrath
Keyword(s):  
Winter Wheat ◽  
Crop Production ◽  
Model Performance ◽  
Soil P ◽  
P Cycling ◽  
P Accumulation ◽  
P Content ◽  
Performance Statistics ◽  
The Impact ◽  
P Supply

The phosphorus (P) supply from soils is crucial to crop production. Given the complexity involved in P-cycling, a model that can simulate the major P-cycling processes and link with other nutrients and environmental factors, e.g., soil temperature and moisture, would be a useful tool. The aim of this study was to describe a process-based P module added to the SPACSYS (Soil Plant and Atmosphere Continuum System) model and to evaluate its predictive capability on the dynamics of P content in crops and the impact of soil P status on crop growth. A P-cycling module was developed and linked to other modules included in the SPACSYS model. We used a winter wheat (Triticum aestivum, cv Xi-19) field experiment at Rothamsted Research in Harpenden to calibrate and validate the model. Model performance statistics show that the model simulated aboveground dry matter, P accumulation and soil moisture dynamics reasonably well. Simulated dynamics of soil nitrate and ammonium were close to the observed data when P fertiliser was applied. However, there are large discrepancies in fields without P fertiliser. This study demonstrated that the SPACSYS model was able to investigate the interactions between carbon, nitrogen, P and water in a single process-based model after the tested P module was implemented.

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.

DURUM WINTER WHEAT YIELD DEPENDING ON PREPARATIONS ACTIVITY FOR SEEDS AND PLANTS TREATMENT

1970 ◽  
pp. 63-66
Author(s):  
V. А. Fedotov ◽  
N. V. Podlesnykh ◽  
А. L. Lukin ◽  
L. М. Vlasova
Keyword(s):  
Winter Wheat ◽  
Durum Wheat ◽  
Seed Treatment ◽  
Crop Production ◽  
Foliar Application ◽  
Wheat Yield ◽  
Forest Steppe ◽  
Staff Members ◽  
Central Chernozem Region ◽  
Central Chernozem

In the conditions of the Central Russian Black Earth Region, it is quite possible to cultivate hard winter wheat for the needs of the cereal and macaroni industry. Winter-hardy varieties Zolotko, Donchanka and etc. are suitable. Presowing cultivation with Albit, Siberian fertility, Agat 25K, Micromax, etc. affects significantly on their yield. The combination of presowing seed treatment with foliar application of vegetative plants with the same and other products (Afbif, Reksalin, ABC and etc.) turned out to be particularly effective.  Synergism has been revealed in the combination of seed treatment with Albit, Rexolin ABC and Vitazim with spraying of plants with Albit solution, Abibif, Siberian Fertility, Fertigrain Foliar and other products. Winter durum wheat have not yet allowed to cultivation in Central Chernozem Region although, as our researches have shown many varieties (Zolotko, Donchanka, Amazonka, Kurant, Alyy parus, Aksinit, Terra ant other) can be suitable for cultivation in Voronezh region forest-steppe [1, 3, 4, 8], they are 10…15 centners per hectare more yield than spring durum wheat cultivated here. The deficit of durum wheat cereal in Central Chernozem Region, which is necessary for pasta industry is very large, it can be avoided by introducing and improving the cultivation technology of winter varieties of this crop. Staff members of the crop production, fodder production and agrotechnologies department of Voronezh SAU have identified (since 1996) and substantiated the cultivation possibility of domestic relatively winter resistance winter durum wheat in Central Chernozem Region (Amazonka, Donchanka, Zolotko, Kurant and other).

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.

Use of covariance structures for temporal errors in the analysis of a three-course wheat rotation and tillage trial

2004 ◽  
Vol 142 (2) ◽  
pp. 193-201 ◽  
Author(s):  
M. SINGH ◽  
M. PALA
Keyword(s):  
Crop Rotation ◽  
Crop Production ◽  
Production Systems ◽  
Wheat Yield ◽  
Covariance Structure ◽  
Cropping System ◽  
Information Criterion ◽  
Covariance Structures ◽  
Yield Data ◽  
Over Time

Crop rotation serves as a mechanism for developing sustainable crop production systems. Crop-rotation trials are used to identify agronomic input factors suitable for use in a cropping system. In crop-rotation trials, experimental errors within the same plot over time are correlated. The form of the covariance structure of the plot errors may be specific to the data from a rotation trial, but is unknown and is generally assumed. Statistical analyses are usually based on the assumption that plot errors are independent, or have constant covariance. An experiment was conducted using wheat-based, three-course rotations containing tillage treatment subplots over 12 years at ICARDA's experimental station at Tel Hadya, a moderately dry area in northern Syria. This study examined several covariance structures for temporal errors arising over the rotation plots and tillage subplots, in order to model wheat yield data. Eighteen covariance structures were examined, and the best pair was selected using the Akaike Information Criterion. The best pair comprised first-order autocorrelation and homogeneous variance for temporal errors in rotation plots, and uniform correlation with heterogeneous variances for temporal errors in tillage subplots. Using the 12 years of data obtained for wheat yield and the best pair of covariance structures, the tillage and rotation effects were found to be statistically significant and to have significant interactions with the cycle of rotation. The precision of the means calculated differed from those calculated using a control structure based on homogeneous error variances and constant correlation. The cumulative yield build-up over time differed significantly over the rotations and the tillage methods. An increasing yield trend was observed for the bread wheat rotation, while a yield decline was observed in durum wheat when the rotation was repeated. When evaluating the effects of input factors in crop rotations, we therefore recommend that the covariance structures be examined and that a suitably chosen structure be used.

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