scholarly journals Quantifying the spatial variation in the potential productivity and yield gap of winter wheat in China

2017 ◽  
Vol 16 (4) ◽  
pp. 845-857 ◽  
Author(s):  
Shi-yuan ZHANG ◽  
Xiao-hu ZHANG ◽  
Xiao-lei QIU ◽  
Liang TANG ◽  
Yan ZHU ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Spatial Variation ◽  
Yield Gap ◽  
Potential Productivity

scholarly journals Selection of Appropriate Spatial Resolution for the Meteorological Data for Regional Winter Wheat Potential Productivity Simulation in China Based on WheatGrow Model

Agronomy ◽  
2018 ◽  
Vol 8 (10) ◽  
pp. 198 ◽  
Author(s):  
Xiaohu Zhang ◽  
Hao Xu ◽  
Li Jiang ◽  
Jianqing Zhao ◽  
Wenjun Zuo ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Spatial Heterogeneity ◽  
Spatial Variation ◽  
Spatial Resolution ◽  
Scale Effect ◽  
Meteorological Data ◽  
Small Range ◽  
Potential Productivity ◽  
Selection Of ◽  
Effect Index

The crop model based on physiology and ecology has been widely applied to the simulation of regional potential productivity. By determining the appropriate spatial resolution of meteorological data required for model simulation for different regions, we can reduce the difficulty of acquiring model input data, thereby improving the regional computing efficiency of the model and increasing the model applications. In this study, we investigated the appropriate spatial resolution of meteorological data needed for the regional potential productivity simulation of the WheatGrow model by scale effect index and verify the feasibility of using the landform to obtain the appropriate spatial resolution of meteorological data required by the potential productivity simulation for the winter wheat region of China. The research results indicated that the spatial variation of landforms in the winter wheat region of China is significantly correlated to the spatial variation of multi-year meteorological data. Based on the scale effect index, we can obtain a spatial distribution of appropriate spatial resolution for the meteorological data required for the regional potential productivity simulation of the WheatGrow model for the winter wheat region of China. Moreover, although we can use the spatial heterogeneity of landforms to guide the selection of appropriate spatial resolution for the meteorological data, in the regions where the spatial heterogeneity of the landform is relatively weak or relatively strong over a small range, the method of using a single heterogeneity index derived from semi-variogram cannot well reflect the scale effect of simulation results and needs further improvement.

scholarly journals How much cattle can brazil pastures support? An analysis based on “YIELD GAP”

2017 ◽  
Vol 8 (4) ◽  
pp. 162-169
Author(s):  
Christiane Cavalcante Leite ◽  
Britaldo Silveira Soares Filho ◽  
Marcos Heil Costa ◽  
Ranieri Carlos Ferreira de Amorim
Keyword(s):  
Spatial Variation ◽  
Agricultural Intensification ◽  
Animal Production ◽  
Yield Gap ◽  
Potential Yield ◽  
Biophysical Factors ◽  
Spatial Datasets ◽  
Degraded Pasture ◽  
Different Climates

The degradation of pastures is one of Brazil's biggest problems today and directly affects the sustainability of livestock. The animal production in a degraded pasture can be six times smaller than a grazing or recovered in good maintenance state. So we can consider that productivity could be increased in pasture areas, and analyze how productivity is limited by biophysical factors (climate, for example) versus management. Using spatial datasets, we compare yield patterns for the pasturelands within regions of similar climate. We use this comparison to evaluate the potential yield obtainable for pasturelands in different climates around the Brazil using the limits of Brazilian biomes. We then compare the actual yields currently being achieved with their ‘potential yield’ to estimate the ‘yield gap’, present spatial datasets of both the potential yields and yield gap patterns for pasturelands around the year 1995 and 2006. This study is intended to be an important new resource for scientists and policymakers alike, helping to more accurately understand spatial variation of yield and agricultural intensification potential, as well as employing these data to better utilize existing infrastructure and optimize the distribution of development and aid capital.

scholarly journals Plant Population and Fungicide Economically Reduced Winter Wheat Yield Gap in Kansas

2019 ◽  
Vol 111 (2) ◽  
pp. 650-665 ◽  
Author(s):  
Brent R. Jaenisch ◽  
Amanda de Oliveira Silva ◽  
Erick DeWolf ◽  
Dorivar A. Ruiz-Diaz ◽  
Romulo P. Lollato
Keyword(s):  
Winter Wheat ◽  
Wheat Yield ◽  
Plant Population ◽  
Yield Gap ◽  
Winter Wheat Yield

Multi-scale assessment of winter wheat yield gaps with an integrated evaluation framework in the Huang-Huai-Hai farming region in China

2019 ◽  
Vol 157 (6) ◽  
pp. 523-536
Author(s):  
S. Li ◽  
J. Liu ◽  
M. Shang ◽  
H. Jia ◽  
Y. Feng ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Wheat Yield ◽  
Evaluation Framework ◽  
High Yield ◽  
Limiting Factor ◽  
Yield Gap ◽  
Multi Scale ◽  
Winter Wheat Yield ◽  
Yield Gaps ◽  
Attainable Yield

AbstractQuantifying reasonable crop yield gaps and determining potential regions for yield improvement can facilitate regional plant structure adjustment and promote crop production. The current study attempted to evaluate the yield gap in a region at multi-scales through model simulation and farmer investigation. Taking the winter wheat yield gap in the Huang-Huai-Hai farming region (HFR) for the case study, 241 farmers’ fields in four typical high-yield demonstration areas were surveyed to determine the yield limitation index and attainable yield. In addition, the theoretical and realizable yield gap of winter wheat in 386 counties of the HFR was assessed. Results showed that the average field yield of the demonstration plots was 8282 kg/ha, accounting for 0.72 of the potential yield, which represented the highest production in the region. The HFR consists of seven sub-regions designated 2.1–2.7: the largest attainable yield gap existed in the 2.6 sub-region, in the southwest of the HFR, while the smallest was in the 2.2 sub-region, in the northwest of the HFR. With a high irrigated area rate, the yield gap in the 2.2 sub-region could hardly be reduced by increasing irrigation, while a lack of irrigation remained an important limiting factor for narrowing the yield gap in 2.3 sub-region, in the middle of the HFR. Therefore, a multi-scale yield gap evaluation framework integrated with typical field survey and crop model analysis could provide valuable information for narrowing the yield gap.

scholarly journals Temporal Dynamics and Spatial Variation of Azoxystrobin and Propiconazole Resistance in Zymoseptoria tritici: A Hierarchical Survey of Commercial Winter Wheat Fields in the Willamette Valley, Oregon

2017 ◽  
Vol 107 (3) ◽  
pp. 345-352 ◽  
Author(s):  
Christina H. Hagerty ◽  
Nicole P. Anderson ◽  
Christopher C. Mundt
Keyword(s):  
Winter Wheat ◽  
Spatial Variation ◽  
Fungicide Resistance ◽  
Temporal Dynamics ◽  
Septoria Tritici Blotch ◽  
Septoria Tritici ◽  
Willamette Valley ◽  
Zymoseptoria Tritici ◽  
Quinone Outside Inhibitor ◽  
Inhibitor Resistance

Fungicide resistance can cause disease control failure in agricultural systems, and is particularly concerning with Zymoseptoria tritici, the causal agent of Septoria tritici blotch of wheat. In North America, the first quinone outside inhibitor resistance in Z. tritici was discovered in the Willamette Valley of Oregon in 2012, which prompted this hierarchical survey of commercial winter wheat fields to monitor azoxystrobin- and propiconazole-resistant Z. tritici. Surveys were conducted in June 2014, January 2015, May 2015, and January 2016. The survey was organized in a hierarchical scheme: regions within the Willamette Valley, fields within the region, transects within the field, and samples within the transect. Overall, frequency of azoxystrobin-resistant isolates increased from 63 to 93% from June 2014 to January 2016. Resistance to azoxystrobin increased over time even within fields receiving no strobilurin applications. Propiconazole sensitivity varied over the course of the study but, overall, did not significantly change. Sensitivity to both fungicides showed no regional aggregation within the Willamette Valley. Greater than 80% of spatial variation in fungicide sensitivity was at the smallest hierarchical scale (within the transect) of the survey for both fungicides, and the resistance phenotypes were randomly distributed within sampled fields. Results suggest a need for a better understanding of the dynamics of fungicide resistance at the landscape level.

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%.

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