Predicting spatial patterns of within-field crop yield variability

2018 ◽  
Vol 219 ◽  
pp. 106-112 ◽  
Author(s):  
Bernardo Maestrini ◽  
Bruno Basso
Keyword(s):  
Crop Yield ◽  
Spatial Patterns ◽  
Yield Variability ◽  
Field Crop

scholarly journals Early season prediction of within-field crop yield variability by assimilating CubeSat data into a crop model

2022 ◽  
Vol 313 ◽  
pp. 108736
Author(s):  
Matteo G. Ziliani ◽  
Muhammad U. Altaf ◽  
Bruno Aragon ◽  
Rasmus Houburg ◽  
Trenton E. Franz ◽  
...  
Keyword(s):  
Crop Yield ◽  
Crop Model ◽  
Yield Variability ◽  
Field Crop ◽  
Early Season

scholarly journals Substitution of Inorganic Nitrogen Fertilizer with Green Manure (GM) Increased Yield Stability by Improving C Input and Nitrogen Recovery Efficiency in Rice Based Cropping System

Agronomy ◽  
2019 ◽  
Vol 9 (10) ◽  
pp. 609 ◽  
Author(s):  
Qaswar ◽  
Jing ◽  
Ahmed ◽  
Shujun ◽  
Dongchu ◽  
...  
Keyword(s):  
Crop Yield ◽  
Green Manure ◽  
Inorganic Nitrogen ◽  
Cropping System ◽  
N Fertilization ◽  
Yield Stability ◽  
N Fertilizer ◽  
Yield Variability ◽  
Variability Index ◽  
Double Rice

A long-term field experiment was carried out (since 2008) for evaluating the effects of different substitution rates of inorganic nitrogen (N) fertilizer by green manure (GM) on yield stability and N balance under double rice cropping system. Treatments included, (1) N0 (no N fertilizer and no green manure); (2) N100 (recommended rate of N fertilizer and no green manure); (3) N100-M (recommended rate of N fertilizer and green manure); (4) N80-M (80% of recommended N fertilizer and green manure); (5) N60-M (60% of recommended N fertilizer and green manure); and (6) M (green manure without N fertilization). Results showed that, among all treatments, annual crop yield under N80-M treatment was highest. Crop yield did not show significant differences between N100-M and N80-M treatments. Substitution of different N fertilizer rates by GM reduced the yield variability index. Compared to the N0 treatment, yield variability index of early rice under N100-M, N80-M, and N60-M treatments was decreased by 11%, 26%, and 36%, respectively. Compared to the N0 treatment, yield variability index of late rice was decreased by 12%, 38%, 49%, 47%, and 24% under the N100, N100-M, N80-M, N60-M, and M treatments, respectively. During period of 2009–2013 and 2014–2018, nitrogen recovery efficiency (NRE) was highest under N80-M treatment and N balance was highest under N100 treatment. NRE of all treatments with GM was increased over the time from 2009–2013 to 2014–2018. All treatments with GM showed increasing trend of SOC over the years. Substitution of N fertilizer by GM also increased C inputs and soil C:N ratio compared to the N100 and N0 treatments. Boosted regression model indicated that C input, N uptake and AN were most influencing factors of crop yield. Thus, we concluded that N fertilization rates should be reduced by 20% under GM rotation to attain high yield stability of double rice cropping system through increasing NRE and C inputs.

Analysing and simulating spatial patterns of crop yield in Guizhou Province based on artificial neural networks

2020 ◽  
pp. 030913332095663 ◽  
Author(s):  
Boyi Liang ◽  
Hongyan Liu ◽  
Timothy A Quine ◽  
Xiaoqiu Chen ◽  
Paul D Hallett ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Crop Yield ◽  
Spatial Patterns ◽  
Crop Production ◽  
Guizhou Province ◽  
Crop Species ◽  
Environmental Controls ◽  
Artificial Neural ◽  
Propagation Network

The area of karst terrain in China covers 3.63×106 km2, with more than 40% in the southwestern region over the Guizhou Plateau. Karst comprises exposed carbonate bedrock over approximately 1.30×106 km2 of this area, which suffers from soil degradation and poor crop yield. This paper aims to gain a better understanding of the environmental controls on crop yield in order to enable more sustainable use of natural resources for food production and development. More precisely, four kinds of artificial neural network were used to analyse and simulate the spatial patterns of crop yield for seven crop species grown in Guizhou Province, exploring the relationships with meteorological, soil, irrigation and fertilization factors. The results of spatial classification showed that most regions of high-level crop yield per area and total crop yield are located in the central-north area of Guizhou. Moreover, the three artificial neural networks used to simulate the spatial patterns of crop yield all demonstrated a good correlation coefficient between simulated and true yield. However, the Back Propagation network had the best performance based on both accuracy and runtime. Among the 13 influencing factors investigated, temperature (16.4%), radiation (15.3%), soil moisture (13.5%), fertilization of N (13.5%) and P (12.4%) had the largest contribution to crop yield spatial distribution. These results suggest that neural networks have potential application in identifying environmental controls on crop yield and in modelling spatial patterns of crop yield, which could enable local stakeholders to realize sustainable development and crop production goals.

scholarly journals Decomposing global crop yield variability

2014 ◽  
Vol 9 (11) ◽  
pp. 114011 ◽  
Author(s):  
Tamara Ben-Ari ◽  
David Makowski
Keyword(s):  
Crop Yield ◽  
Yield Variability

The effects of scale on crop yield variability

2003 ◽  
Vol 78 (3) ◽  
pp. 425-434 ◽  
Author(s):  
T. Górski ◽  
K. Górska
Keyword(s):  
Crop Yield ◽  
Yield Variability

scholarly journals A multi-model analysis of teleconnected crop yield variability in a range of cropping systems

2019 ◽  
Author(s):  
Matias Heino ◽  
Joseph H. A. Guillaume ◽  
Christoph Müller ◽  
Toshichika Iizumi ◽  
Matti Kummu
Keyword(s):  
Crop Yield ◽  
Crop Production ◽  
Food System ◽  
Crop Yields ◽  
Southern Oscillation ◽  
Yield Variability ◽  
Management Scenarios ◽  
Yield Data ◽  
Climate Oscillations ◽  
Crop Models

Abstract. Climate oscillations are periodically fluctuating oceanic and atmospheric phenomena, which are related to variations in weather patterns and crop yields worldwide. In terms of crop production, the most widespread impacts have been observed for the El Niño Southern Oscillation (ENSO), which has been found to impact crop yields in all continents that produce crops, while two other climate oscillations – the Indian Ocean Dipole (IOD) and the North Atlantic Oscillation (NAO) – have been shown to impact crop production especially in Australia and Europe, respectively. In this study, we analyse the impacts of ENSO, IOD and NAO on the growing conditions of maize, rice, soybean and wheat at the global scale, by utilizing crop yield data from an ensemble of global gridded crop models simulated for a range of crop management scenarios. Our results show that simulated crop yield variability is correlated to climate oscillations to a wide extent (up to almost half of all maize and wheat harvested areas for ENSO) and in several important crop producing areas, e.g. in North America (ENSO, wheat), Australia (IOD & ENSO, wheat) and northern South America (ENSO, soybean). Further, our analyses show that higher sensitivity to these oscillations can be observed for rainfed, and fully fertilized scenarios, while the sensitivity tends to be lower if crops are fully irrigated. Since, the development of ENSO, IOD and NAO can be reliably forecasted in advance, a better understanding about the relationship between crop production and these climate oscillations can improve the resilience of the global food system to climate related shocks.

Crop Yield Variability as Influenced by Water in Rain-Fed Agriculture

2015 ◽  
pp. 453-465 ◽  
Author(s):  
S. Logsdon ◽  
J. Pruger ◽  
D. Meek ◽  
T. Colvin ◽  
D. James ◽  
...  
Keyword(s):  
Crop Yield ◽  
Yield Variability

scholarly journals Understanding the weather signal in national crop-yield variability

2017 ◽  
Vol 5 (6) ◽  
pp. 605-616 ◽  
Author(s):  
Katja Frieler ◽  
Bernhard Schauberger ◽  
Almut Arneth ◽  
Juraj Balkovič ◽  
James Chryssanthacopoulos ◽  
...  
Keyword(s):  
Crop Yield ◽  
Yield Variability
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