Using Multispectral Imagery and Pixel Unmixing Techniques for Estimating Crop Yield Variability

2005 ◽  
Author(s):  
Chenghai Yang ◽  
James H. Everitt ◽  
Joe M. Bradford
Keyword(s):  
Crop Yield ◽  
Yield Variability ◽  
Multispectral Imagery

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.

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

The relationship between extreme weather and low crop yields

2020 ◽  
Author(s):  
Matias Heino ◽  
Weston Anderson ◽  
Michael Puma ◽  
Matti Kummu
Keyword(s):  
Soil Moisture ◽  
Crop Yield ◽  
Crop Yields ◽  
Explanatory Power ◽  
The United States ◽  
Extreme Weather ◽  
Extreme Weather Events ◽  
Weather Data ◽  
Yield Variability ◽  
Weather Events

<p>It is well known that climate extremes and variability have strong implications for crop productivity. Previous research has estimated that annual weather conditions explain a third of global crop yield variability, with explanatory power above 50% in several important crop producing regions. Further, compared to average conditions, extreme events contribute a major fraction of weather induced crop yield variations. Here we aim to analyse how extreme weather events are related to the likelihood of very low crop yields at the global scale. We investigate not only the impacts of heat and drought on crop yields but also excess soil moisture and abnormally cool temperatures, as these extremes can be detrimental to crops as well. In this study, we combine reanalysis weather data with national and sub-national crop production statistics and assess relationships using statistical copulas methods, which are especially suitable for analysing extremes. Further, because irrigation can decrease crop yield variability, we assess how the observed signals differ in irrigated and rainfed cropping systems. We also analyse whether the strength of the observed statistical relationships could be explained by socio-economic factors, such as GDP, social stability, and poverty rates. Our preliminary results indicate that extreme heat and cold as well as soil moisture abundance and excess have a noticeable effect on crop yields in many areas around the globe, including several global bread baskets such as the United States and Australia. This study will increase understanding of extreme weather-related implications on global food production, which is relevant also in the context of climate change, as the frequency of extreme weather events is likely to increase in many regions worldwide.</p>

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