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

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 Impacts of compound hot–dry extremes on US soybean yields

2021 ◽  
Vol 12 (4) ◽  
pp. 1371-1391
Author(s):  
Raed Hamed ◽  
Anne F. Van Loon ◽  
Jeroen Aerts ◽  
Dim Coumou
Keyword(s):  
Soil Moisture ◽  
Synergistic Effects ◽  
Weather Data ◽  
Yield Variability ◽  
Critical Element ◽  
Early Season ◽  
Soybean Yield ◽  
Late Season ◽  
The Us ◽  
Dry Conditions

Abstract. The US agriculture system supplies more than one-third of globally traded soybean, and with 90 % of US soybean produced under rainfed agriculture, soybean trade is particularly sensitive to weather and climate variability. Average growing season climate conditions can explain about one-third of US soybean yield variability. Additionally, crops can be sensitive to specific short-term weather extremes, occurring in isolation or compounding at key moments throughout crop development. Here, we identify the dominant within-season climate drivers that can explain soybean yield variability in the US, and we explore the synergistic effects between drivers that can lead to severe impacts. The study combines weather data from reanalysis and satellite-informed root zone soil moisture fields with subnational crop yields using statistical methods that account for interaction effects. On average, our models can explain about two-thirds of the year-to-year yield variability (70 % for all years and 60 % for out-of-sample predictions). The largest negative influence on soybean yields is driven by high temperature and low soil moisture during the summer crop reproductive period. Moreover, due to synergistic effects, heat is considerably more damaging to soybean crops during dry conditions and is less problematic during wet conditions. Compounding and interacting hot and dry (hot–dry) summer conditions (defined by the 95th and 5th percentiles of temperature and soil moisture respectively) reduce yields by 2 standard deviations. This sensitivity is 4 and 3 times larger than the sensitivity to hot or dry conditions alone respectively. Other relevant drivers of negative yield responses are lower temperatures early and late in the season, excessive precipitation in the early season, and dry conditions in the late season. We note that the sensitivity to the identified drivers varies across the spatial domain. Higher latitudes, and thus colder regions, are positively affected by high temperatures during the summer period. On the other hand, warmer southeastern regions are positively affected by low temperatures during the late season. Historic trends in identified drivers indicate that US soybean production has generally benefited from recent shifts in weather except for increasing rainfall in the early season. Overall, warming conditions have reduced the risk of frost in the early and late seasons and have potentially allowed for earlier sowing dates. More importantly, summers have been getting cooler and wetter over the eastern US. Nevertheless, despite these positive changes, we show that the frequency of compound hot–dry summer events has remained unchanged over the 1946–2016 period. In the longer term, climate models project substantially warmer summers for the continental US, although uncertainty remains as to whether this will be accompanied by drier conditions. This highlights a critical element to explore in future studies focused on US agricultural production risk under climate change.

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.

Round-leaved Mallow (Malva pusilla) Interference in Spring Wheat (Triticum aestivum) and Lentil (Lens culinaris) in Saskatchewan

Weed Science ◽  
1995 ◽  
Vol 43 (3) ◽  
pp. 381-388 ◽  
Author(s):  
Roberte M. D. Makowski
Keyword(s):  
Crop Yield ◽  
Spring Wheat ◽  
Lens Culinaris ◽  
Yield Loss ◽  
Yield Losses ◽  
Variable Model ◽  
Early Season ◽  
Crop Density ◽  
Density And Biomass ◽  
Tiller Density

The competitive ability of annual round-leaved mallow was determined in spring wheat and lentil at Indian Head and Regina, Saskatchewan, in 1985 and 1986 using paired quadrats. Significant biomass and seed yield loss occurred in three of four tests in lentil and two of three tests in spring wheat. Differences in numbers of wheat tillers produced between weedy and weed-free plots were found in three of four tests. A two-variable model comprised of early season crop density loss and round-leaved mallow biomass best accounted for the majority of variation in crop yield loss for both lentil and wheat, and tiller density loss in wheat. In 1985 at Indian Head, where no yield loss occurred for either wheat or lentil, round-leaved mallow had been seeded immediately before the crop. Greater yield losses occurred at Regina, in the presence of an older, well-established infestation. In the years and locations with the greatest crop yield losses, round-leaved mallow emerged before the crop causing poor crop emergence. At Regina in 1986, crop yield losses were more than 60% in wheat and 90 to 100% in lentil because of large differences in crop density between weed-free and weedy subplots. Round-leaved mallow exhibited great variability in growth, producing more biomass per plant, more capsules per plant, and more capsules per gram of biomass in the less competitive crop, lentil, than in wheat. Density and biomass of round-leaved mallow were not correlated; with a density of 200 plants m−2, round-leaved mallow biomass in wheat ranged from 100 to 500 g m−2; while in lentil, from 200 to as high as 1000 g m−2, approximately double the range found in wheat. The type of round-leaved mallow infestation (newly seeded or well-established) and environmental conditions (mainly early season precipitation) account for differences between sites and between years.

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
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