Carbon-13 Discrimination Can be Used to Evaluate Soybean Yield Variability

2003 ◽  
Vol 95 (2) ◽  
pp. 430 ◽  
Author(s):  
D. E. Clay ◽  
S. A. Clay ◽  
J. Jackson ◽  
K. Dalsted ◽  
C. Reese ◽  
...  
Keyword(s):  
Yield Variability ◽  
Soybean Yield

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.

Carbon-13 Discrimination Can be Used to Evaluate Soybean Yield Variability

2003 ◽  
Vol 95 (2) ◽  
pp. 430-435 ◽  
Author(s):  
D. E. Clay ◽  
S. A. Clay ◽  
J. Jackson ◽  
K. Dalsted ◽  
C. Reese ◽  
...  
Keyword(s):  
Yield Variability ◽  
Soybean Yield

Characterization of Soybean Yield Variability Using Crop Growth Models and 13C Discrimination

2003 ◽  
Author(s):  
Joel O. Paz ◽  
William D. Batchelor ◽  
David E. Clay ◽  
Sharon A. Clay ◽  
Cheryl Reese
Keyword(s):  
Growth Models ◽  
Crop Growth ◽  
Yield Variability ◽  
Soybean Yield ◽  
13C Discrimination ◽  
Crop Growth Models

scholarly journals Slash Spatial Linear Modeling: Soybean Yield Variability as a Function of Soil Chemical Properties

2018 ◽  
Vol 42 (0) ◽  
Author(s):  
Regiane Slongo Fagundes ◽  
Miguel Angel Uribe-Opazo ◽  
Luciana Pagliosa Carvalho Guedes ◽  
Manuel Galea
Keyword(s):  
Chemical Properties ◽  
Soil Chemical Properties ◽  
Yield Variability ◽  
Linear Modeling ◽  
Soybean Yield

scholarly journals Soil Fertility Level and Cropping Practices Determining Soybean Yield in Northern East and Center of Benin

2019 ◽  
pp. 1-10
Author(s):  
F. O. Chabi ◽  
G. D. Dagbenonbakin ◽  
C. E. Agbangba ◽  
B. Oussou ◽  
G. L. Amadji ◽  
...  
Keyword(s):  
Soil Fertility ◽  
Management Practices ◽  
Cropping System ◽  
Yield Variability ◽  
Soil Fertility Management ◽  
Soybean Yield ◽  
Ecological Zones ◽  
Fertility Management ◽  
Cropping Practices ◽  
Agro Ecological Zones

Soybean is a food security crop in Benin due to its high nutritional value but its yield in the farmers’ cropping system is very low. The present study aims to provide appropriate response to the yield variability among fields in two agro-ecological zones of Benin namely: Southern Borgou zone (AEZ 3 in the north) and cotton zone of central Benin (AEZ 5). Soil samples were collected from 0-20 cm depth in 120 fields (50 in the AEZ 3 and 70 in the AEZ 5). pH (water), soil organic carbon (Walkley and Black method), total nitrogen (Kjeldahl method), CEC (0.01 N ammonium acetate at pH 7 method) and available phosphorus (Bray 1) were determined in the laboratory of Soil Science Water and Environment (LSSEE) of the National Agricultural Research Institute of Benin (INRAB). Cropping system (crop rotations, soil fertility management practices) were also collected using an open ended questionnaire. Classification and regression trees (CARTs) models were used for data analyses. Soybean yield variability among the agro-ecological zones were registered and the highest yield recorded was less than 1 t.ha-1. Considering soil characteristics, soil organic matter level was the most important variable determining yield variability. Furthermore, quantities of P applied and farmyard manure were cropping practices inducing yield variability (86.4% and 15% of the variability respectively). Our results also show that, yield differences noticed among the agro-ecological zones were induced by CEC and pH (water). The study suggested promotion of integrated soil fertility management practices to sustain soybean yield in the study area.

RELATIONSHIP BETWEEN PLANT AVAI LABLE SOIL WATER AND YIELD FOR EXPLAINING SOYBEAN YIELD VARIABILITY

2002 ◽  
Vol 18 (4) ◽  
Author(s):  
A. Irmak ◽  
W. D. Batchelor ◽  
J. W. Jones ◽  
S. Irmak ◽  
J. O. Paz ◽  
...  
Keyword(s):  
Soil Water ◽  
Yield Variability ◽  
Soybean Yield

scholarly journals Impacts of hot-dry compound extremes on US soybean yields

2021 ◽  
Author(s):  
Raed Hamed ◽  
Anne F. Van Loon ◽  
Jeroen Aerts ◽  
Dim Coumou
Keyword(s):  
Soil Moisture ◽  
Crop Yields ◽  
Root Zone ◽  
Synergistic Effects ◽  
Weather Data ◽  
Yield Variability ◽  
Soybean Yield ◽  
Increased Risk ◽  
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 explore synergistic effects between drivers that can lead to severe impacts. The study combines weather data from reanalysis, satellite-based evapotranspiration and root-zone soil moisture with sub-national crop yields using statistical methods that account for interaction effects. Our model can explain on average about half of the year-to-year yield variability (60 % on all years and 40 % on 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 less so during wet conditions. Compound and interacting hot and dry August conditions (defined by the 95th and 5th percentiles of temperature and soil moisture, respectively) reduce yields by 1.25 standard deviation. This sensitivity is, respectively, 6 and 3 times larger than the sensitivity to hot or dry conditions alone. Other important drivers of negative yield responses are lower evapotranspiration early in the season and lower minimum temperature late in the season, both likely reflecting an increased risk of frost. The sensitivity to the identified drivers varies across the spatial domain with higher latitudes, and thus colder regions, being less sensitive to hot-dry August months. Historic trends in identified drivers indicates that US soybean has generally benefited from recent shifts in weather. Overall warming conditions have reduced the risk of frost in early and late-season and potentially allowed for earlier sowing dates. More importantly, summers have been getting cooler and wetter over eastern US. Still, despite these positive changes, we show that the frequency of compound hot-dry August month has remained unchanged over 1946–2016. Moreover, in the longer term, climate models project substantially warmer summers for the continental US which likely creates risks for soybean production.

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