Corn Yield Behavior: Effects of Technological Advance and Weather-Conditions

Garcia, Philip; Offutt, Susan E.; Pinar, Musa; Changnon, Stanley A.

doi:10.1175/1520-0450(1987)026<1092:cybeot>2.0.co;2

Cited by 23 publications

(17 citation statements)

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“…Reasons for the lack of more complete agreement with the previous work may include differences in the spatial scale considered in the present and previous studies (plot‐level vs. state‐level or larger scale), difficulty in total separation of the influence of improving technology and its interactions with climate in the yield series of the previous studies, and other factors that influence yields that are not accounted for in the models, such as the influence of weeds, insects, and plant diseases. These results are in agreement with previous work of Thompson (1986) and Garcia et al (1987), who both found increases in weather variability relative to observed crop yields in neighboring production areas of the Corn Belt region during recent decades.…”

Section: Resultssupporting

confidence: 93%

“…Because of significant upward trends in the measured yields during the 1961–1990 period due to improvements in technology and other factors (e.g., Thompson, 1969; Garcia et al, 1987), the simulated and measured yield series were not directly comparable, because the simulated series were derived assuming constant levels of technology. As a result, the measured county‐level yield time series data were first statistically detrended with linear or nonlinear (simple polynomial equations) regressions using the TableCurve 3D software package (SPSS, 1997).…”

Section: Methodsmentioning

confidence: 99%

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Weather Impacts on Maize, Soybean, and Alfalfa Production in the Great Lakes Region, 1895–1996

et al. 2001

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spring freezes (Carter, 1995;Kunkel and Hollinger, 1995), and excessive precipitation and flooding during Weather and climate have had major influences on crop production the growing season (Kunkel et al., 1994). in the Upper Great Lakes states of Michigan, Minnesota, and Wiscon-Analyses of the impact of weather and climate on sin during the past century. However, isolation of the impact of weather is made difficult by the confounding effects of technological agriculture for extended time periods have been freimprovements in agriculture, which have resulted in significant grain quently constrained by the lack of quality long-term yield increases. The objective of this study was to identify climatologiclimatological data series and the limited number of cal impacts involved with the production of three crops commonly experimental treatment combinations available from grown in the region-alfalfa (Medicago sativa L.), maize (Zea mays field experiments. In addition, it is difficult to isolate L.), and soybean [Glycine max (L.) Merr.]-without the influence of the impact of weather due to the confounding effects technology, and trends of relevant agroclimatological variables during of technological improvements in agriculture (e.g., imthe period 1895-1996. The models DAFOSYM, CERES-Maize, and proved varieties, increased rates of fertilization), which SOYGRO models were used to simulate crop growth, development, have resulted in significant yield increases during the and yield of the three crops, respectively. Regionally, low precipitation past century (Thompson, 1986). An alternative strategy and moisture stress were chief limitations to simulated crop yields. is the use of crop simulation models that are based on Simulated maize and soybean yield series were found to increase with time an average of 11.4 kg ha Ϫ1 yr Ϫ1 and 4.9 kg ha Ϫ1 yr Ϫ1 , respectively, the underlying physiological processes governing plant across the study sites during the study period. These increases were growth and development. Such models, if properly caliassociated with average study period increases in total seasonal precipbrated and tested, allow a user to easily investigate the itation of 0.4 mm yr Ϫ1 and decreased total seasonal potential evapoeffects of individual input variables by holding all others transpiration of 0.2 mm yr Ϫ1 . No consistent trends were found for constant and provide a more convenient, less expensive alfalfa. The simulated yield results support previous research identitool than long-term field research in the evaluation of fying a period of benign climate, which favored crop production in crop response to environmental and management facthe region from 1954 to 1973, and was preceded and followed by tors (Angus, 1991). Crop simulation models can also be periods of relatively greater yield variability.used to investigate multiple factors and their interactions at various hierarchical levels, including farm, regional, and national scales.

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Section: Resultssupporting

confidence: 93%

Section: Methodsmentioning

confidence: 99%

Weather Impacts on Maize, Soybean, and Alfalfa Production in the Great Lakes Region, 1895–1996

et al. 2001

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“…A reduced yield increase in the late 1970s and early 1980s was evidenced by Menz and Pardey (1983), but they did not conclude the probability of approaching a yield plateau at that time due to anticipation of more exploitation of genetic diversity and the unpredictable effects of emerging biotechnologies. Similarly, Garcia et al (1987) acknowledged the declining rate of yield increase and suggested that a plateau might ultimately be reached if no technology breakthrough occurred.…”

Section: Resultsmentioning

confidence: 99%

Dryland and Irrigated Corn Yield with Climate, Management, and Hybrid Changes from 1939 through 2009

et al. 2012

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Corn (Zea mays L.) yield has increased from about 1.5 Mg ha−1 in the early 1900s to 8.5 Mg ha−1 in the beginning of the 2000s in the United States. Information about yield and management changes in irrigated and dryland corn yields for the hybrid era is scarce. The objective of the present study was to determine the magnitude of yield and management changes in irrigated and dryland corn from 1939 through 2009. Data from selected irrigated and dryland corn performance trials conducted in Kansas from 1939 through 2009 were analyzed. On average, corn yields have increased at rate of 90 kg ha−1 yr−1 in dryland and 120 kg ha−1 yr−1 in irrigated trials. Corn yield changes from one decade to another were not similar for the seven decades considered. Both irrigated and dryland yields increased significantly at least every two decades until the last three, during which dryland yields stagnated. Changes in hybrid technology and changes in crop management factors, such as a decrease in planting and harvesting date by about a quarter of a day yr−1, increased planting density at the rate of 597 plants ha−1 yr−1, and increased N and P fertilizer rates by 2.6 and 0.40 kg ha−1 yr−1, respectively, were found for the same time period in dryland corn. In addition, climate changes contributed to yield increases in the past through increased total rainfall, average monthly minimum and maximum temperature in March, and decreased maximum temperature from July through September.

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“…Agricultural yield data typically have an upward low-frequency trend because of technological improvements in crop genetics and management techniques (Hall et al, 1992). However, the increased variability of yields could be either a function of the heightened sensitivity of technology to weather, or to temporal increases in weather variability (Garcia et al, 1987). Many authors in the development of their studies have removed the trend of the yield of several crops, amongst them the soybean, attributing it mainly to technological development (Huff and Neill, 1982;Magrin et al, 1998;Podestá et al, 1999;Alexandrov and Hoogenboom, 2001;Hurtado et al, 2001).…”

Section: Agricultural Informationmentioning

confidence: 99%

The impact of climate variability on soybean yields in Argentina. Multivariate regression

Penalba

Bettolli

Vargas

2007

Meteorological Applications

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Climate variability is examined and discussed in this work, emphasizing its influence over the fluctuation of soybean yield in the Pampas (central-eastern Argentina). Monthly data of rainfall, maximum and minimum temperatures, thermal range and seasonal rainfall were analysed jointly with the soybean yield in the period 1973-2000. Low-frequency variability was significant only in the minimum temperature during November in almost all the stations. This situation is favourable to the crop since during this month, seed germination, a growth stage sensitive to low temperatures, takes place. In the crop's core production region, 72% of the series of soybean yield presented a positive trend. Except in years with extreme rainfall situations, interannual variability of the soybean yield is in phase with the seasonal rainfall interannual variability. During these years, losses in the soybean crop occurred, with yield negative anomalies greater than one standard deviation. Soybean yield showed spatial coherence at the local scale, except in the crop's core zone. The association between each climate variable and yield did not show a defined regional pattern. Summer high temperature and rainfall excesses during the period of maturity and harvest have the greatest negative impact on the crop, whilst higher minimum temperatures during the growing season favour high yields. The joint effect of climate variables over yield was studied with multivariate statistical models, assuming that the effect of other factors (such as soil, technology, pests) is contained in the residuals. The regression models represent the estimates of the yield satisfactorily (high percentage of explained variance) and can be used to assess expected anomalies of mean soybean yield for a particular year. However, the predictor variables of the yield depend on the region.

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Corn Yield Behavior: Effects of Technological Advance and Weather-Conditions

Cited by 23 publications

References 0 publications

Weather Impacts on Maize, Soybean, and Alfalfa Production in the Great Lakes Region, 1895–1996

Weather Impacts on Maize, Soybean, and Alfalfa Production in the Great Lakes Region, 1895–1996

Dryland and Irrigated Corn Yield with Climate, Management, and Hybrid Changes from 1939 through 2009

The impact of climate variability on soybean yields in Argentina. Multivariate regression

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