Abstract
The role of weather in crop production at field is central to understanding the impact of climate change on agriculture and its implications for food security. In this study, we developed a bioeconomic modeling framework for estimating the field effect of weather on crop production at the regional scale with unobserved producer behavior. We took a systematic perspective for model development, explicitly considering crop production as a coupled human–nature system dominated by management adapted to local environmental and economic conditions. We drew on economics to characterize producer management behavior and crop yield consistent with the process of field production. We integrated scientific findings on plant growth and production economics to parameterize the yield function of crop that can be statistically estimated with observed data. An empirical application of our approach to spring wheat production found spatially heterogeneous effect of weather and climate change impact. Our modeling approach can be applied to different crops or regions to develop locally specific understandings of the management adjusted, production effect of weather and climate change impact, with implications on cropping system resilience and for adaptation.
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Notes
Note that the observed production effect represents the result of not only the biological process of crop growth in relation to external biophysical conditions but also unobserved production management and adjustment by local producers. It is not the same as the biological response of crops to biophysical factors under experimental/controlled conditions.
Agricultural production in North Dakota, US, is mainly rain-fed. According to the 2007 Census of Agriculture, the irrigated land accounted for only 0.6 % of farmland in North Dakota (USDA NASS 2009a).
In the production function, we omitted fixed input such as machinery and land for annual crop production decision (See Debertin 2012). This is appropriate particularly for the type of yield data examined in our empirical analysis that are average yield per unit land at the county level, a spatial scale for which the total amount of machinery and land in a year is likely fixed.
Note that the location-specific production function is intended to describe different production management across space by different sets of parameters.
In this study, we attempt to identify the local effect of weather using location-specific production (and yield) functions, which may be mixed with and disguised by the effect of production management that in general mitigates negative effects and maintains or enhances positive effects of natural biophysical conditions—an important characteristic of crop production as a managed ecosystem. With a location-specific production function, we do not and never intent to model a biological relationship between crop yield and generic growth conditions that is identical for a specific crop across space.
In this study, we do not model producer decision on land use and climate change adaptation, which is not the focus of the study. Rather, we examine the effect on observed yield of weather in relation to production factors for an observed crop of interest.
In this study, we did not specify an upper limit of threshold temperature (for spring wheat production) as we could not find a commonly accepted empirical estimate. Nonetheless, the adopted dynamic crop growth model (4), which has a quadratic structure on the growth effect of temperature, should be able to account for the effect of too high or too low temperatures harmful to crop growth.
The Ricardian notion on the relationship between land use and land quality suggests that land of good quality will be used first. Based on the notion, the Eq. (9) would be \({\varvec{\upgamma}}(A) = \int_{{\underline{{{\varvec{\upiota}}(A)}} }}^{{\overline{{\varvec{\upiota}}} }} {{\varvec{\upiota}}a({\varvec{\upiota}},A)\,{\text{d}}{\varvec{\upiota}}}\). Consequently, the Eq. (10) would be \(\frac{{{\text{d}}{\varvec{\upgamma}}(A)}}{{{\text{d}}A}} = \int_{{\underline{{{\varvec{\upiota}}(A)}} }}^{{\overline{{\varvec{\upiota}}} }} {{\varvec{\upiota}}\frac{{{\text{d}}a({\varvec{\upiota}},A)}}{{{\text{d}}A}}\,{\text{d}}{\varvec{\upiota}}} - \underline{{{\varvec{\upiota}}(A)}} a[\underline{{{\varvec{\upiota}}(A)}} ,A]\frac{{{\text{d}}\underline{{{\varvec{\upiota}}(A)}} }}{{{\text{d}}A}}\), which should be less than zero.
It is worth noting that acreage is used here as an indicator of land quality, and should not be considered and interpreted as farm level land use decision. Otherwise, it would be inconsistent with production economics theory.
The criteria for the delineation of CRDs are unclear, we assume that biophysical conditions and agricultural production are relatively homogeneous within each CRD but may vary across CRDs.
The starting and ending dates of growing season are based on reported usual planting and harvesting dates for ND from USDA NASS (1997). Fixed dates are used for each year due to lack of historical data.
It is worth noting that acreage can correlate with other factors (e.g., difficulty in management) leading to decreasing return to scale, which would also imply a negative yield effect. Nonetheless, the estimated yield function and the effect of weather are still valid.
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Appendix
Appendix
The appendix Table 2 presents the 90 % confidence intervals for the estimated yield change with projected climate change.
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Jiang, Y., Koo, W.W. Estimating the local effect of weather on field crop production with unobserved producer behavior: a bioeconomic modeling framework. Environ Econ Policy Stud 16, 279–302 (2014). https://doi.org/10.1007/s10018-014-0079-9
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DOI: https://doi.org/10.1007/s10018-014-0079-9