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Impact of derived global weather data on simulated crop yields.

van Wart J, Grassini P, Cassman KG - Glob Chang Biol (2013)

Bottom Line: In contrast, simulated Yp or Yw using observed daily weather data from stations in the NOAA database combined with solar radiation from the NASA-POWER database were in much better agreement with Yp and Yw simulated with CWD (i.e. little bias and an RMSE of 12-19% of the absolute mean).We conclude that results from studies that rely on GWD to simulate agricultural productivity in current and future climates are highly uncertain.An alternative approach would impose a climate scenario on location-specific observed daily weather databases combined with an appropriate upscaling method.

View Article: PubMed Central - PubMed

Affiliation: Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, 68583-0915, USA.

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Four panel figure comparing reported weather data from control and GWDs during pre- (black triangles) and post-silking (red circles) for maize (a), and pre- and post-anthesis in wheat (b).
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fig05: Four panel figure comparing reported weather data from control and GWDs during pre- (black triangles) and post-silking (red circles) for maize (a), and pre- and post-anthesis in wheat (b).

Mentions: Stepwise multiple regression helped assess the causes of underlying bias in estimates of Yp and Yw using simulations with GWD, especially for rainfed maize and wheat, which are grown in regions with relatively uniform topography in the USA and Germany respectively (see Table S5 for a summary of GWD and NOAA-SR weather data biases). The range in average annual precipitation, however, differs markedly among CWD sites in Germany (500 and 850 mm) the USA (450–900 mm) and rainfall does not replace evapotranspiration in much of the western Corn Belt where the CWD sites are located (Grassini et al., 2009). As a result, estimated water deficit and solar radiation had a large influence on discrepancies between Yw estimated by GWDs and CWD (Table 2). In general, the sign of coefficients in Table 2 are indicative of the relationship between that variable and yield. For instance, a positive sign for water deficit indicates that as this variable increases (less precipitation and more ETo) so do the deviations in simulated yields with a GWD as compared with simulations using the CWD. The closer GWD and NOAA-SR based simulated yields were to CWD based simulations (i.e. low RMSE and ME as shown in Figs 2–4), the poorer the explanatory power of the final regression model. For example, the water deficit calculated over the USA simulated maize growing season was 76% and 86% smaller for simulations based on CRU (data not shown) and NASA data, respectively, compared with those based on CWD (Fig.5a). Similarly, water deficit was 31% larger with NCEP maize simulations than with CWD, especially during the post-silking phase in which water deficit was 43% larger than the CWD (see Figs S1–S12).


Impact of derived global weather data on simulated crop yields.

van Wart J, Grassini P, Cassman KG - Glob Chang Biol (2013)

Four panel figure comparing reported weather data from control and GWDs during pre- (black triangles) and post-silking (red circles) for maize (a), and pre- and post-anthesis in wheat (b).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4288967&req=5

fig05: Four panel figure comparing reported weather data from control and GWDs during pre- (black triangles) and post-silking (red circles) for maize (a), and pre- and post-anthesis in wheat (b).
Mentions: Stepwise multiple regression helped assess the causes of underlying bias in estimates of Yp and Yw using simulations with GWD, especially for rainfed maize and wheat, which are grown in regions with relatively uniform topography in the USA and Germany respectively (see Table S5 for a summary of GWD and NOAA-SR weather data biases). The range in average annual precipitation, however, differs markedly among CWD sites in Germany (500 and 850 mm) the USA (450–900 mm) and rainfall does not replace evapotranspiration in much of the western Corn Belt where the CWD sites are located (Grassini et al., 2009). As a result, estimated water deficit and solar radiation had a large influence on discrepancies between Yw estimated by GWDs and CWD (Table 2). In general, the sign of coefficients in Table 2 are indicative of the relationship between that variable and yield. For instance, a positive sign for water deficit indicates that as this variable increases (less precipitation and more ETo) so do the deviations in simulated yields with a GWD as compared with simulations using the CWD. The closer GWD and NOAA-SR based simulated yields were to CWD based simulations (i.e. low RMSE and ME as shown in Figs 2–4), the poorer the explanatory power of the final regression model. For example, the water deficit calculated over the USA simulated maize growing season was 76% and 86% smaller for simulations based on CRU (data not shown) and NASA data, respectively, compared with those based on CWD (Fig.5a). Similarly, water deficit was 31% larger with NCEP maize simulations than with CWD, especially during the post-silking phase in which water deficit was 43% larger than the CWD (see Figs S1–S12).

Bottom Line: In contrast, simulated Yp or Yw using observed daily weather data from stations in the NOAA database combined with solar radiation from the NASA-POWER database were in much better agreement with Yp and Yw simulated with CWD (i.e. little bias and an RMSE of 12-19% of the absolute mean).We conclude that results from studies that rely on GWD to simulate agricultural productivity in current and future climates are highly uncertain.An alternative approach would impose a climate scenario on location-specific observed daily weather databases combined with an appropriate upscaling method.

View Article: PubMed Central - PubMed

Affiliation: Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, 68583-0915, USA.

Show MeSH