Justin van Wart

Impact of derived global weather data on simulated crop yields.

Crop simulation models can be used to estimate impact of current and future climates on crop yields and food security, but require long-term historical daily weather data to obtain robust simulations. In many regions where crops are grown, daily weather data are not available. Alternatively, gridded weather databases (GWD) with complete terrestrial coverage are available, typically derived from: (i) global circulation computer models; (ii) interpolated weather station data; or (iii) remotely sensed surface data from satellites. The present studys objective is to evaluate capacity of GWDs to simulate crop yield potential (Yp) or water-limited yield potential (Yw), which can serve as benchmarks to assess impact of climate change scenarios on crop productivity and land use change. Three GWDs (CRU, NCEP/DOE, and NASA POWER data) were evaluated for their ability to simulate Yp and Yw of rice in China, USA maize, and wheat in Germany. Simulations of Yp and Yw based on recorded daily data from well-maintained weather stations were taken as the control weather data (CWD). Agreement between simulations of Yp or Yw based on CWD and those based on GWD was poor with the latter having strong bias and large root mean square errors (RMSEs) that were 26–72% of absolute mean yield across locations and years. 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.

Evaluating Cultivars for Organic Farming: Maize, Soybean, and Wheat Genotype by System Interactions in Eastern Nebraska

A common hypothesis among farmers is that there are unique crop traits necessary to achieve high yields in organic systems not currently expressed in conventionally developed cultivars. To test this hypothesis, high-yielding cultivars of three annual crops developed in conventional systems were grown in parallel organic and conventional cropping systems over multiple years. Yield results revealed no consistent genotype by system interactions. We conclude that with conventionally developed cultivars it is logical to choose the highest yielding genotypes based on nonbiased yield comparison trials in conventional systems unless results are available from organic evaluations.

Kenya public weather processed by the Global Yield Gap Atlas project

The Global Yield Gap Atlas project (GYGA - http://yieldgap.org ) has undertaken a yield gap assessment following the protocol recommended by van Ittersum et. al. (van Ittersum et. al., 2013). One part of the activities consists of collecting and processing weather data as an input for crop simulation models in sub-Saharan African countries including Kenya. This publication covers weather data for 10 locations in Kenya. The project looked for good quality weather data in areas where crops are pre-dominantly grown. As locations with good public weather data are sparse in Africa, the project developed a method to generate weather data from a combination of observed and other external weather data. One locations holds actually measured weather data, the other 9 locations show propagated weather data. The propagated weather data consist on TRMM rain data (or NASA POWER if TRMM is not available) and NASA POWER Tmax, Tmin, and Tdew data corrected based on calibrations with short-term (<10 years) observed weather data. sources (Van Wart et.al. 2015).