Ethan E. Butler

Random Forests for Global and Regional Crop Yield Predictions

Accurate predictions of crop yield are critical for developing effective agricultural and food policies at the regional and global scales. We evaluated a machine-learning method, Random Forests (RF), for its ability to predict crop yield responses to climate and biophysical variables at global and regional scales in wheat, maize, and potato in comparison with multiple linear regressions (MLR) serving as a benchmark. We used crop yield data from various sources and regions for model training and testing: 1) gridded global wheat grain yield, 2) maize grain yield from US counties over thirty years, and 3) potato tuber and maize silage yield from the northeastern seaboard region. RF was found highly capable of predicting crop yields and outperformed MLR benchmarks in all performance statistics that were compared. For example, the root mean square errors (RMSE) ranged between 6 and 14% of the average observed yield with RF models in all test cases whereas these values ranged from 14% to 49% for MLR models. Our results show that RF is an effective and versatile machine-learning method for crop yield predictions at regional and global scales for its high accuracy and precision, ease of use, and utility in data analysis. RF may result in a loss of accuracy when predicting the extreme ends or responses beyond the boundaries of the training data.

Variations in the sensitivity of US maize yield to extreme temperatures by region and growth phase

Maize yield is sensitive to high temperatures, and most large scale analyses have used a single, fixed sensitivity to represent this vulnerability over the course of a growing season. Field scale studies, in contrast, highlight how temperature sensitivity varies over the course of development. Here we couple United States Department of Agriculture yield and development data from 1981–2012 with weather station data to resolve temperature sensitivity according to both region and growth interval. On average, temperature sensitivity peaks during silking and grain filling, but there are major regional variations. In Northern states grain filling phases are shorter when temperatures are higher, whereas Southern states show little yield sensitivity and have longer grain filling phases during hotter seasons. This pattern of grain filling sensitivity and duration accords with the whole-season temperature sensitivity in US maize identified in recent studies. Further exploration of grain filling duration and its response to high temperatures may be useful in determining the degree to which maize agriculture can be adapted to a hotter climate.

Comment on “Agriculture facilitated permanent human occupation of the Tibetan Plateau after 3600 B.P. ”

Chen et al. (Reports, 16 January 2015, p. 248) argued that early Tibetan agriculturalists pushed the limits of farming up to 4000 meters above sea level. We contend that this argument is incompatible with the growing requirements of barley. It is necessary to clearly define past crop niches to create better models for the complex history of the occupation of the plateau.

Mapping local and global variability in plant trait distributions

Significance Currently, Earth system models (ESMs) represent variation in plant life through the presence of a small set of plant functional types (PFTs), each of which accounts for hundreds or thousands of species across thousands of vegetated grid cells on land. By expanding plant traits from a single mean value per PFT to a full distribution per PFT that varies among grid cells, the trait variation present in nature is restored and may be propagated to estimates of ecosystem processes. Indeed, critical ecosystem processes tend to depend on the full trait distribution, which therefore needs to be represented accurately. These maps reintroduce substantial local variation and will allow for a more accurate representation of the land surface in ESMs. Our ability to understand and predict the response of ecosystems to a changing environment depends on quantifying vegetation functional diversity. However, representing this diversity at the global scale is challenging. Typically, in Earth system models, characterization of plant diversity has been limited to grouping related species into plant functional types (PFTs), with all trait variation in a PFT collapsed into a single mean value that is applied globally. Using the largest global plant trait database and state of the art Bayesian modeling, we created fine-grained global maps of plant trait distributions that can be applied to Earth system models. Focusing on a set of plant traits closely coupled to photosynthesis and foliar respiration—specific leaf area (SLA) and dry mass-based concentrations of leaf nitrogen (Nm) and phosphorus (Pm), we characterize how traits vary within and among over 50,000 ∼50×50-km cells across the entire vegetated land surface. We do this in several ways—without defining the PFT of each grid cell and using 4 or 14 PFTs; each model’s predictions are evaluated against out-of-sample data. This endeavor advances prior trait mapping by generating global maps that preserve variability across scales by using modern Bayesian spatial statistical modeling in combination with a database over three times larger than that in previous analyses. Our maps reveal that the most diverse grid cells possess trait variability close to the range of global PFT means.

Global Relationships between Cropland Intensification and Summer Temperature Extremes over the Last 50 Years

AbstractConversion of native ecosystems to cropland and the use of irrigation are considered dominant pathways through which agricultural land-use change alters regional climate. Recent research proposes that increases in cropland productivity, or intensification, also influences climate through increasing evapotranspiration. Increases in evapotranspiration are expected to have the greatest temperature influence on extremely hot summer days with high vapor pressure deficits. Here, the generalizability and importance of such relationships are assessed by examining historical land-use and climate trends in seven regions across the globe, each containing a major temperate or subtropical cropping area. Trends in summer high-temperature extremes are sequentially compared against trends in cropland area, area equipped for irrigation, precipitation, and summer cropping intensity. Trends in temperature extremes are estimated using quantile regression of weather station observations, and land-use data are from agr...