Random Forests for global and regional crop yield predictions Performance evaluation and application for climate impact studies

Transcription

1 Random Forests for global and regional crop yield predictions Performance evaluation and application for climate impact studies Soo-Hyung Kim, Ph.D. Associate Professor School of Environmental and Forest Sciences College of the Environment University of Washington

2 Acknowledgements Jig Han Jeong, Jonathan P. Resop, Nathaniel D. Mueller, David H. Fleisher, Kyungdahm Yun, Ethan E. Butler, Dennis J. Timlin, Kyo-Moon Shim, James S. Gerber, and Vangimalla. R. Reddy Cooperative Research Program for Agricultural Science and Technology Development (Project No. PJ ), Rural Development Administration, Republic of Korea USDA-ARS Specific Cooperative Agreement: , USA

3 Overview What is Random Forests (RF)? Performance of RF for global and regional crop yield predictions RF applications for a climate change study: A wheat example in North America

4 Random Forests (RF) Leo Breiman, UC Berkeley Statistical / machine learning method Classification and Regression Trees (CART) Capable of classification and regression Predictions based on voting or averaging of randomized forests (thus, Random Forests) consisting of many trees through bagging Widely used for species distribution modeling in ecology (Classification) Cutler et al Random forests for classification in ecology. Ecology, 88: Climate envelopes Rarely used for crop yield predictions in agriculture (Regression)

5 RF classification performance Lawler JJ et al Predicting climateinduced range shifts: model differences and model reliability. Global Change Biology, 12: All of the differences among models would be daunting were it not for the finding that one modeling approach clearly performed better than all of the alternatives. In particular, random forest models had the highest median performance scores across all four measures of model accuracy (Table 1), and were consistently ranked the best performing of the six model types (Fig. 4).

6 GLM TREE GAM Correct absence Commission error Correct presence Omission error RF ANN GARP Black Tufted-ear Marmoset * Lawler et al. (2006)

7 Overview What is Random Forests (RF)? Performance of RF for global and regional crop yield predictions Regression RF applications for a climate change study: A wheat example in North America

8 RF for crop yield Jeong JH, Resop JP, Mueller ND, Fleisher DH, Yun K, Butler EE, Timlin DJ, Shim K-M, Gerber JS, Reddy VR, Kim S-H Random forests for global and regional crop yield predictions. PLoS ONE, 11: e Comparing RF with MLR Global wheat yield US maize yield over 30 years Northeast Seaboard Region (NESR) potato and silage maize yield randomforest package in R and Arc GIS

9 RF Performance

10 RF vs. Multiple Linear Regression (MLR) Model Efficiency EF =1- N å i=1 N å (y i - ŷ i ) 2 ( y i - y) 2 i=1 d =1- N å Willmott s d i=1 å N (y i - ŷ i ) 2 i=1 ( ŷ i - y + y i - y ) 2

11 Input variables and their importance rank

12 RF partial dependence plots for key variables

13 Overview What is Random Forests (RF)? Performance of RF for global and regional crop yield predictions Regression RF applications for a climate change study: A wheat example in North America

14 Wheat Megaenvironments: ME6 (red) Spring wheat Minimum coldest quarter temperture < -13 C Minimum warmest quarter temperature > 9 C Spring sown, high latitude (>45 N or S), photosensitive varieties Delineates the northern limits of current wheat growing regions

15 Observed and Predicted ME6 wheat yield using RF Actual yield N. America (predicted mean yield: 2.31 ton/ha) Ton/ha RF Predicted yield

16 Effects of non-climatic variables Q: How important are the effects of non-climatic variables on yield when wheat growing areas shift northward with climate change? RF performs well with mixed variables Method Categorical (i.e., soil order) Continuous Identify new ME6 climate envelope by 2050 based on SRES A2 of MRI-CGCM Climate only model + Soil order or + Photoperiod sensitivity (as longest day-length) Full model Climate+ Soil + Photoperiod

17 Projected ME6 envelope in N. America by 2050 with SRES A2

18 Predicted yield maps Climate only model (1.99 ton/ha) Climate + Soil model (1.98 ton/ha) Ton/ha Climate + Ppd model (1.75 ton/ha) Full model (1.74 ton/ha)

19 Predictions for 2050 and RF VIP

20 Model mean difference maps Climate soil model (0.01 ton/ha) Climate Photoperiod model (0.24 ton/ha) Climate Full model (0.25 ton/ha) Legend Value High : 2 Ton/ha Low : -2 Blue: higher yield with climate only model Red: higher yield with additional factors

21 Discussion Random Forests highly capable of predicting crop yields at global and regional scales for different crops Superior performance compared to MLR Simple implementation using R and other tools and fast execution Useful for climate change studies in conjunction with soils and other categorical data Identify hot spots for crop and management improvements Limitations Potential overfitting Conditions outside the boundaries of training data Non additive interactions not included in training How can we overcome the limitations?

22 Thank you Contact: