Random forests for global and regional crop yield predictions

PLoS ONE

Volumn 11, Issue 6, 2016, Pages

  Jeong, Jig Han ^a   Resop, Jonathan P ^b,c   Mueller, Nathaniel D ^d   Fleisher, David H ^c   Yun, Kyungdahm ^a   Butler, Ethan E ^e   Timlin, Dennis J ^c   Shim, Kyo Moon ^f   Gerber, James S ^e   Reddy, Vangimalla R ^c   Kim, Soo Hyung ^a  

^a University of Washington   (United States)

^b UNIVERSITY OF MARYLAND   (United States)

^c AGRICULTURAL RESEARCH SERVICE   (United States)

^d HARVARD UNIVERSITY   (United States)

^e UNIVERSITY OF MINNESOTA   (United States)

^f National Institute of Animal Science   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

CLIMATE; DATA ANALYSIS; ERROR; GRAIN YIELD; HARVEST; MAIZE; MODEL; MULTIPLE LINEAR REGRESSION ANALYSIS; PLANT YIELD; POTATO; PREDICTION; RANDOM FOREST; STATISTICS; WHEAT; CROP; MACHINE LEARNING; THEORETICAL MODEL;

CROPS, AGRICULTURAL; MACHINE LEARNING; MODELS, THEORETICAL;

EID: 84973502992     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0156571     Document Type: Article

References (50)

1. Lobell DB, Burke MB. Why are agricultural impacts of climate change so uncertain? The importance of temperature relative to precipitation. Environ Res Lett. 2008; 3(3).
2. Tilman D, Balzer C, Hill J, Befort BL. Global food demand and the sustainable intensification of agriculture. Proceedings of the National Academy of Sciences. 2011; 108(50):20260-4.
3. Lobell DB, Burke MB. On the use of statistical models to predict crop yield responses to climate change. Agricultural and Forest Meteorology. 2010; 150(11):1443-52.
4. Sheehy JE, Mitchell PL, Ferrer AB. Decline in rice grain yields with temperature: Models and correlations can give different estimates. Field Crop Res. 2006; 98(2-3):151-6.
5. Landau S, Mitchell RAC, Barnett V, Colls JJ, Craigon J, Payne RW. A parsimonious, multiple-regression model of wheat yield response to environment. Agricultural and Forest Meteorology. 2000; 101(2-3):151-66.
6. Lawler JJ, White D, Neilson RP, Blaustein AR. Predicting climate-induced range shifts: model differences and model reliability. Glob Change Biol. 2006; 12(8):1568-84.
7. Olden JD, Lawler JJ, Poff NL. Machine learning methods without tears: A primer for ecologists. Q Rev Biol. 2008; 83(2):171-93. PMID: 18605534
8. Morin X, Thuiller W. Comparing niche- and process-based models to reduce prediction uncertainty in species range shifts under climate change. Ecology Ecology. 2009; 90(5):1301-13. PMID: 19537550
9. Prasad A, Iverson L, Liaw A. Newer Classification and Regression Tree Techniques: Bagging and Random Forests for Ecological Prediction. Ecosystems. 2006; 9(2):181-99.
10. Marmion M, Parviainen M, Luoto M, Heikkinen RK, Thuiller W. Evaluation of consensus methods in predictive species distribution modelling. Diversity and distributions. 2009; 15(1):59-69.
11. Cutler DR, Edwards TC, Beard KH, Cutler A, Hess KT, Gibson J, et al. Random forests for classification in ecology. Ecology. 2007; 88(11):2783-92. PMID: 18051647
12. Diaz-Uriarte R, de Andres SA. Gene selection and classification of microarray data using random forest. Bmc Bioinformatics. 2006; 7.
13. Lunetta KL, Hayward LB, Segal J, Van Eerdewegh P. Screening large-scale association study data: exploiting interactions using random forests. Bmc Genet. 2004; 5.
14. Bureau A, Dupuis J, Falls K, Lunetta KL, Hayward B, Keith TP, et al. Identifying SNPs predictive of phenotype using random forests. Genet Epidemiol. 2005; 28(2):171-82. PMID: 15593090
15. Fukuda S, Spreer W, Yasunaga E, Yuge K, Sardsud V, Muller J. Random Forests modelling for the estimation of mango (Mangifera indica L. cv. Chok Anan) fruit yields under different irrigation regimes. Agric Water Manage. 2013; 116:142-50.
16. Mutanga O, Adam E, Cho MA. High density biomass estimation for wetland vegetation using World-View-2 imagery and random forest regression algorithm. Int J Appl Earth Obs. 2012; 18:399-406.
17. Vincenzi S, Zucchetta M, Franzoi P, Pellizzato M, Pranovi F, De Leo GA, et al. Application of a Random Forest algorithm to predict spatial distribution of the potential yield of Ruditapes philippinarum in the Venice lagoon, Italy. Ecol Model. 2011; 222(8):1471-8.
18. Breiman L. Random forests. Mach Learn. 2001; 45(1):5-32.
19. Gromping U. Variable Importance Assessment in Regression: Linear Regression versus Random Forest. Am Stat. 2009; 63(4):308-19.
20. Berk RA. Statistical learning from a regression perspective. New York, NY: Springer Verlag; 2008. xvii+358p. p.
21. Villareal R, Klatt A, editors. Wheats for more tropical environments. Symposium on Wheats for More Tropical Environments, Mexico, DF (Mexico), 24-28 Sep 1984; 1985: CIMMYT.
22. Monfreda C, Ramankutty N, Foley JA. Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000. Global Biogeochemical Cycles. 2008; 22(1).
23. ESRI. ArcGIS Desktop: Release 10. 2011.
24. Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A. Very high resolution interpolated climate surfaces for global land areas. Int J Climatol. 2005; 25(15):1965-78.
25. Hutchinson M. ANUSPLIN Version 4.3. User Guide, The Australian National University, Centre for Resources and Environmental Studies, Canberra. 2004.
26. Mueller ND, Gerber JS, Johnston M, Ray DK, Ramankutty N, Foley JA. Closing yield gaps through nutrient and water management. Nature. 2012; 490(7419):254-7. doi: 10.1038/nature11420 PMID: 22932270
27. NASS. Census of Agriculture. In: National Agricultural Statistics Service USDoA, editor. 2015.
28. Butler EE, Huybers P. Variations in the sensitivity of US maize yield to extreme temperatures by region and growth phase. Environ Res Lett. 2015; 10(3).
29. Menne MJ, Durre I, Vose RS, Gleason BE, Houston TG. An overview of the global historical climatology network-daily database. Journal of Atmospheric and Oceanic Technology. 2012; 29(7):897-910.
30. Resop JP, Fleisher DH, Timlin DJ, Reddy V. Biophysical Constraints to Potential Production Capacity of Potato across the US Eastern Seaboard Region. Agronomy Journal. 2014; 106(1):43-56.
31. NASS. Census of Agriculture. In: National Agricultural Statistics Service USDoA, editor. 2009.
32. NASS. 2010 Cropland Data Layer. In: National Agricultural Statistics Service USDoA, editor. 2011.
33. R Development Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing ( Austria, Eds). 2010.
34. Liaw A, Wiener M. Classification and regression by randomForest. R News. 2002; 2(3):18-22.
35. Venables WN, Ripley BD. Modern applied statistics with S. Fourth Edition. ed. New York: Springer,; 2002.
36. Willmott CJ. On the validation of models. Physical geography. 1981; 2(2):184-94.
37. Wallach D, Makowski D, Jones JW. Working with dynamic crop models: Methods, tools, and examples for agriculture and environment. 2nd ed. Wallach D, Makowski D, Jones JW, Brun F, editors. Amsterdam, Boston: Elsevier; 2014.
38. Kim S-H, Yang Y, Timlin DJ, Fleisher DH, Dathe A, Reddy VR, et al. Modeling temperature responses of leaf growth, development, and biomass in maize with MAIZSIM. Agronomy Journal. 2012; 104(6):1523-37.
39. Krause P, Boyle DP, Base F. Comparison of different efficiency criteria for hydrological model assessment. Advances in Geosciences. 2005; 5:89-97.
40. Svetnik V, Liaw A, Tong C, Wang T. Application of Breiman's random forest to modeling structure-activity relationships of pharmaceutical molecules. Lect Notes Comput Sc. 2004; 3077:334-43.
41. Svetnik V, Liaw A, Tong C, Culberson JC, Sheridan RP, Feuston BP. Random forest: A classification and regression tool for compound classification and QSAR modeling. J Chem Inf Comp Sci. 2003; 43(6):1947-58.
42. Chen XW, Liu M. Prediction of protein-protein interactions using random decision forest framework. Bioinformatics. 2005; 21(24):4394-400. PMID: 16234318
43. Friedman JH. Greedy function approximation: A gradient boosting machine. Ann Stat. 2001; 29(5):1189-232.
44. Hastie T, Tibshirani R, Friedman JH. The elements of statistical learning: data mining, inference, and prediction: with 200 full-color illustrations. New York: Springer; 2001. xvi+533p. p.
45. Kucharik CJ, Ramankutty N. Trends and Variability in U.S. Corn Yields Over the Twentieth Century. Earth Interactions. 2005; 9(1):1-29.
46. Breiman L. Statistical modeling: The two cultures. Stat Sci. 2001; 16(3):199-215.
47. Butler EE, Huybers P. Adaptation of US maize to temperature variations. Nature Climate Change. 2013; 3(1):68-72.
48. Carlson RE. Heat-stress, plant-available soil-moisture, and corn yields in Iowa: a short-term and longterm view. J Prod Agric. 1990; 3(3):293-7.
49. Schlenker W, Roberts MJ. Nonlinear temperature effects indicate severe damages to US crop yields under climate change. Proc Natl Acad Sci U S A. 2009; 106(37):15594-8. doi: 10.1073/pnas.0906865106 PMID: 19717432
50. Segal MR. Machine learning benchmarks and random forest regression. 2004.