Comparing mechanistic and empirical approaches to modeling the thermal niche of almond
- 327 Downloads
Delineating locations that are thermally viable for cultivating high-value crops can help to guide land use planning, agronomics, and water management. Three modeling approaches were used to identify the potential distribution and key thermal constraints on on almond cultivation across the southwestern United States (US), including two empirical species distribution models (SDMs)—one using commonly used bioclimatic variables (traditional SDM) and the other using more physiologically relevant climate variables (nontraditional SDM)—and a mechanistic model (MM) developed using published thermal limitations from field studies. While models showed comparable results over the majority of the domain, including over existing croplands with high almond density, the MM suggested the greatest potential for the geographic expansion of almond cultivation, with frost susceptibility and insufficient heat accumulation being the primary thermal constraints in the southwestern US. The traditional SDM over-predicted almond suitability in locations shown by the MM to be limited by frost, whereas the nontraditional SDM showed greater agreement with the MM in these locations, indicating that incorporating physiologically relevant variables in SDMs can improve predictions. Finally, opportunities for geographic expansion of almond cultivation under current climatic conditions in the region may be limited, suggesting that increasing production may rely on agronomical advances and densifying current almond plantations in existing locations.
KeywordsAgroclimatology Species distribution modeling Phenology Almond
We are appreciative of the almond expertise provided by David Doll, the feedback on early versions of the manuscript from Amber Kerr and Kripa Jagannathan, and the feedback from three anonymous reviewers.. This research was supported by the National Institute of Food and Agriculture competitive grant, award number 2011-68002-30191.
- Almond Board of California (2015) Almond Almanac 2015. http://www.almonds.com/sites/default/files/content/attachments/2015_almanac.pdf. Accessed 1 August 2016.
- Connell JH, Gradziel TM, Lampinen BD, Micke WC, Floyd J (2010) Harvest maturity of almond cultivars in California’s Sacramento Valley. Options Méditerranéennes. Serie A, Seminaires Méditerranéennes 94:19–23Google Scholar
- Covert MM (2011) The influence of chilling and heat accumulation on bloom timing, bloom length, and crop yield. Masters thesis, California Polytechnic State University, San Luis Obispo. doi: 10.15368/theses.2011.222
- Crane T, Roncoli C, Paz J, Hoogenboom G (2010) Seasonal climate forecasts and agricultural risk management: the social lives of applied climate technologies. In: S. Drobot, Demuth, J. & Gruntfest, E. (Eds.), Weather and Society*Integrated Studies Compendium, National Center for Atmospheric Research, Boulder, Colorado. http://www.sip.ucar.edu/wasis/compendium.php. Accessed 4 August 2016.
- Hatfield J, Takle G, Grotjahn R, Holden P, Izaurralde RC, Mader T, Marshall E, Liverman D (2014) Ch. 6: Agriculture. In: Melillo JM, Richmond TC, Yohe GW (Eds.) Climate change impacts in the United States: the Third National Climate Assessment. U.S. Global Change Research Program, 150–174. doi:10.7930/J02Z13FR.
- Howitt R, MacEwan D, Medellín-Azuara J, Lund J, Sumner D (2015) Economic analysis of the 2015 drought for California agriculture. Center for Watershed Sciences, University of California, Davis. https://watershed.ucdavis.edu/files/biblio/Economic_Analysis_2015_California_Drought__Main_Report.pdf. Accessed 12 August 2016
- Janick J, Moore JN (1996) Fruit breeding, Nuts Vol. 3. John Wiley & Sons, New YorkGoogle Scholar
- Johnson R, Cody BA (2015) California Agricultural Production and Irrigated Water Use. UNT Digital Library Washington D.C. http://digital.library.unt.edu/ark:/67531/metadc770633/ Accessed 26 August 2016.
- Leemans R, Solomon AM (1993) Modeling the potential change in yield and distribution of the earth’s crops under a warmed climate (No. PB-94-157369/XAB; EPA--600/J-94/158). Environmental Protection Agency, Corvallis, OR (United States).Google Scholar
- Linvill DE (1990) Calculating chilling hours and chill units from daily maximum and minimum temperature observations. Hortscience 25:14–16Google Scholar
- Miranda C, Santesteban LG, Royo JB (2005) Variability in the relationship between frost temperature and injury level for some cultivated Prunus species. Hortscience 40:357–361Google Scholar
- Mitchell KE, Lohmann D, Houser PR, Wood EF, Schaake JC, Robock A, Cosgrove BA, Sheffield J, Duan Q, Luo L (2004) The multi-institution North American Land Data Assimilation System (NLDAS): utilizing multiple GCIP products and partners in a continental distributed hydrological modeling system. Journal of Geophysical Research: Atmospheres 109:D07S90.Google Scholar
- Polce C, Garratt MP, Termansen M, Ramirez-Villegas J, Challinor AJ, Lappage MG, Boatman ND, Crowe A, Endalew AM, Potts SG, Somerwill KE (2014) Climate-driven spatial mismatches between British orchards and their pollinators: increased risks of pollination deficits. Glob Chang Biol 20:2815–2828CrossRefGoogle Scholar
- Snyder RL, Melo-Abreu JP (2005) Frost protection: fundamentals, practice and economics. Food and Agricultural Organization of the United Nations, RomeGoogle Scholar
- UCIPM. Almond: identify hull split. http://ipm.ucanr.edu/PMG/C003/m003fchullsplit.html. Accessed 24 June 2016.
- University of California. Regional Almond Variety Trial Progress Report (1996–2006). http://fruitsandnuts.ucdavis.edu/dsadditions/Regional_Almond_Variety_Trials/. Accessed 18 January 2017.
- US Department of Agriculture. National Agricultural Statistics Service (2016). Data and Statistics. https://www.nass.usda.gov/Data_and_Statistics/index.php. Accessed 6 June 2016.
- USDA National Agricultural Statistics Service Cropland Data Layer (2014). Published crop-specific data layer. USDA-NASS, Washington, DC. https://nassgeodata.gmu.edu/CropScape/. Accessed 6 June 2016.
- Zavalloni C, Andresen JA, Flore JA (2006) Phenological models of flower bud stages and fruit growth of Montmorency sour cherry based on growing degree-day accumulation. J Am Soc Hortic Sci 131:601–607Google Scholar