Climate suitability predictions for the cultivation of macadamia (Macadamia integrifolia) in Malawi using climate change scenarios
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47. Thuiller W, Lavergne S, Roquet C, Boulangeat I, Lafourcade B, Araujo M. B. Consequences of climate change on the tree of life in Europe. Nature. 2011; 470: 531–534. https://doi.org/10.1038/nature09705 PMID: 21326204 48. Rabara RC, Sotto RC, Salas EAL. Species distribution modeling and phenotypic diversity reveals a col- lection gap in the Musa balbisiana germplasm conservation in the Philippines. Asian Journal of Agricul- ture. 2020; 4 (2): 60–71. https://doi.org/10.13057/asianjagric/g040203 49. Berrar D. Cross-validation. Encyclopedia of Bioinformatics and Computational Biology. 2018; 3: 542– 545. https://doi.org/10.1016/B978-0-12-809633-8.20349-X 50. Shabani F, Kumar L, Ahmadi M. Assessing accuracy methods of species distribution models: AUC, Specificity, Sensitivity and the True Skill Statistic. Global Journal of Human Social Science, 2018; 18: 6–18. https://socialscienceresearch.org/index.php/GJHSS/article/download/2469/2358. 51. Jime´nez-Valverde A. Threshold-dependence as a desirable attribute for discrimination assessment: implications for the evaluation of species distribution models. Biodiversity Conservation. 2014; 23: 369–385. htts//doi.org/10.1007/s10531-013-0606-1. 52. Jorcin P, Barthe L, Berroneau M, Dore F, Geniez P, Grillet P, et al. Modelling the distribution of the Ocel- lated Lizard in France: Implications for conservation. Amphibian and Reptile Conservation. 2019; 13: 276–298. 53. Chemura A, Kutywayo D, Chidoko P, Mahoya C. Bioclimatic modelling of current and projected climatic
suitability of coffee (Coffea arabica) production in Zimbabwe. Regional Environmental Change. 2016; 16: 473–485. https://doi.org/10.1007/s10113-015-0762-9 54. Liu C, White M, Newell G. Selecting thresholds for the prediction of species occurrence with presence- only data. Journal of Biogeography. 2013; 40:778–789. https://doi.org/10.1111/jbi.12058 55. Chemura A, Schauberger B, Gornott C. Impacts of climate change on agro-climatic suitability of major food crops in Ghana. PLoS One, 2020; 15: 2020; 6: 1–21. https://doi.org/10.1371/journal.pone. 0229881 PMID: 32598391 56. de Sousa K, van Zonneveld M, Holmgren M, Kindt R, Ordoñez JC. The future of coffee and cocoa agro- forestry in a warmer Mesoamerica. Nature Scientific Reports. 2019; 9: 1–9. https://doi.org/10.1038/ s41598-019-45491-7 57. Mastrandrea M. D. et al., “Guidance Note for Lead Authors of the IPCC Fifth Assessment Report on Consistent Treatment of Uncertainties,” 2010, [Online]. Available: http://www.ipcc.ch. 58. Mastrandrea MD, Field CB., Stocker TF, Edenhofer O, Ebi KL, Frame DJ, et al. Guidance note for lead authors of the IPCC Fifth Assessment Report on Consistent Treatment of Uncertainties. 2010. Avail- able: http://www.ipcc.ch. 59. Evans N. The status of the macadamia value chain in Malawi. Consultation Report. 2020. 60. Mayer DG, Stephenson RA, Jones KH, Wilson KJ, Bell DJD, Wilkie J, et al. Annual forecasting of the Australian macadamia crop—integrating tree census data with statistical climate-adjustment models. Agricultural Systems. 2006; 91: 159–170. https://doi.org/10.1016/j.agsy.2006.02.004
61. Mittal D, Vincent N, Conway K, van Garderen D, Pardoe EA, Todd J, et al. Future climate projections for Malawi. 2017. Available online: www.futureclimateafrica.org (accessed on 10 January 2021). 62. McSweeney C, New M, Lizcano G. UNDP climate change country profiles Malawi. Natl. Commun. Sup- port Program. 2008. pp 1–27.https://ncsp.undp.org/sites/default/files/Malawi.oxford.report.pdf. 63. Stephenson RA, Gallagher EC. Effects of temperature during latter stages of nut development on growth and quality of macadamia nuts. Scientia Horticulturae. 1986; 30: 219–225. https://doi.org/10. 1016/0304-4238(86)90100-7 64. Dixon J, Barber R. Relationship between temperature in October and November and d Yield," No. Sep- tember. New Zealand Avocado Growers’ Association Annual Research Report. 2016; 8: 59–70. 65. The World Bank. Malawi Country Environmental Analysis. 2019. Washington, DC, USA. http://dx.doi. org/10.1016/j.cirp.2016.06.001%0A http://dx.doi.org/10.1016/j.powtec.2016.12.055%0A https://doi. org/10.1016/j.ijfatigue.2019.02.006%0Ahttps://doi.org/10.1016/j.matlet.2019.04.024%0A https://doi. org/10.1016/j.matlet.2019.127252%0A http://dx.doi.o. 66. Mittal D, Vincent N, Conway K, van Garderen D, Pardoe E.A, Todd J, et al. Future climate projections for Malawi. Annex. Available online: www.futureclimateafrica.org (accessed on 10 January 2021). 67. Kutywayo D, Chemura A, Kusena W, Chidoko P, Mahoya C. The impact of climate change on the potential distribution of agricultural pests: The case of the coffee white stem borer (Monochamus leuco-
notus P.) in Zimbabwe. PLoS One. 2013; 8. https://doi.org/10.1371/journal.pone.0073432 68. Chemura A. Modelling spatial variability of coffee (Coffea arabica L) crop condition with multispectral remote sensing data. The University of KwaZulu-Natal, KwaZulu, South Africa, Ph.D. Theses. 2017. PLOS ONE Macadamia integrifolia: Climate suitability of Malawi PLOS ONE | https://doi.org/10.1371/journal.pone.0257007 September 9, 2021 20 / 20