Climate suitability predictions for the cultivation of macadamia (Macadamia integrifolia) in Malawi using climate change scenarios
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PLOS ONE Macadamia integrifolia: Climate suitability of Malawi PLOS ONE | https://doi.org/10.1371/journal.pone.0257007 September 9, 2021 18 / 20
25. Barrueto AK, Merz J, Hodel E, Eckert S. The suitability of Macadamia and Juglans for cultivation in Nepal: an assessment based on spatial probability modelling using climate scenarios and in situ data. Regional Environmental Change. 2018; 18: 859–871. https://doi.org/10.1007/s10113-017-1225-2 26. Nagao MA, Hirae HH. Macadamia: Cultivation and physiology. Critical Reviews in Plant Sciences. 1992; 10: 441–470. https://doi.org/10.1080/07352689209382321 27. Pichakum A, Supaibulwatana K, Chintakovid W, Chanseetis C. Role of temperature and altitude on flowering performances of macadamia nut. Acta Horticulturae. 2015; 1024: 127–132, https://doi.org/ 10.17660/ActaHortic.2014.1024.13 28. Evans N. Suitability Mapping of the Malawi macadamia industry. Consultation Report. 2008. 29. Chemura A, Mudereri BT, Yalew AW, Gornott C. Climate change and specialty coffee potential in Ethio- pia. Nature Scientific Reports. 2021; 11:1–13. https://doi.org/10.1038/s41598-021-87647-4 PMID: 33854166 30. Taghizadeh-Mehrjardi R, Nabiollahi K, Rasoli L, Kerry R, Scholten T. Land suitability assessment and agricultural production sustainability using machine learning models. Agronomy. 2020; 10: 1–20. https://doi.org/10.3390/agronomy10040573 31. Benson T, Mabiso A, Nankhuni F. Detailed crop suitability maps and an agricultural zonation scheme for Malawi. Michigan State University, Michigan, USA. 2016. Available: http://dec.usaid.gov/. 32. Mutegi J, Kabambe V, Zingore S, Harawa R, Wairegi L. The status of fertilizer recommendation in
Malawi: Gaps, challenges, and opportunities. Soil Health Consortium of Malawi. 2015. pp 56. 33. Barbet-Massin M, Jiguet F, Albert CH, Thuiller W. Selecting pseudo-absences for species distribution models: How, where, and how many? Methods in Ecology and Evolution. 2012; 3: 327–338. https://doi. org/10.1111/j.2041-210X.2011.00172.x 34. van Vuuren DP, Edmonds J, Kainuma M, Riahi K, Thomson A, Hibbard K, Hurtt GC, Kram T, Krey V, Lamarque JF, Masui T, Meinshausen M, Nakicenovic N, Smith SJ, Rose SK. The representative con- centration pathways: An overview. Climate Change. 2011; 109: 5–31. https://doi.org/10.1007/s10584- 011-0148-z 35. de Sousa K, van Zonneveld M, Imbach P, Casanoves F, Kindt R, Ordoñez JC. Suitability of key Central American agroforestry species under future climates. 2017; An atlas. ICRAF Occasional Paper no. 26. Turrialba, Costa Rica. 36. United Nations Environnent Programme. Emissions Gap Report. 2019. UNEP, Nairobi. 37. U.K. Met Office. UKCP18 Guidance: Representative Concentration Pathways. 2018. pp. 18–20. https:// www.metoffice.gov.uk/research/approach/collaboration/ukcp/guidance-science-reports. 38. van Vuuren DP, den Elzen GJ, Lucas PL, Eickout B, Strengers BJ, van Ruijven B, Wonink S, van Houdt R. Stabilizing greenhouse gas concentrations at low levels: An assessment of reduction strategies and costs. Climate Change 2007; 81: 119–159. https://doi.org/10.1007/s10584-006-91729 39. Mudereri BT, Chitata T, Chemura A, Makaure A, Mukanga A, Abdel-Rahman EM. Is the protected area
coverage still relevant in protecting the Southern Ground-hornbill (Bucorvus leadbeateri) biological niche in Zimbabwe? Perspectives from ecological predictions. GIScience Remote Sensing. 2021; 58: 405–424. https://doi.org/10.1080/15481603.2021.1883947 40. Chemura A, Mudereri BT, Yalew AW, Gornott C. Climate change and specialty coffee potential in Ethio- pia. Nature Scientific Reports. 2021; 11:1–13. https://doi.org/10.1038/s41598-021-87647-4 PMID: 33854166 41. Lin FJ. Solving multicollinearity in the process of fitting regression models using the nested estimate procedure. Quality and Quantity. 2008; 42: 417–426. https://doi.org/10.1007/s11135-006-9055-1 42. Lin FJ. Solving multicollinearity in the process of fitting regression models using the nested estimate procedure. Quality and Quantity. 2008; 42: 417–426. https://doi.org/10.1007/s11135-006-9055-1 43. Ranjitkar S, Sujakhu NM, Merz J, Kindt R, Xu J, Matin MA, et al. Suitability analysis and projected cli- mate change impact on banana and coffee production zones in Nepal. PLoS One. 2016; 11. https://doi. org/10.1371/journal.pone.0163916 PMID: 27689354 44. Kindt R. Ensemble species distribution modelling with transformed suitability values. Environmental Modelling and Software. 2018; 100: 136–145. https://doi.org/10.1016/j.envsoft.2017.11.009 45. de Sousa K, Solberg SØ. Conservation gaps in traditional vegetables native to Europe and Fennoscan- dia. Agriculture. 2020. 10: 8. 340. https://doi.org/10.3390/agriculture10080340
46. Brotons L, Thuiller W, Arau´jo MB, Hirzel AH. Presence-absence versus presence-only modelling meth- ods for predicting bird habitat suitability. Ecography (Cop.). 2004; 27: 437–448. https://doi.org/10.1111/ j.0906-7590.2004.03764.x PLOS ONE Macadamia integrifolia: Climate suitability of Malawi PLOS ONE | https://doi.org/10.1371/journal.pone.0257007 September 9, 2021 19 / 20
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