Fine-scale multi-temporal and spatial analysis of agricultural drought in agro-ecological regions of Zimbabwe

References

• Allen, R.G., Pereira, L.S., Raes, D. and Smith, M., 1998. Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. Fao, Rome, 300(9), p.D05109.
   Google Scholar
• Aswathi PV, Nikam BR, Chouksey A, Aggarwal ASP. 2018. Assessment and monitoring of agricultural droughts in maharashtra using meteorological and remote sensing based indices. ISPRS Ann Photogr Remote Sens Spatial Inform Sci. 4(5). 253-264
   Google Scholar
• Aziz A, Umar M, Mansha M, Khan MS, Javed MN, Gao H, Farhan SB, Iqbal I, Abdullah S. 2018. Assessment of drought conditions using HJ-1A/1B data: A case study of Potohar region, Pakistan. Geomatics Nat Hazards Risk. 9(1):1019–1036.
   Web of Science ®Google Scholar
• Bhuiyan C, Singh RP, Kogan FN. 2006. Monitoring drought dynamics in the Aravalli region (India) using different indices based on ground and remote sensing data. Int J Appl Earth Obs Geoinform. 8(4):289–302.
   Web of Science ®Google Scholar
• Chamaille-Jammes S, Fritz H, Murindagomo F. 2007. Detecting climate changes of concern in highly variable environments: Quantile regressions reveal that droughts worsen in Hwange National Park, Zimbabwe. J Arid Environ. 71(3):321–326.
   Web of Science ®Google Scholar
• Chifurira R, Chikobvu D, Dubihlela D. 2016. Rainfall prediction for sustainable economic growth. Environ Econ. 7(4):120–129.
   Google Scholar
• Chikodzi D, Mutowo G. 2013. Drought monitoring for Masvingo Province in Zimbabwe: a remote sensing perspective. Herald J Geograph Regional Plann. 2:56–60.
   Google Scholar
• Chikodzi D, Zinhiva H, Simba FM, Talent M. 2013. Reclassification of agro-ecological zones in Zimbabwe-the rationale, methods and expected benefits: The case of Masvingo Province. J Sustainable Devel Africa. 15(1):104–116.
   Google Scholar
• Cook KH. 2000. The South Indian convergence zone and interannual rainfall variability over Southern Africa. J Climate. 13(21):3789–3804.
   Web of Science ®Google Scholar
• Dent B, Torguson J, Hodler T. 2009. Cartography: Thematic map design. New York: McGraw Hill Higher Education.
   Google Scholar
• Dhakar R, Kumar VS, Pradhan S. 2013. Study on inter-seasonal and intra-seasonal relationships of meteorological and agricultural drought indices in the Rajasthan State of India. J Arid Environ. 97(2013):108e119–119.
   Google Scholar
• Drisya, J.; Kumar, D.S.; Roshni, T. Spatiotemporal Variability of Soil Moisture and Drought Estimation Using a Distributed Hydrological Model. In Integrating Disaster Science and Management; Pijush, S., Dookie, K., Chandan, G., Eds.; Elsevier: Amsterdam, The Netherlands, 2018; 451–460.
   Google Scholar
• Dutta D, Kundu A, Patel NR, Saha SK, Siddiqui AR. 2015. Assessment of agricultural drought in Rajasthan (India) using remote sensing derived vegetation condition index (VCI) and standardized precipitation index (SPI). Egyptian J Remote Sens Space Sci. 18(1):53–63.
   Web of Science ®Google Scholar
• ESA. 2016. ESA Climate Change Initiative Land Cover Sentinel-2 Prototype Map of Africa 2016 at 20 meters. Accessed 20 April 2021. http://2016africalandcover20m.esrin.esa.int/download.php
   Google Scholar
• FAO. 2016. The state of food and agriculture 2016: Climate change, agriculture, and food security. Rome, Italy: FAO.
   Google Scholar
• Frischen J, Meza I, Rupp D, Wietler K, Hagenlocher M. 2020. Drought risk to agricultural systems in Zimbabwe: A spatial analysis of hazard, exposure, and vulnerability. Sustainability. 12(3):752–723.
   Web of Science ®Google Scholar
• Gitelson AA, Kogan F, Zakarin E, Spivak L, Lebed L. 1998. Using AVHRR data for quantitive estimation of vegetation conditions: Calibration and validation. Adv Space Res. 22(5):673–676.
   Web of Science ®Google Scholar
• Jain SK, Keshri R, Goswami A, Sarkar A. 2010. Application of meteorological and vegetation indices for evaluation of drought impact: a case study for Rajasthan, India. Nat Hazards. 54(3):643–656.
   Web of Science ®Google Scholar
• Javed T, Li Y, Rashid S, Li F, Hu Q, Feng H, Chen X, Ahmad S, Liu F, Pulatov B., 2021. Performance and relationship of four different agricultural drought indices for drought monitoring in China's mainland using remote sensing data. Sci Total Environ. 759:143530.
   PubMed Web of Science ®Google Scholar
• Ji L, Peters AJ. 2003. Assessing vegetation response to drought in the northern Great Plains using vegetation and drought indices. Remote Sens Environ. 87(1):85–98.
   Web of Science ®Google Scholar
• Jiao W, Zhang L, Chang Q, Fu D, Cen Y, Tong Q. 2016. Evaluating an enhanced vegetation condition index (VCI) based on VIUPD for drought monitoring in the continental United States. Remote Sens. 8(3):224.
   Web of Science ®Google Scholar
• Khan N, Sachindra DA, Shahid S, Ahmed K, Shiru MS, Nawaz N. 2020. Prediction of droughts over Pakistan using machine learning algorithms. Adv Water Res. 139:103562.
   Web of Science ®Google Scholar
• Khan R, Gilani H. 2021. Global drought monitoring with big geospatial datasets using Google Earth Engine. Environ Sci Pollut Res. 28(14):17244–17264..
   PubMed Web of Science ®Google Scholar
• Kogan FN. 1995. Application of vegetation index and brightness temperature for drought detection. Adv Space Res. 15(11):91–100.
   PubMed Web of Science ®Google Scholar
• Kogan F, Guo W. 2015. 2006–2015 mega-drought in the western USA and its monitoring from space data. Geomatics Nat Hazards Risk. 6(8):651–668.
   Web of Science ®Google Scholar
• Kogan F, Guo W, Yang W. 2017. SNPP/VIIRS vegetation health to assess 500 California drought. Geomatics Nat Hazards Risk. 8(2):1383–1395.
   Web of Science ®Google Scholar
• Kogan F, Guo W, Yang W. 2020. Near 40-year drought trend during 1981-2019 earth warming and food security. Geomatics Nat Hazards Risk. 11(1):469–490.
   Web of Science ®Google Scholar
• Kogan F, Guo W, Yang W. 2019. Drought and food security prediction from NOAA new generation of operational satellites. Geomatics Nat Hazards Risk. 10(1):651–666.
   Web of Science ®Google Scholar
• Kogan F, Guo W, Yang W, Harlan S. 2018. Space-based vegetation health for wheat yield modeling and prediction in Australia. J Appl Remote Sens. 12(2):026002.
   Web of Science ®Google Scholar
• Kuri F, Masocha M, Murwira A, Murwira KS. 2020. Differential impact of remotely sensed dry dekads on maize yield in Zimbabwe. Geocarto Int. 35(13):1489–1510.
   Web of Science ®Google Scholar
• Kuri F, Murwira A, Murwira KS, Masocha M. 2018. Accounting for phenology in maize yield prediction using remotely sensed dry dekads. Geocarto Int. 33(7):723–736.
   Web of Science ®Google Scholar
• Kuri F, Murwira A, Murwira KS, Masocha M. 2014. Predicting maize yield in Zimbabwe using dry dekads derived from remotely sensed vegetation condition index. Int J Appl Earth Observ Geoinform. 33:39–46.
   Web of Science ®Google Scholar
• Lawal S, Bruce H, Temitope SE, Ayodeji A. 2021. On the suitability of using vegetation indices to monitor the response of Africa's terrestrial ecoregions to drought. Sci Total Environ. 792(2021):148282.
   PubMed Web of Science ®Google Scholar
• Liu WT, Kogan FN. 1996. Monitoring regional drought using the vegetation condition index. Int J Remote Sens. 17(14):2761–2782.
   Web of Science ®Google Scholar
• Magno R, De Filippis T, Di Giuseppe E, Pasqui M, Rocchi L, Gozzini B. 2018. Semi-automatic operational service for drought monitoring and forecasting in the Tuscany region. Geosciences. 8(2):49.
   Web of Science ®Google Scholar
• Manjowe M, Mushore TD, Gwenzi J, Mutasa C, Matandirotya E, Mashonjowa E. 2018. Circulation mechanisms responsible for wet or dry summers over Zimbabwe. AIMS Environ Sci. 5(3):154–172.
   Web of Science ®Google Scholar
• Makarau A, Jury MR. 1997. Predictability of Zimbabwe summer rainfall. Int J Climatol: J R Meteorol Soc. 17(13):1421–1432.
   Web of Science ®Google Scholar
• Maphosa B. 1994. Lessons from the 1992 drought in Zimbabwe. The quest for alternative food policies. Nordic J Afr Stud. 3:53–58.
   Google Scholar
• Mason S, Lindesay J, Tyson J. 1994. Simulating drought in Southern Africa using sae surface temperature variations. Water SA. 20:15–22.
   Web of Science ®Google Scholar
• Masvaya EN, Nyamangara J, Nyawasha RW, Zingore S, Delve RJ, Giller KE. 2010. Effect of farmer management strategies on spatial variability of soil fertility and crop nutrient uptake in contrasting agro-ecological zones in Zimbabwe. Nutr Cycl Agroecosyst. 88(1):111–120.
   Web of Science ®Google Scholar
• Matthews BW. 1975. Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim Biophys Acta (BBA) Protein Struct. 405(2):442–451.
   PubMed Web of Science ®Google Scholar
• Mazvimavi D. 2010. Investigating changes over time of annual rainfall in Zimbabwe. Hydrol Earth Syst Sci. 14(12):2671–2679.
   Web of Science ®Google Scholar
• Mckee TB, Doesken NJ, Kleist J. 1993. The relationship of drought frequency and duration to time scale. In Proceedings of the Eighth Conference on Applied Climatology, Anaheim, California, 17–22 January 1993. Boston, American Meteorological Society, p. 179–184.
   Google Scholar
• Mikaili O, Rahimzadegan M. 2022. Investigating remote sensing indices to monitor drought impacts on a local scale (case study: Fars province, Iran). Nat Hazards. 111(3):2511–2529.
   Web of Science ®Google Scholar
• Moyo S. 2000. The political economy of land acquisition and redistribution in Zimbabwe, 1990-1999. J Southern African Stud. 26(1):5–28.
   Web of Science ®Google Scholar
• Kourouma JM, Eze E, Negash E, Phiri D, Vinya R, Girma A, Zenebe A., 2021. Assessing the spatio-temporal variability of NDVI and VCI as indices of crops productivity in Ethiopia: a remote sensing approach, Geomatics. Nat Hazards Risk. 12(1):2880–2903.
   Web of Science ®Google Scholar
• Kuri F,Masocha M,Murwira A,Murwira KS. 2020. Differential impact of remotely sensed dry dekads on maize yield in Zimbabwe. Geocarto International. 35(13):1489–1510. doi:10.1080/10106049.2019.1583774.
   Web of Science ®Google Scholar
• Muchadeyi FC, Wollny CBA, Eding H, Weigend S, Makuza SM, Simianer H. 2007. Variation in village chicken production systems among agro-ecological zones of Zimbabwe. Trop Anim Health Prod. 39(6):453–461.
   PubMed Web of Science ®Google Scholar
• Mugandani R, Wuta M, Makarau A, Chipindu B. 2012. Re-classification of agro-ecological regions of Zimbabwe in conformity with climate variability and change. Afr Crop Sc J. 20(2):361–369.
   Google Scholar
• Mupepi O, Matsa MM. 2022. Spatio-temporal dynamics of drought in Zimbabwe between 1990 and 2020: a review. Spat Inf Res. 30(1):117–114.
   Web of Science ®Google Scholar
• Muthumanickam D, Kannan P, Kumaraperumal R, Natarajan S, Sivasamy R, Poongodi C. 2011. Drought assessment and monitoring through remote sensing and GIS in western tracts of Tamil Nadu, India. Int J Remote Sens. 32(18):5157–5176.
   Web of Science ®Google Scholar
• Mutowo G, Chikodzi D. 2014. Remote sensing based drought monitoring in Zimbabwe. Disaster Prev Manage. 23(5):649–659.
   Web of Science ®Google Scholar
• Oladipo EO, Kyari JD. 1993. Fluctuations in the onset, termination and length of the growing season in Northern Nigeria. Theor Appl Climatol. 47(4):241–250.
   Web of Science ®Google Scholar
• Qian X, Liang L, Shen Q, Sun Q, Zhang L, Liu Z, Zhao S, Qin Z. 2016. Drought trends based on the VCI and its correlation with climate factors in the agricultural areas of China from 1982 to 2010. Environ Monit Assess. 188(11):639.
   PubMed Web of Science ®Google Scholar
• Quiring SM, Ganesh S. 2010. Evaluating the utility of the Vegetation Condition Index (VCI) for monitoring meteorological drought in Texas. Agric for Meteorol. 150(3):330–339.
   Web of Science ®Google Scholar
• R Development Core Team. 2015. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna.
   Google Scholar
• Shoko C, Masocha M, Dube T. 2015. A new potential method to estimate abundance of small game species. African J Ecology, 53(4): 406–412.
   Web of Science ®Google Scholar
• Sierra-Soler A, Adamowski J, Malard J, Qi Z, Saadat H, Pingale S. 2016. Assessing agricultural drought at a regional scale using LULC classification, SPI, and vegetation indices: case study in a rainfed agro-ecosystem in Central Mexico. Geomatics Nat Hazards Risk. 7(4):1460–1488.
   Web of Science ®Google Scholar
• Simba FM, Chikodzi D, Murwendo T. 2012. Climate change scenarios, perceptions and crop production: a case study of Semi-arid Masvingo Province in Zimbabwe. J Earth Sci Climate Change. 3(124):2.
   Google Scholar
• Singh RP, Roy S, Kogan F. 2003. Vegetation and temperature condition indices from NOAA AVHRR data for drought monitoring over India. Int J Remote Sens. 24(22):4393–4402.
   Web of Science ®Google Scholar
• Tadross M, Suarez P, Lotsch A, Hachigonta S, Mdoka M, Unganai L, Lucio F, Kamdonyo D, Muchinda M. 2007. Changes in growing-season rainfall characteristics and downscaled scenarios of change over southern Africa: implications for growing maize. In IPCC regional Expert Meeting on Regional Impacts, Adaptation, Vulnerability, and Mitigation, Nadi, Fiji, 193–204.
   Google Scholar
• Törnros T, Menzel L. 2014. Addressing drought conditions under current and future climates in the Jordan River region. Hydrol Earth Syst Sci. 18(1):305–318.
   Web of Science ®Google Scholar
• Tsakiris G, Vangelis H. 2005. Establishing a drought index incorporating evapotranspiration. Eur Water. 9:3–11.
   Google Scholar
• Twomlow, S.J., Steyn, J.T. and Du Preez, C.C., 2006. Dryland farming in southern Africa. Dryland Agricul. 23:769–836.
   Google Scholar
• Unganai LS, Kogan FN. 1998. Drought monitoring and corn yield estimation in Southern Africa from AVHRR data. Remote Sens Environ. 63(3):219–232.
   Web of Science ®Google Scholar
• Vasilca D. 2019. How to create an effective thematic map. Bull UASVM Horticulture. 76:258–267.
   Google Scholar
• Wei W, Zhang J, Zhou L, Xie B, Zhou J, Li C. 2021. Comparative evaluation of drought indices for monitoring drought based on remote sensing data. Environ Sci Pollut Res. 28(16):20408–20425.
   PubMed Web of Science ®Google Scholar
• Wilhite DA, Hayes MJ, Svodoba MD. 2000. Drought monitoring and assessment in the U.S. In: Voght JV, Somma F (eds) Drought and drought mitigation in Europe. Kluwers, Dordrecht.
   Google Scholar
• Wilhite DA, Glantz MH. 1985. Understanding: The drought phenomenon: The role of definitions. Water Int. 10(3):111–120.
   Google Scholar
• Winkler K, Gessner U, Hochschild V. 2017. Identifying droughts affecting agriculture in Africa based on remote sensing time series between 2000–2016: rainfall anomalies and vegetation condition in the context of ENSO. Remote Sens. 9(8):831.
   Web of Science ®Google Scholar
• World Bank. 2005. Global drought hazard distribution. Zimbabwe: World Bank Zimbabwe.
   Google Scholar
• Zambrano F, Lillo-Saavedra M, Verbist K, Lagos O. 2016. Sixteen years of agricultural drought assessment of the BioBío region in Chile using a 250 m resolution Vegetation Condition Index (VCI). Remote Sens. 8(6):530.
   Web of Science ®Google Scholar
• Zhang H, Ali S, Ma Q, Sun L, Jiang N, Jia Q, Hou F., 2021. Remote sensing strategies to characterization of drought, vegetation dynamics in relation to climate change from 1983 to 2016 in Tibet and Xinjiang Province, China. Environ Sci Pollut Res. 28(17):21085–21100.
   PubMed Web of Science ®Google Scholar