Using seasonal climate forecasts to improve maize production decision support in Zimbabwe

Zinyengere, Nkulumo; Mhizha, Teddious; Mashonjowa, Emmanuel; Chipindu, B.; Geerts, Sam; Raes, Dirk

doi:10.1016/j.agrformet.2011.07.015

Cited by 62 publications

(31 citation statements)

References 18 publications

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“…Such results compare well with findings by Nyabako and Manzungu (2012), who reported a climate shift of the growing season length, and a future forecast of a climate condition which is not suitable for maize production in Masvingo district by 2020 (seven years from the current date). In addition, existing model experimental findings also concurs with the study findings and suggests that annual rainfall, particularly early and late rains will decrease across Zimbabwe by 50 % in 2020s (Hulm and Sheard, 1999 (Unganai, 1996;Phillips et al, 1998;Makarau, 1999;Low, 2005;Zinyengere et al, 2011;Mugandani et al, 2012). We therefore suggest a more rigorous study that investigates climate change in terms of variability on year to year basis and over a long period of time.…”

Section: Classification Of Cereals (Maize Sorghum and Millet) Accordsupporting

confidence: 75%

“…The rainfall season for Masvingo district is unimodal and spans from the month of November to March (Raes et al, 2004;Zinyengere et al, 2011;Mhizha et al, 2012). The prevailing climate is characterised by high interannual rainfall which is inherently variable and unreliable.…”

Section: Fig 31: Location Of the Study Areamentioning

confidence: 99%

“…Various authors have reported different approaches that are used when determining the onset and cession of the rain season (Raes et al, 2004;Bello et al, 2010;Zinyengere et al, 2011). In Zimbabwe for example, two criterions are used in coming up with the onset of the rain season.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Climate Change: Matching Growing Season Length with Maize Crop Varietal Life Cycles in Semi-Arid Regions of Zimbabwe

Mubvuma¹

2013

GJAS

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Climate change is set to increase the risk and uncertainty of maize production in the semi-arid Regions of Zimbabwe. The study had the objective to determine the existence of climate change in Masvingo District using the long term behaviour of the growing season length and its parameters over a period of 31 years , and to match the life cycle period of locally available maize, sorghum and pearl millet varieties with growing season length. The onset of the rain season was found to be an important indicator of growing season length and was observed to have changed from late October to late November. The growing season length for the District was noted to have changed significantly (P < 0.01) from 120 days during the early 70s to 100 days in the year 2001. With the seasonal length now averaging at 100 days, the results show that the current climate is no longer suitable for growing maize when considering the average life cycle of maize in terms of days from planting to harvesting, but is now marginally suitable for Sorghum and Pearl Millet production. Farmers in Masvingo Province are advised to stop growing rain fed maize for livelihood but focus on growing sorghum and pearl millet crops.

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Section: Classification Of Cereals (Maize Sorghum and Millet) Accordsupporting

confidence: 75%

Section: Fig 31: Location Of the Study Areamentioning

confidence: 99%

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Climate Change: Matching Growing Season Length with Maize Crop Varietal Life Cycles in Semi-Arid Regions of Zimbabwe

Mubvuma¹

2013

GJAS

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“…In fact, sub-seasonal forecast information can be useful for developing strategies for proactive natural disaster mitigation (Brunet et al, 2010;Vitart et al, 2012). Previous studies have evaluated the potential of sub-seasonal to seasonal forecasts for heat wave forecasting (e.g., Hudson et al, 2011a;White et al, 2014), hydrological forecasting (e.g., Orth and Seneviratne, 2013;Yuan et al, 2014), water resources management (e.g., Sankarasubramanian et al, 2009), hydropower production management (e.g., Garcia-Morales and Dubus, 2007), and crop yield predic-tion (e.g., Hansen et al, 2006;Zinyengere et al, 2011). Due to the improvement of numerical models, prediction techniques, and computing resources, there is an increasing focus on sub-seasonal forecasts (e.g., Toth et al, 2007;Vitart et al, 2008;Brunet et al, 2010;Hudson et al, 2011bHudson et al, , 2013Robertson et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

CFSv2-based sub-seasonal precipitation and temperature forecast skill over the contiguous United States

Tian

Wood

Yuan

2017

Hydrol. Earth Syst. Sci.

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Abstract. This paper explored the potential of a global climate model for sub-seasonal forecasting of precipitation and 2 m air temperature. The categorical forecast skill of 10 precipitation and temperature indices was investigated using the 28-year sub-seasonal hindcasts from the Climate Forecast System version 2 (CFSv2) over the contiguous United States (CONUS). The forecast skill for mean precipitation and temperature as well as for the frequency and duration of extremes was highly dependent on the forecasting indices, regions, seasons, and leads. Forecasts for 7-and 14-day temperature indices showed skill even at weeks 3 and 4, and generally were more skillful than precipitation indices. Overall, temperature indices showed higher skill than precipitation indices over the entire CONUS region at sub-seasonal scale. While the forecast skill related to mean precipitations was low in summer over the CONUS, the number of rainy days, number of consecutive rainy days, and number of consecutive dry days showed considerably high skill for the western coastal region. The presence of active Madden-Julian Oscillation (MJO) events improved CFSv2 weekly mean precipitation forecast skill over most parts of the CONUS, but it did not necessarily improve the weekly mean temperature forecasts. The 30-day forecasts of precipitation and temperature indices calculated from the downscaled monthly CFSv2 forecasts were less skillful than those calculated directly from CFSv2 daily forecasts, suggesting the usefulness of CFSv2 for sub-seasonal hydrological forecasting.

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“…The global applicability of AquaCrop is dependent on its being tested in a diverse environment under differing soil conditions, crops, agronomic practices, and climatic conditions [32,33] . For example, the calibration and evaluation of the performance of AquaCrop has been carried out for quinoa [34] , wheat [35][36][37][38][39] , sorghum [40] , maize [28,[41][42][43][44][45][46] , potato [32,47] , and cabbage [48,49] . Previous studies have demonstrated that AquaCrop is able to accurately simulate crop canopy cover, biomass yield and grain yield in diverse environments and under a variety of meteorological conditions and management practices.…”

Section: Introductionmentioning

confidence: 99%

Quantitative analysis of yield and soil water balance for summer maize on the piedmont of the North China Plain using AquaCrop

Wang¹,

Huang²,

Li³

2015

Front. Agr. Sci. Eng.

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The North China Plain (NCP) is a major grain production area in China, but the current winter wheatsummer maize system has resulted in a large water deficit. This water-shortage necessitates the improvement of crop water productivity in the NCP. A crop water model, AquaCrop, was adopted to investigate yield and water productivity (WP) for rain-fed summer maize on the piedmont of the NCP. The data sets to calibrate and validate the model were obtained from a 3-year (2011-2013) field experiment conducted on the Yanshan piedmont of the NCP. The range of root mean square error (RMSE) between the simulated and measured biomass was 0.67-1.25 t$hm -2 , and that of relative error (RE) was 9.4%-15.4%, the coefficient of determination (R 2 ) ranged from 0.992 to 0.994. The RMSE between the simulated and measured soil water storage at depth of 0-100 cm ranged from 4.09 to 4.39 mm; and RE and R 2 in the range of 1.07%-1.20% and 0.880-0.997, respectively. The WP as measured by crop yield per unit evapotranspiration was 2.50-2.66 kg$m . The simulated impact of long-term climate (i.e., 1980-2010) and groundwater depth on crop yield and WP revealed that the higher yield and WP could be obtained in dry years in areas with capillary recharge from groundwater, and much lower values elsewhere. The simulation also suggested that supplementary irrigation in areas without capillary groundwater would not result in groundwater over-tapping since the precipitation can meet the water required by both maize and ecosystem, thus a beneficial outcome for both food and ecosystem security can be assured.

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Using seasonal climate forecasts to improve maize production decision support in Zimbabwe

Cited by 62 publications

References 18 publications

Climate Change: Matching Growing Season Length with Maize Crop Varietal Life Cycles in Semi-Arid Regions of Zimbabwe

Climate Change: Matching Growing Season Length with Maize Crop Varietal Life Cycles in Semi-Arid Regions of Zimbabwe

CFSv2-based sub-seasonal precipitation and temperature forecast skill over the contiguous United States

Quantitative analysis of yield and soil water balance for summer maize on the piedmont of the North China Plain using AquaCrop

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