Local impacts of climate change and agronomic practices on dry land crops in Southern Africa

Zinyengere, Nkulumo; Crespo, Olivier; Hachigonta, Sepo; Tadross, Mark

doi:10.1016/j.agee.2014.07.002

Cited by 47 publications

(41 citation statements)

References 38 publications

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“…Therefore, they need information on the potential impact of climate change for the next few decades. Recent studies have established that an undertaking of climate change impact assessments at local scales is essential as it allows exploration of local agronomic management practices and their incorporation into adaptation strategies formulation (Zinyengere, Crespo, Hachigonta, & Tadross, 2014). In Tanzania, there is paucity of information on impacts of climate change on sorghum and maize though studies elsewhere seem to suggest that impacts on sorghum are predicted to be less significant compared to maize.…”

Section: Introductionmentioning

confidence: 99%

“…Uncertainty in the projected impact of climate change on crops was assessed through two measures namely, sign of mean yield change and comparison of interannual yield variability using coefficients of variation (CV), similar to methods used by Ruiz-Ramos and Minguez (2010) and Zinyengere et al (2014). The sign of mean yield change was determined for each crop, crop model, GCM and RCP.…”

Section: Uncertainty and Confidence Assessmentmentioning

confidence: 99%

“…For example, according to Lobell et al (2008) maize yield in Southern Africa is projected to decline by about 30% compared with a decrease of only 2% for sorghum by 2030. Other studies show that sorghum will increase by a range of 19 to 72% across eastern and southern Africa (Zinyengere et al, 2014;Turner & Rao, 2013).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Impacts of Climate Variability and Change on Rainfed Sorghum and Maize: Implications for Food Security Policy in Tanzania

Msongaleli¹,

Rwehumbiza²,

Tumbo³

et al. 2015

JAS

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Concern about food security has increased because of a changing climate, which poses a great threat to food crop productivity. Climate change projections from the Coupled Model Inter-comparison Project phase 5 (CMIP5) and crop models were used to investigate the impacts of climate change on rain-fed cereal production. Calibrated and evaluated crop models simulated maize and sorghum yields over time periods and scenarios across central zone Tanzania with and without adaptation. Simulation outputs without adaptation showed predominant decrease and increase in maize and sorghum yields, respectively. The results showed that maize yields were predicted to decline between 1% and 25% across periods, representative concentration pathways (RCPs) and global circulation models (GCMs). However, sorghum yields were on average predicted to increase between 5% and 21%. Overall when adaptation is incorporated toward mid-century, yields are projected to increase for both crops. The yield projections variation between cereal crops highlights the importance of location and crop specific climate change impact assessments. Despite the uncertainties in predicting the impacts of climate change on rainfed crops, especially on cereals (maize and sorghum) which are important staple food crops in semi-arid Tanzania, the findings of this study enable policy makers to develop plans aimed at sustainable food security. In conclusion, the results demonstrate the presumption that sorghum productivity stands a better chance than maize under prospects of negative impacts from climate change in central zone Tanzania.

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Section: Introductionmentioning

confidence: 99%

Section: Uncertainty and Confidence Assessmentmentioning

confidence: 99%

See 1 more Smart Citation

Impacts of Climate Variability and Change on Rainfed Sorghum and Maize: Implications for Food Security Policy in Tanzania

Msongaleli¹,

Rwehumbiza²,

Tumbo³

et al. 2015

JAS

View full text Add to dashboard Cite

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“…The information is useful for management purposes in estimating irrigation water requirements, irrigation scheduling, and water delivery scheduling (Todorovic 2005). So far, there have been considerable research attempting to model the effects of climate change on global water resources and agriculture production (Thomson et al 2006;Mimi and Jamous 2010;Zhou et al 2012;Yao and Xu 2013;Samper et al 2015;Antonellini et al 2014;Prȃvȃlie et al 2014;Zinyengere et al 2014;Thevs et al 2015). However, food production not only consumes ''blue water'' which is withdrawn from water bodies and groundwater and used for irrigation, but also relies on ''green water'' (rainfed agriculture) (Falkenmark 2003;Liu and Savenije 2008).…”

Section: Introductionmentioning

confidence: 99%

Modeling water requirements of major crops and their responses to climate change in the North China Plain

2015

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The North China Plain (NCP) is one of the most important food production bases in China. However, its agriculture water resources are threatened by climate change. In this paper, the CROPWAT model is used to evaluate crop water requirement (CWR), crop green water requirement (CGWR), and crop blue water requirement (CBWR) for main crops in NCP (winter wheat, summer maize, cotton, millet, and soybean) with a spatial resolution of 5 arc-minute from 1961 to 2010. Their responses to future climate changes are investigated. The results show that the mean annual total CWR of the main crops during growing periods amounted to 114.68 km 3 a -1 in the past 50 years. More than 72 % of CWR to support NCP crop production is green water. The spatial distributions of CWR, CGWR, and CBWR are closely related to the planting areas and irrigation availability. Summer maize, millet, and soybean are high CGWR crops with proportions of above 84 %, while the lowest CGWR proportion is in winter wheat, 58.89 %. For climate change impacts in future, holding the crop planting system and irrigation conditions unchanged, it is projected that the total CWR in 2030s will require approximately 8.75-11.25 km 3 a -1 additional water. Results show that the CWR increase in 2030s is mainly due to the increase in temperature. Under the projected temperature in 2030s and the current rainfall scenario, total CWR, CGWR, and CBWR increments were 8.58, 1.76, and 6.82 km 3 a -1 , respectively. Nearly 80 % of the CWR increment is from the increase in CBWR. Therefore, agricultural water shortage crisis will further aggravate under future climate change scenarios in NCP, and effective water-saving measures must be taken to mitigate the negative effects of climate change.

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“…Assessing climate change impact on agricultural production are mostly undertaken at large spatial scales, missing out on local scale impacts and adaptation potential under which farmers operate (Zinyengere et al, 2014). Existing studies on regional climate modeling have mostly focused on southern Africa and West Africa (Hewitson and Crane, 2006;Stockdale et al, 2010;Paeth et al, 2011) with very few studies in Zambia.…”

Section: Introductionmentioning

confidence: 99%

Climate change impact on maize (Zea mays L.) yield using crop simulation and statistical downscaling models: A review

Chisanga¹,

Phiri²,

Chinene³

2017

Sci. Res. Essays

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Review of literature related to the impact of climate change on maize (Zea mays L.) yield using Global Climate Models (GCMs), statistical downscaling, and crop simulation (APSIM-maize-and-CERES-maize models) models are discussed. GCMs can simulate the current and future climatic scenarios. Crop yield projections using crop models require climate inputs at higher spatial resolution than that provided by GCMs. The computationally inexpensive statistical downscaling technique is widely used for this translation. Studies on regional climate modeling have mostly focused on Southern Africa and West Africa, with very few studies in Zambia. Additionally, the integrated use of climate and crop models have received relatively less attention in Africa compared to other parts of the world. Conversely, the AgMIP protocols have been implemented in Sub-Saharan Africa (SSA) (Ethiopia, Kenya, Tanzania, Uganda and South Africa) and South Asia (SA) (Sri Lanka). In Zambia, however, the protocols have not been applied at either regional or local scale. Applying crop and statistical downscaling models requires calibration and validation, and these are crucial for correct climate and crop simulation. The review shows that although uncertainties exist in the design of models, and parameters, soil, climate and management options, the climate would adversely affect maize yield production in SSA. The potential effect of climate change on maize production can be studied using crop models such as agricultural production simulator (APSIM) and decision support system for agrotechnology (DSSAT) models. There is need to use integrated assessment modeling to study future climate impact on maize yield. The assessment is essential for long-term planning in food security and in developing adaptation and mitigation strategies in the face of climate variability and change.

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Local impacts of climate change and agronomic practices on dry land crops in Southern Africa

Cited by 47 publications

References 38 publications

Impacts of Climate Variability and Change on Rainfed Sorghum and Maize: Implications for Food Security Policy in Tanzania

Impacts of Climate Variability and Change on Rainfed Sorghum and Maize: Implications for Food Security Policy in Tanzania

Modeling water requirements of major crops and their responses to climate change in the North China Plain

Climate change impact on maize (Zea mays L.) yield using crop simulation and statistical downscaling models: A review

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