The Impacts (I) of climate change on crops were estimated by projecting the likely future changes to Angola’s climate, and then analysing the effects of those projected climate changes on economically important crops. Firstly, the potential future changes to Angola’s climate were computed through analysis of 29 General Circulation Models (GCMs) downloaded from the AgMERRA dataset 2, based on the methods described by Ramirez-Villegas et al (2013) 3. Future climate changes were computed assuming the scenario of ‘RCP 8.5’ (where ‘RCP 8.5’ refers to one of four hypothetical scenarios for future global greenhouse gas emissions proposed by the Intergovernmental Panel on Climate Change).
However, the study does find that climate change is likely to result in multiple negative effects on smallholder farmers in the study area, through disruption of familiar seasonal trends, increased water and heat stress and reduced growing season.
In the absence of adequately detailed socio-economic data, this study was unable to undertake a rigorous assessment of the adaptive capacities of farming households in each province – it is anticipated that the finalisation of Angola’s next agricultural census study will contribute useful data that will support future initiatives focused on adaptive capacity and vulnerability to climate change.
The analyses presented in this study are intended to provide an illustrative comparison of the potential effects of future climate change on production of economically important crops, as well as the differential impacts of climate change on agricultural households in each of Angola’s 18 provinces. For each of the crops considered in this study (maize, sorghum, beans, groundnuts and cassava) the relative impacts of climate change on crop production are considered at the Province level, aiming to identify those Provinces which are likely to be most or least vulnerable to climate change impacts on the given crop.
This analysis was used to generate predictions of the effect of climate change across Angola, comparing the historical baseline (the average climate for the period 1980– 2010) to the Mid-Century future (2050, the average climate for the period 2040–2069). In particular, the analysis compares the climatic variables of Mean Monthly Precipitation (i.e. the average precipitation for each month), Monthly Mean Temperature and Monthly Minimum Temperature (Tmin).
Analyses of current and future crop suitability were generated using the Food and Agriculture Organisation’s EcoCrop Suitability model4 combined with the most recent statistics available for annual crop production and demographics.
The EcoCrop model estimates the suitability of a given crop to the defined environmental conditions based on the known preferences of each crop such as: i) minimum, optimum and maximum temperature; ii) minimum, optimum and maximum monthly rainfall; and iii) minimum and maximum growing period. Therefore, EcoCrop defines the area of suitability for a given crop based on whether there are adequate climatic conditions (temperature and precipitation) within the growing season and calculates the climatic suitability of the resulting interaction between rainfall and temperature. Readers are referred to the full project report and the work of Ramirez- Villegas et al (2013) for detailed description of methodology.
A suitability index score, ranging from 0 – 1, indicates the relative suitability of a given area for each of the crops assessed (where a
suitability score of 0 is considered to be totally unsuitable, a score of
1 is considered excellent, with a continuous spectrum of marginal, moderate and good suitability types in between). In this study, analyses of the distribution of suitable areas for a given crop allows for the estimation of the total suitable production area, as well as the average suitability index score, within each of Angola’s 18 provinces. The EcoCrop approach also allows for map-based visualisations of crop suitability zones across the country. The use of colour-coded maps to depict the distribution of various categories of crop suitability index scores can be used to demonstrate the distribution of crop-suitable areas, as demonstrated in Figure 1.
The comparison of maps of ‘Historical’ and ‘Future’ distribution of crop suitability can be used to estimate the potential changes to the size and relative productivity of crop-suitable areas. In addition, this approach allows for the identification of specific areas which are likely to undergo positive or negative changes (anomalies) as a result of climate change, and may
be used to inform decision-making such as identification of climate- vulnerable areas and value chains to be prioritised for additional support. The potential impacts of climate change on each crop were estimated based on:
• the changes to total suitable area (km2) and average suitability index score between the historical baseline and ‘mid-century’ future5;
• and estimated historical crop production in each Province, derived from national agricultural production statistics 6.
The potential impacts of climate change on each crop can be quantified in several ways, for example, in terms of changes to “production per capita”, “production per household” and “production per Province”. It should be emphasised that no further calibration or validation of EcoCrop analyses was carried out in support of this study and that results should be considered as indicative guidelines only, to inform additional local-level decision- making and further research.
The predicted changes in Mean Monthly Temperature (TMean) during the period from ‘Historical’ to ‘Future 2050’ timepoints indicate that climate change will result in consistent increases in Mean Temperature across spatial and temporal dimensions in Angola. A common prediction across each of the country’s provinces is that TMean will increase in all provinces during the period from ‘Historical’ to ‘Future 2050’ timepoints by at least 1.5°C. The hottest months of October and November are predicted to increase by 2.1–2.4°C, relative to a Historical average of 24°C. Similar increases of 1.8–2.3°C are predicted for all other months of the year, including the peak summer months that support the rainfed agricultural season (up until March/ April) as well as the colder winter months of May–August.
The overall effect of increased temperatures is likely to result in complex impacts on the agricultural sector, particularly when considered in combination with the predicted decreases and delayed timing of rainfall. The large increases in temperature (2.1–2.4°C) in the months of September–November will increase crop water demand and evapotranspiration losses of water from agricultural soils, coinciding with the reduced rainfall predicted for the same months.
This effect is likely to increase the risks of crop failure as a result of inadequate or erratic rainfall during the establishment of rainfed crops. Furthermore, the increased average temperatures are likely to include increased frequency or severity of heat waves and unusually hot days, further contributing to evapotranspirative losses of water and crop stress.
A possible additional effect of the increase in winter temperatures may be to increase the feasibility and productivity of irrigated agriculture during the dry, cooler winter (particularly in high altitude areas). Increased winter temperatures may result in suitably warm conditions for off-season irrigated production of staples such as maize and beans, as well as various horticultural crops such as tomatoes and other assorted vegetables.
Taken cumulatively over the entire growing season, the combination of reduced rainfall and increased temperatures are likely to reduce agricultural production, either as a result of decreased yield or outright crop failure, particularly in the case of heat- and drought-sensitive crops such as maize.
The predicted changes in mean monthly precipitation from the historical baseline to the mid- century (2050) future indicate that climate change will result in complex changes in rainfall across Provinces and months (see Table 3). Province-level summaries of predicted monthly changes in precipitation can be found in the supplementary Appendix.
A common prediction across each of the country’s 18 Provinces is that mean monthly precipitation and total annual precipitation will be reduced in all Provinces during the period from ‘baseline’ to ‘Future 2050’ timepoints. Total rainfall at the onset of the rainy season in the months of September and October is predicted to be reduced from 21 to 15 mm/month and 71 to 49 mm/
month (total reduction of rainfall of 6 mm and 22 mm, respectively). Further reductions in monthly precipitation are predicted for the summer rainy season months from November–March ranging from 5 to 15 mm/month. The overall effect of these reductions to monthly precipitation throughout the rainy season is to reduce the total seasonal rainfall for the period October–April by 8.5 %, from 765 mm/season to 700 mm/season.
An additional effect, which is likely to vary on an interannual basis as well as spatially within each season, is the effective timing of the onset of rainfall at the start of the growing season. The average reduction in national rainfall predicted for the start of the rainy season is likely to vary between
provinces and Angola’s agro- ecological zones but in some cases may result in inadequate rainfall to support effective establishment of crops during the period which is traditionally associated with the start of the growing season.
These analyses indicate that climate change may delay the onset of rainfall relative to the traditional agricultural calendar, in turn resulting in changes to the timing of various agricultural activities such as field preparation and sowing of seed. The majority of the rainfed agricultural growing season is characterised by monthly rainfall deficits and is likely to result in fundamental changes to local crop choices and agricultural practices by the year 2050.
Beans are widely grown as a staple subsistence crop across most of Angola’s provinces, with widespread areas of moderate, good or excellent suitability, and the spatial extent of suitability is only limited by the arid southern and coastal borders.
Climate change is projected to result in a reduction in total area suitable for bean production, as well as a reduction in the average suitability index scores across most of Angola. Most provinces are likely to undergo a transition from moderate/good suitability to marginal/very marginal suitability for beans.
The provinces worst affected are likely to include Luanda, Cunene and Cuando Cubango, where the future suitability scores are projected to be reduced to marginal or very marginal. Other provinces that are likely to experience significant reductions in area suitable for beans include Bengo and Namibe.
In terms of %change production per person, it is predicted that households will experience a decrease in annual production ranging from -2% in Huambo, up to -60% in Cunene and Luanda (relative to a very low baseline production in the latter provinces). In terms of the total impact of climate change on the annual production of agricultural households, the predicted decrease in annual production may range from 0.05 kg up to 15 kg per household in most provinces.
The provinces anticipated to be most negatively impacted by total reduced production at the household level include Namibe, Bie, Bengo and Moxico (resulting in lost production ranging from 20-50 kg per household per annum).
At the provincial level, the greatest losses of production are predicted in Benguela, Huíla and Bié, each province may experience shortfalls of over 6,000 tonnes each. In total, it is estimated that the annual production of beans across all provinces will be reduced by 42,351 tonnes, representing a significant loss of household income and staple food production. The total replacement cost for lost production of beans, at a national level, is estimated to be USD 38.1 million10.
Table. Impact of predicted climate changes on the future production area (ha) and total annual production (tonnes/ annum) of Beans (Phaseolus vulgaris) in each province of Angola at Baseline (Historical) and future Mid-Century periods, and resultant changes to annual production per capita, per household and per province.
At the household level, the provinces which will experience the most severe negative impacts on per capita production are Luanda (1), Cunene
(2) and Cuanda Cubango (3). At the province level, the regions that will experience the most severe negative impacts on total production are Benguela (1), Huíla (2) and Bié (3).