10.5.1 Climate change impact on quality and yield of crops modelling results 10.5.1.1 APSIM crop modelling results
Error! Reference source not found.68 shows the projected yield for maize for the intermediate future (2046 – 2065) in the Carolina area, derived from APSIM calculations. The figures are expressed as percentages of the yield used in the base analysis.
Climate data from four GCMs were applied in the APSIM modelling to project intermediate future yield for wheat. One model projects an average decrease of 25% while three models project an increase in average yield of approximately 10%.
Figure 68: Projected yield (% of base yield) [2046 – 2065] for maize in Carolina area based on APSIM calculations
10.5.1.2 CCCT modelling results
When breaching a critical climate threshold, the impact on yield and/or quality can be either positive or negative. The critical crop climate thresholds for different crops were collected during a workshop which was attended by various role-players, including amongst others, industry experts and the case study farmer.
Table 77 shows the critical climate thresholds for maize, soybeans and sugar beans.
Table 77: Critical climate thresholds for maize, soybeans and sugar beans
Source: Carolina workshop and expert group discussions (2012)
Refer to
Table 7777 and the Appendix for threshold penalty weights for yield and quality. The critical thresholds for wheat can be interpreted as follows:
Maize
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Tmnx < -5 °C in Dec – daily minimum temperature of less than -5 °C results in a -5% reduction in yield.
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Tmxd > 35 °C for 3+ days Jan-Feb – maximum daily temperatures of 35 °C for 3 days or more during January and February have a negative impact on yield (-5%).
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Tmnd < 12 °C in Nov – minimum daily temperatures of less than 12 °C negatively impact on yield (-1%).
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Rainfall < 40 mm in Oct – less than 40 mm of rain during the month of October has a negative impact on yield (-5%).
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Rainfall < 60 mm in Nov - less than 60 mm of rain during the month of November has a negative impact on yield (-5%).
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Rainfall < 80 mm in Dec - less than 80 mm of rain during the month of December has a negative impact on yield (-5%).
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Rainfall < 100 mm in Jan - less than 100 mm of rain during the month of January has a negative impact on yield (-15%).
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Rainfall < 60 mm in Feb - less than 60 mm of rain during the month of February has a negative impact on yield (-5%).
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Rainfall > 80 mm in Feb – more than 80 mm of rain during the month of February has a positive impact on yield (+10%).
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Rainfall > 80 mm in Mar – more than 80 mm of rain during the month of March has a positive impact on yield (+10%).
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Rainfall > 160 mm in Feb-Mar – more than 160 mm of rain during February and March has a positive impact on yield (+10%).
Soybeans
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Tmnd < -5 °C Oct – Jan – daily minimum temperatures less than -5 °C during October to January impact negatively on yield (-50%).
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Tmxd > 28 °C for 3+ days in mid Jan-Feb – maximum daily temperatures in excess of 28 °C for 3 days or more from mid-January to end of February have a negative impact on yield (-5%).
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Average temperature > 25 °C in Nov – average temperature in excess of 25 °C impacts negatively on yield (-10%).
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Tmxd > 35 °C Jan – maximum daily temperatures in excess of 35 °C during the month of January have a negative impact on yield (-10%).
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Tmxd > 30 °C with low RH in Jan - maximum daily temperatures in excess of 30 °C with low relative humidity during the month of January have a negative impact on yield (-10%).
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Rainfall < 50 mm in Nov - less than 50 mm of rain during the month of November has a negative impact on yield (-10%).
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Rainfall < 80 mm in Nov - less than 80 mm of rain during the month of December has a negative impact on yield (-10%).
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Rainfall < 100 mm in Jan - less than 100 mm of rain during the month of January has a negative impact on yield (-10%).
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Rainfall < 60 mm in Feb - less than 60 mm of rain during the month of February has a negative impact on yield (-10%).
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Rainfall < 40 mm in Jan - less than 40 mm of rain during the month of January has a negative impact on yield (-10%).
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Rainfall > 60 mm and < 150 mm in Feb – total rainfall of more than 60 mm but less than 150 mm during the month of February has a positive impact on yield (+5%).
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Rainfall > 60 mm and < 150 mm in Mar - total rainfall of more than 60 mm but less than 150 mm during the month of March has a positive impact on yield (+5%).
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Rainfall > 120 mm and < 300 mm in Feb-Mar - total rainfall of more than 120 mm but less than 300 mm during February and March has a positive impact on yield (+5%).
Sugar beans
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Tmnd < -5 °C Oct-Jan – daily minimum temperatures less than -5 °C during October to January impact negatively on yield (-50%).
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Tmxd > 26 °C for 3+ days in mid Jan-Feb – maximum daily temperatures in excess of 26 °C for 3 days or more from mid-January to end of February have a negative impact on yield (-10%).
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Tmxd > 30 °C with low RH in Jan - maximum daily temperatures in excess of 30 °C with low relative humidity during the month of January have a negative impact on yield (-10%).
-
Tmxd > 30 °C Jan – maximum daily temperatures in excess of 30 °C during the month of January have a negative impact on yield (-10%).
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Rainfall < 50 mm in Nov - less than 50 mm of rain during the month of November has a negative impact on yield (-10%).
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Rainfall < 80 mm in Nov - less than 80 mm of rain during the month of December has a negative impact on yield (-10%).
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Rainfall < 100 mm in Jan - less than 100 mm of rain during the month of January has a negative impact on yield (-10%).
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Rainfall < 60 mm in Feb - less than 60 mm of rain during the month of February has a negative impact on yield (-5%).
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Rainfall > 140 mm Jan - total rainfall of more than 140 mm during the month of January has a positive impact on yield (+5%).
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Rainfall > 60 mm and < 100 mm in Feb - total rainfall of more than 60 mm but less than 100 mm during the month of February has a positive impact on yield (+5%).
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Rainfall > 60 mm and < 100 mm in Mar - total rainfall of more than 60 mm but less than 150 mm during the month of March has a positive impact on yield (+5%).
Error! Reference source not found. shows the CCCT modelling results for five different GCMs for the present and intermediate future (2046 – 2065). The values are 20-year average values for the different models. All five models project an average increase in yield of approximately 10%. This result correlates to a large extent with the APSIM crop modelling results where three out of four models projected similar increases in average yield.
Table 78: CCCT modelling yield projections for maize in the Carolina area
10.5.2 Adaptation strategies available
Adaptation options for the Carolina area can be divided in two categories, namely changes in:
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Cropping systems
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Production practices
10.5.2.1 Cropping systems (crop rotation)
Current cropping systems are maize-soybeans-maize-soybeans and maize-sugar beans-maize-sugar beans combined with beef and mutton production. An alternative cropping system adapted for the region to be included in the integrated model is maize-maize-maize-maize (mono system).
10.5.2.2 Production practices
Adaptations options include conservation agricultural production practices versus conventional production practices.
10.5.2.3 Other adaptation strategies (not included in the model)
The following are a list of adaptation strategies debated in the group discussions, but not included in the integrated climate change model:
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Narrower row width (for better moist conservation)
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More short growers (access to genetics is a problem)
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Moisture management is very important
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Grain sorghum and sunflower production as alternatives (to be researched).
10.5.3 Financial vulnerability assessment results
Table 7979 summarises the financial ratios of the different climate scenarios that were modelled.
Table 79: Financial assessment results for Carolina case study
The modelling results for Carolina case study (20% start-up debt ratio) can be interpreted as follows:
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An IRR of 5% is projected under the present climate scenario. When intermediate climate scenarios are imposed on the model, the IRR increases to respectively 6% for the CCCT model and 7% for the ACM model. The inclusion of adaptation strategies tends to have a positive effect on profitability with the IRR increasing to 9% (CCCT) and 12% (ACM).
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A NPV of R7.8 million is projected under present climate conditions. For intermediate climate scenarios a NPV of R21.8 million for the CCCT model and R29.8 million for the ACM model are projected. Both these projections are positively influenced by the inclusion of adaptation strategies in the model. A NPV of R52 million is projected for the CCCT model and a NPV of R91 million for the ACM model. The impact of intermediate climate projections tends not to have a negative impact on profitability and return on investment. The inclusion of adaptation strategies can ultimately put the farming system in a better position than the current conventional system under present climate scenarios.
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A cash flow ratio of 133% is projected under present climate conditions. This ratio, however, declines marginally to 143% (CCCT model) and 150% (ACM) when intermediate climate scenarios are imposed on the model. Both models show an improvement in cash flow ratio when adaptation strategies are included in the model (CCCT model = 163%, ACM model = 186%). The adoption of conservation agriculture principles seems to contribute to profitability in the Carolina area.
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A highest debt ratio of 15% is projected under present climate scenarios. When intermediate climate scenarios are imposed on the model, the highest debt ratio increases to 14% (CCCT model) and 12% (ACM model). The inclusion of adaptation strategies positively influences the highest debt ratio to 12% and 5% for the CCCT model and the ACM model respectively. All these ratios are well within acceptable financing norms.
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A highest debt level of R17.6 million is projected under present climate conditions. This is the starting debt level for all scenarios and also the highest for the 20-year projection period.
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Similar to the Moorreesburg case study, the Carolina case study farm already converted to the more sustainable cropping system. The best adaptation strategy for the region is also to convert to conservation agriculture.
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