TY - JOUR
T1 - Accounting for Weather Variability in Farm Management Resource Allocation in Northern Ghana
T2 - An Integrated Modeling Approach
AU - Adelesi, Opeyemi Obafemi
AU - Kim, Yean Uk
AU - Webber, Heidi
AU - Zander, Peter
AU - Schuler, Johannes
AU - Hosseini-Yekani, Seyed Ali
AU - MacCarthy, Dilys Sefakor
AU - Abdulai, Alhassan Lansah
AU - van der Wiel, Karin
AU - Traore, Pierre C.Sibiry
AU - Adiku, Samuel Godfried Kwasi
N1 - Publisher Copyright:
© 2023 by the authors.
PY - 2023/5
Y1 - 2023/5
N2 - Smallholder farmers in Northern Ghana face challenges due to weather variability and market volatility, hindering their ability to invest in sustainable intensification options. Modeling can help understand the relationships between productivity, environmental, and economical aspects, but few models have explored the effects of weather variability on crop management and resource allocation. This study introduces an integrated modeling approach to optimize resource allocation for smallholder mixed crop and livestock farming systems in Northern Ghana. The model combines a process-based crop model, farm simulation model, and annual optimization model. Crop model simulations are driven by a large ensemble of weather time series for two scenarios: good and bad weather. The model accounts for the effects of climate risks on farm management decisions, which can help in supporting investments in sustainable intensification practices, thereby bringing smallholder farmers out of poverty traps. The model was simulated for three different farm types represented in the region. The results suggest that farmers could increase their income by allocating more than 80% of their land to cash crops such as rice, groundnut, and soybeans. The optimized cropping patterns have an over 50% probability of increasing farm income, particularly under bad weather scenarios, compared with current cropping systems.
AB - Smallholder farmers in Northern Ghana face challenges due to weather variability and market volatility, hindering their ability to invest in sustainable intensification options. Modeling can help understand the relationships between productivity, environmental, and economical aspects, but few models have explored the effects of weather variability on crop management and resource allocation. This study introduces an integrated modeling approach to optimize resource allocation for smallholder mixed crop and livestock farming systems in Northern Ghana. The model combines a process-based crop model, farm simulation model, and annual optimization model. Crop model simulations are driven by a large ensemble of weather time series for two scenarios: good and bad weather. The model accounts for the effects of climate risks on farm management decisions, which can help in supporting investments in sustainable intensification practices, thereby bringing smallholder farmers out of poverty traps. The model was simulated for three different farm types represented in the region. The results suggest that farmers could increase their income by allocating more than 80% of their land to cash crops such as rice, groundnut, and soybeans. The optimized cropping patterns have an over 50% probability of increasing farm income, particularly under bad weather scenarios, compared with current cropping systems.
KW - CLEM
KW - Northern Ghana
KW - SIMPLACE
KW - bio-economic farm model
KW - integrated model
KW - mixed cropping system
KW - weather risk
UR - http://www.scopus.com/inward/record.url?scp=85159343975&partnerID=8YFLogxK
U2 - 10.3390/su15097386
DO - 10.3390/su15097386
M3 - Article
AN - SCOPUS:85159343975
SN - 2071-1050
VL - 15
JO - Sustainability (Switzerland)
JF - Sustainability (Switzerland)
IS - 9
M1 - 7386
ER -