Exploring future changes in smallholder farming systems by linking socio-economic scenarios with regional and household models
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Citations
Climate-smart agriculture for food security
Land system science and sustainable development of the earth system: A global land project perspective
Drivers of household food availability in sub-Saharan Africa based on big data from small farms
Livestock and the Environment: What Have We Learned in the Past Decade?
Climate-smart agriculture global research agenda: scientific basis for action
References
The conditions of agricultural growth
The DSSAT cropping system model
Livestock production: recent trends, future prospects
Scale and Cross-Scale Dynamics: Governance and Information in a Multilevel World
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Frequently Asked Questions (17)
Q2. What are the future works in this paper?
The authors set out to study how agricultural systems in the Kenyan highlands might evolve as a 29 result of drivers of change that could create opportunities for intensification and 30 diversification for different types of farming systems. As change occurs, it 11 is essential to have the ability to study what may happen to different types of households, 12 how they might react and adapt or not, what the costs associated with these adaptations 13 could be, who will be the winners and the losers, what kinds of robust interventions may 14 be suitable for different types of farming systems, and what could be the socio-economic 15 and environmental trade-offs if these were to be implemented. Rather 10 than providing a two-dimensional “ low-medium-high investment ” set of scenarios, the 11 scenario set included equitable versus inequitable growth as another dimension, one that 12 can be expressed as spatial differentiation. The scenarios have furthermore provided a long-term 14 future context beyond present-day conditions in which evolution of smallholder systems 15 can be simulated over multiple iterations.
Q3. How much of the household income is obtained from work on other farms?
Some off-farm income is obtained through wages received from work on other farms by 4 the household head and his wife, accounting for 18.8% of total household income.
Q4. What is the role of policy experts in the development of socio-economic 1 scenarios?
The involvement of regional experts in the development of socio-economic 1 scenarios has enabled us to explore change in smallholder systems under different policy-2 relevant conditions that incorporate both desired futures as expressed by government 3 strategies as well as less optimistic, more challenging futures.
Q5. What is the metric used to determine the distribution of farming systems?
The map with farming systems distribution at the base year, the set of static and 17 dynamic spatial data layers, and the logit models that relate the probability of occurrence 18 of farming systems to location characteristics were used as input to a spatial and temporal 19 model of farming systems dynamics.
Q6. Why are the costs of livestock keeping more important than those due to agriculture?
5 Livestock keeping costs are more important than those due to agriculture, because of little 6 hiring of labour for cropping activities.
Q7. What were the income elasticity demands for maize?
Income elasticity 8 demands were derived from USDA (2013); the authors used 0.58, 0.81, 0.9 and 1.6 for maize, 9beans, milk and tea respectively.
Q8. How many feedbacks have been considered in the iterative simulations?
in their iterative simulations the authors have 16 not considered feedbacks from the models to the scenarios which might result in cross-17 level system shifts (Kinzig, 2006), such as regional land-use change patterns prompting 18 changes in national government policies.
Q9. Why is the move towards more specialized dairy activities near cities consistent with previous studies?
This move towards more specialized dairy activities near cities is consistent 24 with previous studies that showed that dairy is profitable near cities despite high farming 25 costs, because of high demand for milk translating into a higher milk price.
Q10. How many ha is dedicated to crops?
Farm size is the biggest of all the case studies with 4.8 ha 16 (all owned), from which 3.7 ha is dedicated to crops and the rest to cut-and-carry 17 pastures.
Q11. How many representative case study farms were selected?
6 7Description of Case Study Households 8 9From the final 18 household groups (six classes and three sub-groups in each), a 10 representative case study farm was selected.
Q12. What is the way to predict the optimal occurrence of a certain farming system?
As spatial variables are changing over time, the optimal occurrence 29 of a certain farming system for a given location will change (Table 7).
Q13. How much money is available to start the farming of passion fruit?
It is important to notice that the farmer only starts growing 16 passion fruit if cash is available to start this activity, which has high initial costs.
Q14. What is the scenario for a dairy farmer?
The “optimal base scenario” slightly 31increases the amount of land used for food and cash crops (maize and beans) at the 1 expense of the grassland area, but maintains the dairy activity.
Q15. Why is the evolution of land size different from the other case studies?
It is important to notice that, due to the peri-urban location of the farm, close to 4 the capital city Nairobi where population densities are higher and where there is a higher 5 demand for land for non-agricultural activities, the evolution of land size is opposite to 6 that observed for the other case studies.
Q16. How much of the study area was projected to change?
27 28 Inequitable growth scenario 29 About 15% of the surface area of the study area was projected to change for this scenario, 30 the lowest value of the range predicted in Figure 5.
Q17. What is the effect of the procedure on the local suitability of the farming system?
Through this procedure it is possible that the local suitability based on the 3 location factors is overruled by the iteration variable due to the differences in regional 4 demand.