Crop Production Risk as a Factor in Sustainable Land Management

Author(s):  
J. Dumanski ◽  
R. de Jong ◽  
A. Bootsma ◽  
M. Brklacich
Author(s):  
Nancy McCarthy ◽  
Talip Kilic ◽  
Josh Brubaker ◽  
Siobhan Murray ◽  
Alejandro de la Fuente

Abstract Climate change is predicted to increase the frequency of extreme weather events, increasing the vulnerability of smallholder farmers dependent on rain-fed agriculture. We evaluate the extent to which farmers in Malawi suffer crop production losses due to extreme weather, and whether sustainable land management (SLM) practices help shield crop production losses from extreme events. We use a three period panel dataset where widespread floods and droughts occurred in separate periods, offering a unique opportunity to evaluate impacts using data collected immediately following these events. Results show that crop production outcomes were severely hit by both floods and droughts, with average losses ranging between 32–48 per cent. Legume intercropping provided protection against both floods and droughts, while green belts provided protection against floods. However, we find limited evidence that SLM adoption decisions are driven by exposure to weather shocks; rather, farmers with more productive assets are more likely to adopt.


Author(s):  
Julian Dumanski ◽  
Samuel Gameda ◽  
Christian Pieri ◽  

2021 ◽  
Vol 13 (11) ◽  
pp. 6365
Author(s):  
Alelgn Ewunetu ◽  
Belay Simane ◽  
Ermias Teferi ◽  
Benjamin F. F. Zaitchik

Sustainable land management (SLM) is a leading policy issue in Ethiopia. However, the adoption and continuous use of SLM technologies remain low. This study investigates the interrelationship of adopted SLM technologies and key factors of farmers’ decisions to use SLM technologies in the North Gojjam sub-basin of the Upper Blue Nile. The study was based on the investigation of cross-sectional data obtained from 414 randomly selected rural household heads, focus group discussions, and key informant interviews. Descriptive statistics and Econometric models (i.e., Multivariate Probit and Poisson regression) were used to analyze quantitative data, while a content analysis method was used for qualitative data analysis. Results indicate that at least one type of SLM technology was implemented by 94% of farm households in the North Gojjam sub-basin. The most widely used technologies were chemical fertilizer, soil bund, and animal manure. Most of the adopted SLM technologies complement each other. Farm size, family size, male-headed household, local institutions, perception of soil erosion, livestock size, total income, and extension service increased the adoption probability of most SLM technologies. Plot fragmentation, household age, plot distance, off-farm income, market distance, and perception of good fertile soil discourage the adoption probability of most SLM technologies. To scale up SLM technologies against land degradation, it is important to consider households’ demographic characteristics, the capacity of farm households, and plot-level related factors relevant to the specific SLM technologies being promoted.


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