Diversification Strategies and Adaptation Deficit: Evidence from Rural Communities in Niger

Asfaw, Solomon; Pallante, Giacomo; Palma, A.

doi:10.1016/j.worlddev.2017.09.004

Cited by 105 publications

(107 citation statements)

References 132 publications

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“…Aggregate agricultural productivity in this study is measured by the index of the ratio of the total value of farm output (measured in naira values), to the value of total inputs (expressed in naira) used in farm production. This approach is consistent with the reports of other researchers (Anyanwu, ; Asfaw, Palma, & Lipper, ). Results of the diagnostic test (indicator of the test) presented from Table revealed that estimators adopted for estimation techniques were able to correct the commonly twin biases of simultaneity and selection usually related with conventional estimators such as OLS and fixed effects panel regression (FE) and thus give a robust estimates.…”

Section: Estimations From Tfp Modelsupporting

confidence: 92%

Doggedness of small farms and productivity among smallholder farmers in Nigeria: Empirical linkage and policy implications for poverty reduction

et al. 2019

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Small farms have shown spirited livelihood over time as they have significantly influenced agricultural production and rural poverty reduction. Small farm characterizes agriculture in Nigeria and seems to be persisting. What is the relationship between these persisting small farms and productivity? Multistage sampling technique was engaged in choosing the samples desirable for this study and where 880 farming households' data across the four regions of Nigeria were used to examine this linkage. Data were analyzed by descriptive statistics, cross‐tabulation analysis, and Total Factor Productivity analysis. Majority of the respondents derived livelihood in petty farming, but income received is somewhat insignificant. Farming households with less than 2 ha of agricultural land are poorer (30.1%) and used family/communal land for farming purposes. Total Factor Productivity analysis indicated 29% of these farmers operated on a low productivity out of which 65% of them identified poor, hence confirm the correlation between small farms and low productivity. About 67% of the poor households with low productivity augment household income from nonfarm rural acridities. Productivity has a significant effect on small farms doggedness at p < .05 and a deviation of 3.21. Thus, 1% increase in productivity would lead to 3.21% decrease in small farms persistence. Hence, the empirical analysis of persistence of small farms and the productivity models has thus shown that there exists a causal relationship. The simultaneity test between small farms and productivity revealed a direct link. This suggests that policies that can expedite efficient rural service delivery, interactions of agricultural production systems, and rural livelihoods can influence income increase and can also do same to improve productivity. Similarly, policies that promote the development of the rural nonfarm sector are crucial to help increase income available for farming and hence, improve smallholders' well‐being, preventing small farms from being a pool of the poor.

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Section: Estimations From Tfp Modelsupporting

confidence: 92%

Doggedness of small farms and productivity among smallholder farmers in Nigeria: Empirical linkage and policy implications for poverty reduction

et al. 2019

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“…These two types of households are often discussed independently of one another because they are typically separated by larger geographical, ethnic and cultural divides [25]. The studies on the LD of farmer households have tended to examine crop diversification and engagement in non-agricultural activities [26][27][28][29][30]. Herders have been diversifying into sedentary agriculture and non-agricultural activities to cope with socio-environmental risks, reduce rangeland pressure and increase income [18,20,31,32].…”

Section: Introductionmentioning

confidence: 99%

Characteristics and Determinants of Livelihood Diversification of Different Household Types in Far Northwestern China

Dai

Fan

et al. 2019

Sustainability

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Livelihood diversification is beneficial to mitigate economic and environmental risks and to improve livelihood sustainability and regional sustainable development. Unsettled herder households (UHH), settled herder households (SHH) and farmer households (FH) are different household types in far northwestern China whose livelihood diversification has not been fully explored. By applying a framework of livelihood diversification, this paper presents a comparative analysis of the characteristics and determinants of the diversification of the three household types. The results show that livelihood assets have been unequally distributed, with FH possessing the least assets; however, FH are better than UHH and SHH in the diversification of livelihood activities. Agriculture remains the most important livelihood source. The high-income groups of the three household types have a higher number of livelihood activities but do not necessarily hold an advantage in equality of livelihood activities. Labor capacity and income are positively related to the number of livelihood activities for the three household types. Livestock size is negatively associated with the number of activities of herders. Moreover, age and subsidy have negative impacts on the number of activities for UHH. Based on the findings, we provide policy suggestions on livelihood enhancement and sustainable and effective development of pastoral regions.

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“…Following Asfaw et al [7], gridded monthly precipitation dataset extracted from GPCC Full Data Monthly Product Version 2018, produced by Global Precipitation and Climatology Center (GPCC) and available on 0.5 • × 0.5 • grid [64], were used along with temperature data from Climate Research Unit (CRU TS 4.21), as described in Harris et al [65]. The GPCC Full Data Monthly Product is the most comprehensive gridded precipitation dataset available today, and is based on measurements from over 80,000 stations worldwide.…”

Section: Data Compilationmentioning

confidence: 99%

“…The GPCC Full Data Monthly Product is the most accurate in situ precipitation reanalysis data set of GPCC, and aims to support regional climate monitoring, model validation, climate variability analyses, and water resources assessment studies (e.g., Becker et al [66], Zeise et al [67]). It is also noted to provide representative coverage in Ethiopia [7], and as such, was deemed suitable for this study.…”

Section: Data Compilationmentioning

confidence: 99%

“…The soil moisture at any time can be affected by precipitation in previous months or seasons, and is also affected by other factors, including but not limited to soil type and atmospheric temperature. In Ethiopia, while many farmers grow crops during the Meher growing season that coincides with the longer Kerimt (June-October) rainy season, the shorter Belg (February-May) rains often provide the soil moisture that is necessary for tillage and planting activities, and also improve pastures for livestock [7]. Therefore, lagged relationships between agricultural and meteorological drought indicators are of interest in Ethiopia as well.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Meteorological- and Agriculture-Related Drought Indicators across Ethiopia

Tsige

Uddameri

Forghanparast

et al. 2019

Water

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Meteorological drought indicators are commonly used for agricultural drought contingency planning in Ethiopia. Agricultural droughts arise due to soil moisture deficits. While these deficits may be caused by meteorological droughts, the timing and duration of agricultural droughts need not coincide with the onset of meteorological droughts due to soil moisture buffering. Similarly, agricultural droughts can persist, even after the cessation of meteorological droughts, due to delayed hydrologic processes. Understanding the relationship between meteorological and agricultural droughts is therefore crucial. An evaluation framework was developed to compare meteorologicaland agriculture-related drought indicators using a suite of exploratory and confirmatory tools. Receiver operator characteristics (ROC) was used to understand the covariation of meteorological and agricultural droughts. Comparisons were carried out between SPI-2, SPEI-2, and Palmer Z-index to assess intraseasonal droughts, and between SPI-6, SPEI-6, and PDSI for full-season evaluations. SPI was seen to correlate well with selected agriculture-related drought indicators, but did not explain all the variability noted in them. The correlation between meteorological and agricultural droughts exhibited spatial variability which varied across indicators. SPI is better suited to predict non-agricultural drought states than agricultural drought states. Differences between agricultural and meteorological droughts must be accounted for in order to devise better drought-preparedness planning.

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Diversification Strategies and Adaptation Deficit: Evidence from Rural Communities in Niger

Cited by 105 publications

References 132 publications

Doggedness of small farms and productivity among smallholder farmers in Nigeria: Empirical linkage and policy implications for poverty reduction

Doggedness of small farms and productivity among smallholder farmers in Nigeria: Empirical linkage and policy implications for poverty reduction

Characteristics and Determinants of Livelihood Diversification of Different Household Types in Far Northwestern China

Comparison of Meteorological- and Agriculture-Related Drought Indicators across Ethiopia

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