xp减肥农村扶贫脱贫中英文
英文
Effective alleviation of rural poverty depends on the interplay between productivity, nutrients,
water and soil quality
Sonja Radosavljevic，L. JamilaHaider，
Steven J.Lade，MajaSchlüter
李健龙Abstract
Most of the world's poorest people come from rural areas and depend on their local ecosystems for food production. Recent rearch has highlighted the importance of lf-reinforcing dynamics between low soil quality and persistent poverty but little is known on how they affect poverty alleviation. We investigate how the intertwined dynamics of houhold asts, nutrients (especially phosphorus), water and soil quality influence food production and determine the conditions for escape from poverty for the rural poor. We have developed a suite of dynamic, multidimensional poverty trap models of houholds
that combine economic aspects of growth with ecological dynamics of soil quality, water and nutrient flows to analyze the effectiveness of common poverty alleviation strategies such as intensification through agrochemical inputs, diversification of energy sources and conrvation tillage. Our results show that (i) agrochemical inputs can reinforce poverty by degrading soil quality, (ii) diversification of houhold energy sources can create possibilities for effective application of other strategies, and (iii) quencing of interventions can improve effectiveness of conrvation tillage. Our model-bad approach demonstrates the interdependence of economic and ecological dynamics which preclude blanket solution for poverty alleviation. Stylized models as developed here can be ud for testing effectiveness of different strategies given biophysical and economic ttings in the target region.
Keywords：Poverty trap，Dynamical system，Multistability，Agroecosystem，Phosphorus，Soil quality
1. Introduction
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How to alleviate global poverty and eradicate hunger in places with low agricultural
插入批注快捷键productivity are among humanity's greatest challenges. The concept of poverty traps as situations characterized by persistent, undesirable and reinforcing dynamics (Haider et al., 2018) is increasingl
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y being ud to understand the relationship between persistent poverty and environmental sustainability (Barrett and Bevis, 2015, Barrett and Constas, 2014, Lade et al., 2017). How poverty and environmental degradation are conceptualized and reprented in models can inform development interventions and thereby influence the effectiveness of tho interventions (Lade et al., 2017). Previous poverty trap models have focud on environmental quality or pollution (Barro and Sala-i Martin, 2004, Smulders, 2000, Xepapadeas, 2005), neglecting social-ecological interactions; have illustrated how positive feedback between wealth and technology can increa inequality and result in poverty traps through resource degradation (Mirza et al., 2019); have investigated relations between human health and poverty (Ngonghala et al., 2017); have ud one-dimensional models that can lead to simplified conclusions and inappropriate policy outcomes (Kraay and Raddatz, 2005); have been static models that cannot capture dynamic phenomena such as traps and feedbacks (Barrett and Bevis, 2015); or have been highly abstracted (Lade et al., 2017).
Biophysical complexity is not often considered in poverty trap models and relations between agricultural interventions and social-ecological poverty trap dynamics remain unexplored. Partially becau of this, development efforts tend to focus on blanket solution s, such as the ‘big push’: prom
oting external ast inputs, while neglecting a multitude of other factors affecting poverty. Lade et al. (2017)highlighted the importance of linking economic, natural and human factors in explaining poverty traps and concluded that the ufulness of interventions depends on context, particularly the relationship between poverty and environmental degradation. We build on this study as a conceptual framework to address knowledge gaps regarding the interplay between poverty and the biophysical environment in three ways: (1) we explore how biophysical complexity of the houhold-farm social-ecological system influences the dynamics of poverty traps in agro ecosystems, (2) we asss the impact of development interventions on the dynamics of the system, and (3) we test the effectiveness of interventions. To this end we have developed a ries of dynamical systems models that we u to test diver quences of interventions for alleviating poverty.
We describe biophysical complexity through factors that affect crop growth and limit food
production (Drechl et al., 2001, Rockstrom, 2000), such as nutrients, especially phosphorus, water and soil quality. First, phosphorus is thought to have crosd a threshold of overu at the global scale, leading to environmental conquences such as eutrophication (Rockström et al., 2009), acidification (Guo et al., 2010) and introduction of environmentally persistent chemicals or harmful elements in soil (Carvalho, 2006, Pizzol et al., 2014, Roberts, 2014, Schnug and Lottermor, 2013).
However, at a local level many of the world's poorest areas (e.g. Sub-Saharan Africa) suffer from a lack of soil nutrients, of which phosphorus is one of the main limiting factors for food production (Nziguheba et al., 2016, Verde and Matusso, 2014). Rearch indicates that global demand for phosphorus will ri over the remainder of the 21st century. At the same time, supply of high quality and accessible phosphate rock is likely to peak within the next few decades leading to increas in prices and decreas in affordability, mostly for low income countries (Cordell et al., 2009). Phosphorus application therefore prents a ‘double-edged sword’: in some cas it is necessary to overcome extreme levels of poverty and soil nutrient to break a poverty trap (Lade et al., 2017), but in other cas over application of fertilizers can have vere negative environmental conquences. A cond critical factor for crop growth is water. Rainfed agriculture plays a dominant role in food production, particularly in some of the poorest areas of the world, such as sub-Saharan Africa. Yield gaps are large and often caud by rainfall variability in occurrence and amount rather than by the total lack of water (Rockstrom, 2000). Becau of this, investing in rainwater harvesting, water management and conrvation practices, such as conrvation tillage, is an important strategy for increasing food curity and improving livelihoods. In small-scale mi-arid rainfed farming, the practices prove to be uful to mitigate drought and dry spells (Rockström, 2003) or to allow diversification and cultivating high-value crops, which can be an important poverty alleviation strateg
y (Burney and Naylor, 2012). A third critical factor for crop growth is soil quality. It reflects complex interactions between soil physical, chemical and biological properties including environmental quality and soil's contributions to health, food production and food quality. Including it in models bring additional level of realism and might explain human-environment relations (Altier, 2002, Bünemann et al., 2018, Parr et al., 1992, Verhulst et al., 2010, Thrupp, 2000).
Agricultural interventions are a common strategy for poverty alleviation in developing countries. The interventions we consider here are largely carried out by actors external to the local
community, such as non-governmental organizations(NGOs) or government programmes. For example, in the quest for an ‘African Green Revolution’ interventi ons to increa crop yields have been driven by: major cross-continental initiatives (Alliance for a Green Revolution in Africa), Millennium Villages Programmes (third party funded), donors (U.S. government's Feed the Future program), and national governments with NGO's implementing programmes at a local scale (Scoones and Thompson, 2011). In our models we focus on the implementation level of agricultural interventions.
Inputs of fertilizers or improved eds in the form of agricultural intensification schemes, or conrva
tion tillage and u of manure as a fertilizer, while diversifying houhold energy sources are commonly ud interventions. An intervention may influence one or more of the factors (asts, phosphorus, water or soil quality), thus ultimately influencing the dynamics of the whole agroecosystem. Since there are veral factors at play, poverty alleviation might require more than one intervention to be effective.
The aim of this paper is to develop a ries of models that reprent interlinked dynamics of asts, phosphorus, water and soil quality and allow investigating their effects on the low-productivity poverty trap of many sub-Saharan communities (Barrett and Swallow, 2006, Barrett, 2008, Tittonell and Giller, 2013). Furthermore, we u models to asss the effectiveness of different development interventions for various houhold-farm initial conditions. We begin by constructing a dynamical system model of an agroecosystem prior to any agricultural intervention and continue by developing three models reprenting changes in the dynamics of the agroecosystem due to agricultural interventions. Model assumptions are bad on empirical evidence from the literature on nutrients, soil quality, water and economic aspects of poverty in arid areas as well as expert interviews (Table 1). We first analyze the baline model without interventions and then quentially asss the effectiveness of different alleviation strategies and their combinations (e Table 2 in SI for a summa
ry of the results and insights). We conclude by discussing our results and insights in relation to other theoretical and empirical work, and their importance for development practice and future rearch.
2. The poverty trap models
We u systems of nonlinear ordinary differential equations to t up a ries of multidimensional dynamical systems model of poverty traps. We begin by tting up a model
which describes a houhold-farm system prior to any intervention and continue by prenting models incorporating different agricultural interventions. Table 1 contains our main assumptions about important factors for food production and the relationships between them derived from an extensive literature review and expert interviews. We u empirical evidence about poverty and agricultural production in arid regions, particularly Sub Saharan Africa, to extend a one dimensional theoretical poverty traps model towards a multi-dimensional and more realistic model.
The assumptions enable us to construct causal loop diagrams (Fig. 2, Fig. 3, Fig. 4) and to choo state variables and functional forms for our dynamical systems. The key assumptions are:
1 Phosphorus content of soils. Agricultural production removes phosphorus from crop producing soil
大家好的英文飞蛾之死s, which if not balanced by agroecological methods (Altier, 2002), or application of organic or artificial fertilizers limits crop growth and leads to lower yields (Drechl et al., 2001).
2 Water content of soils. Although rainfed agriculture is a widespread practice, it cannot always provide optimal water conditions, especially under the conditions of climate change.
3 Soil quality. Soil quality is a more complex variable than the nutrient content of soils or its capacity to produce crops alone. Accordingly, we model its dynamics parately to that of phosphorus and acknowledge that it might be lf-regenerating.
4 Asts. Asts such as agrochemicals, improved eds, and tools ud for agriculture, supports agricultural production and can be a limiting factor for people living below the poverty line (Druilhe and Barreiro-Hurlé, 2012, Kataria et al., 2012). We extend standard neoclassical dynamics of asts in which profit can be consumed or saved for investment in future production. Specifically, we implement a ‘savings trap’, in which houholds h ave a lower savings rate at low ast levels, leading to a trap in which they are unable to accumulate enough asts to escape poverty (Kraay and Raddatz, 2005).
We analyze dynamics of the system by studying its attractors and basins of attraction. An attractor is
a state (or t of states) to which the system tends over time starting from an initial state. It is defined by values of state variables, e.g. asts, phosphorus, water or soil quality. A basin of attraction is a t of all states of the system which tend over time towards the same attractor.
3. Results
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