Does Agricultural Development Address the Global Hunger Challenge?

Does agricultural development address the global hunger challenge? Some insights from the Sustainable Agriculture Matrix (SAM) data 

Kevin E Jackson, Duygu Avci,  Xin Zhang

It is usually expected that to address the global hunger challenge, agricultural production needs to be strengthened and made more sustainable. The logic is straightforward: more agricultural production will provide more food, which will be used to alleviate the hunger caused by lack of food. However, has this simple logic been working to address the hunger challenges for countries around the world? Has the global hunger issue been alleviated along with a more sustainable agriculture? To address these questions, we did some initial exploration with the assessment for agricultural sustainability (i.e., Sustainable Agriculture Matrix; SAM) and global hunger challenges for countries around the world.  



 A bit about the method

Specifically, SAM assesses agricultural sustainability with 18 indicators covering the three dimensions of sustainability (environmental, social, and economic; Figure 1). There are three hunger indicators from Sustainable Development Goal 2, Zero Hunger, including ‘Prevalence of undernourishment (undernsh, %),’ ‘Prevalence of stunting in children under 5 years of age (stunting, %),’ ‘Prevalence of wasting in children under 5 years of age (wasting, %)’ (Supplemental Table 1). All the indicators have been processed so that higher values indicate a more sustainable performance (e.g., less hunger, less environmental impacts, better economic performance). Then we tested whether the hunger indicators and SAM indicators are linearly correlated and in what directions for a country using data for the period of 2000-2015. We visualize results at the SAM website, and provide initial interpretation below to inspire more discussions on this topic. Detailed methods can be found in the subsequent section below.

So let’s consider our analysis and sub classifications of tradeoffs and synergies between the SDGs and the SAM indicators and our newly developed application to answer the question: ‘What has been the relationship between hunger reduction and agricultural sustainability?’ 

Figure 1: An example SAM score report showcasing sustainability performance of 18 indicators (outer ring). Red indicates indicator performance/sustainability is at a “dangerous level;” yellow is in a “zone of uncertainty,” and green shows indicator performance/sustainability is within a “safe operating zone.” The middle-ring shows average dimensional performance applying the same traffic color scheme. The central circle denotes the overall national sustainability performance.

Higher productivity, less hunger?

So, has the improvement in agricultural productivity been accompanied with less hunger in a country? Our analysis shows varying answers among countries (Figure 2). Using Wasting (i.e., Prevalence of wasting in children under 5 years of age) as one of the key indicators for the hunger challenges in a country, we found that many countries show insignificant relationships (e.g., United States, see countries in grey color) between reducing hunger and improving agricultural productivity (measured by SAM’s Labor Productivity indicator). For countries showing a significant relationship, as expected, many show that improving agricultural productivity is in synergy with achieving hunger reduction goals (i.e., a synergetic relationship; see countries in different shades of orange colors such as Morocco). Meanwhile, more than ten countries show the improvement in agricultural productivity has been accompanied with aggravating hunger problems (i.e., a tradeoff relationship; see countries in different shades of blue colors such as Paraguay). There is one extreme case that Oman shows that both agricultural productivity and hunger has been deteriorating since 2010.

(Some caveats) Admittedly, the insignificant relationship in some developed countries may be caused by the fact that the labor productivity scores (or the wasting score) were capped at 100 (e.g., US case in Fig. 2). Further investigation using the raw data of these indicators would help to clarify the relationship between these two indicators for these countries. However, take the US case as an example, the deterioration in wasting indicator around year 2005 was not accompanied by a reduction in agricultural productivity to the same extent, indicating the decoupling of improvement in agricultural productivity and hunger reduction. 

Overall, this initial test shows that higher agricultural productivity does not necessarily translate to less hunger (measured by % Prevalence of wasting in children under 5 years of age) in a country, and some countries even show deteriorating hunger issues despite increasing agricultural productivity (e.g., Sudan, Bulgaria, Thailand, South Korea). Therefore, it is critical to investigate those countries that show insignificant, or even tradeoff, relationships and reflect on many questions. For example, 1) what have the benefits brought by higher agricultural productivity been used for, if not for feeding the people in hunger? 2) Is it reasonable to argue for further increasing productivity to address the hunger challenge in these countries? 

Figure 2. The relationships between Wasting (A hunger indicator) and Labor productivity (A SAM indicator). The panels at the bottom show the indicator scores of an example country for each type of relationship.

Beyond agricultural productivity: What about other sustainability concerns?

Sustainable development of the agricultural sector is beyond agricultural productivity.  With the SAM indicators in environmental, social, and economic dimensions, we can further explore the relationships between the pursuit of sustainable agriculture and zero hunger targets using the availability of our data and interactive dashboard environment,, and provide inputs to some of the following questions: 

  • Has hunger reduction been accompanied with aggravating environmental impacts by agricultural production?  [link]
  • Does agricultural trade help to alleviate hunger? 
  • What are the general relationships between agricultural sustainability and hunger reduction?

Interested in Navigating this R Shiny Application On Your Own?

Please read below for some helpful supplementary material and  engage with our SAM/SDG Analysis tool yourself (visit link here).


What Do We Mean by Tradeoffs & Synergies (Methods Expanded)?

Based on the statistical relationships between a pair of indicators, two broad types of relationships can be defined:  tradeoffs and synergies. Given that both SAM and SDG data are prepared to establish monotonicity (i.e., high values always correspond to improved sustainability performance), a synergy can be described as a statistically significant monotonic relationship such that sustainability performance between SDG and SAM index scores either improve or worsen in parallel. Conversely, tradeoffs represent SAM and SDG relationships such that sustainability performance are inversely related. But, simply classifying a unique index pair for a given country as either being in tradeoff or synergy doesn’t elucidate the historical relationship. Are conditions improving or worsening over time? If a country specific indicator pair is in tradeoff, is one indicator  worsening over time and the other  improving? To address this knowledge gap, we further classify synergies and tradeoffs into three sub categories: worsening, improving, and unclear historical trends (Figure 3). These sub-classifications relationships are determined by testing for significant linear trends in our respective SDG and SAM indicator country datasets.  

Figure 3: Products of our tradeoff/synergy analysis between SAM and indicators adopts the following color scheme based on our sub-classifications of tradeoffs and synergies based on Spearman’s rank correlation & linear regression analyses. Arrows indicate the directionality of the evaluated SAM and SDG indicator.

Supplemental Table 1: 3 Indicators from SDG 2 that most closely monitor efforts to reduce hunger.

Index (Abbr.) SDG Indicator Description
wasting 2 Prevalence of wasting in children under 5 years of age (%) The percentage of children up to the age of 5 years whose weight falls below minus two standard deviations from the median weight for their age, according to the WHO Child Growth Standards. UNICEF et al. (2016) report an average prevalence of wasting in high-income countries of 0.75%. We assumed this value for high-income countries with missing data.
stunting 2 Prevalence of stunting in children under 5 years of age (%) The percentage of children up to the age of 5 years that are stunted, measured as the percentage that fall below minus two standard deviations from the median height for their age, according to the WHO Child Growth Standards. UNICEF et al. (2016) report an average prevalence of wasting in high-income countries of 2.58%. We assumed this value for high-income countries with missing data.
undernsh 2 Prevalence of undernourishment (%) The percentage of the population whose food intake is insufficient to meet dietary energy requirements for a minimum of one year. Dietary energy requirements are defined as the amount of dietary energy required by an individual to maintain body functions, health and normal activity. FAO et al. (2015) report 14.7 million undernourished people in developed regions, which corresponds to an average prevalence of 1.17% in the developed regions. We assumed a 1.2% prevalence rate for each high-income country with missing data.