Two Widely-Cited Statistics on Irrigation and Food Security May Be Wrong – Technology Networks

Report on the Empirical Validity of Global Irrigation Statistics and Implications for Sustainable Development Goals

Introduction: Re-evaluating Data for SDG 2 and SDG 6

A recent study published in PNAS Nexus challenges the empirical basis of two foundational statistics that have guided global policy on food security and water management for over five decades. The statistics—that irrigated agriculture accounts for 40% of global crop production and 70% of global freshwater withdrawals—are central to strategies aimed at achieving Sustainable Development Goal 2 (Zero Hunger) and SDG 6 (Clean Water and Sanitation). The report indicates these widely cited figures are based on weak evidence, potentially undermining the effectiveness of policies designed to meet these critical SDGs.

Analysis of Statistical Provenance and Impact on SDG 17

The research, conducted by a team from the University of Birmingham, investigated the origin and dissemination of the “40:70” statistics. The findings highlight a significant issue with data integrity, a core component of SDG 17 (Partnerships for the Goals), which calls for reliable data for evidence-based policymaking.

• The statistics have been cited in over 3,500 research and policy documents.
• An analysis revealed that only 1.5% of these documents contained original data.
• A majority of documents cited as the source for the figures either lacked supporting evidence or did not mention the statistics at all.
• The study concludes that the numbers have spread through “hearsay,” likely due to their effectiveness in simplifying complex food-water challenges.

Data Ambiguity and its Consequences for Sustainable Development

The study reveals a significant degree of uncertainty in current data regarding irrigation’s role, which complicates efforts to formulate effective strategies for SDG 2 and SDG 6. The wide variability means that the available data can support contradictory policy narratives.

Key Findings on Data Variability:

1. Contribution to Global Food Production (SDG 2): The actual figure could range from as low as 18% to as high as 50%. This ambiguity makes it difficult to accurately assess irrigation’s role in achieving global food security.
2. Share of Freshwater Withdrawals (SDG 6): The proportion of global freshwater used for irrigation ranges from 45% to 90%. This vast range obscures a clear understanding of water use efficiency and hinders the development of targeted water conservation policies.

Recommendations for SDG-Aligned Policy Formulation

In light of the findings, the report suggests a shift in approach to policy design for food and water security. To better align with the objectives of the Sustainable Development Goals, the following recommendations are proposed:

• Enhance Data Scrutiny: A critical analysis of the quality of water and agricultural data is necessary, particularly in high-level policy discussions such as the upcoming COP30. This aligns with the data-driven principles of SDG 17.
• Develop Resilient Policies: Policymakers should design strategies for food security (SDG 2) and water management (SDG 6) that are robust and effective regardless of precise global statistical benchmarks.
• Promote Localized Solutions: Efforts should be directed towards collaborating with local stakeholders and communities to identify and implement context-specific solutions for enhancing crop production and water conservation, moving away from a reliance on uncertain global figures.

Reference

Puy A, Linga SN, Wei N, et al. Widely cited global irrigation statistics lack empirical support. PNAS Nexus. 2025;4(11):pgaf323. doi: 10.1093/pnasnexus/pgaf323

1. Which SDGs are addressed or connected to the issues highlighted in the article?

The article highlights issues directly connected to the following Sustainable Development Goals (SDGs):

• SDG 2: Zero Hunger: The article’s central theme is food security and the role of irrigation in global crop production. It questions the widely cited statistic that “irrigation agriculture produces 40% of global crops,” which is fundamental to understanding and achieving food security.
• SDG 6: Clean Water and Sanitation: The article extensively discusses the management of freshwater resources, specifically questioning the statistic that irrigation “withdraws 70% of global freshwater resources.” This relates directly to water use, efficiency, and sustainability.
• SDG 17: Partnerships for the Goals: The core argument of the article is about the lack of robust, evidence-based data for policymaking. It critiques how statistics have spread through “hearsay” and lack empirical support, which undermines the ability to create effective policies and monitor progress, a key aspect of SDG 17.

2. What specific targets under those SDGs can be identified based on the article’s content?

Based on the article’s focus, the following specific SDG targets can be identified:

SDG 2: Zero Hunger

• Target 2.4: “By 2030, ensure sustainable food production systems and implement resilient agricultural practices that increase productivity and production…” The article directly addresses this target by examining the contribution of a key agricultural practice—irrigation—to global food production. The uncertainty about whether irrigation produces “as low as 18% or as high as 50%” of crops shows the difficulty in assessing the sustainability and productivity of current food production systems.

SDG 6: Clean Water and Sanitation

• Target 6.4: “By 2030, substantially increase water-use efficiency across all sectors and ensure sustainable withdrawals and supply of freshwater to address water scarcity…” The article’s investigation into the claim that irrigation accounts for 70% of freshwater withdrawals is central to this target. The finding that this share could range “from 45% to 90%” highlights the critical need for accurate data to manage water-use efficiency and ensure sustainable withdrawals.

SDG 17: Partnerships for the Goals

• Target 17.18: “By 2020, enhance capacity-building support to developing countries… to increase significantly the availability of high-quality, timely and reliable data…” The entire article is a critique of the quality and reliability of data used in global policy. The finding that “only 1.5% of the documents citing these statistics contain original data” and that the figures have “spread through hearsay” directly points to a failure to meet this target and underscores the need for robust, empirically supported data for policymaking.

3. Are there any indicators mentioned or implied in the article that can be used to measure progress towards the identified targets?

The article implies several indicators by discussing the very statistics used to measure progress, even as it critiques their validity.

For SDG Target 2.4

• Implied Indicator: Share of global food production from irrigated agriculture. The article revolves around the “40%” statistic, which is used as a de facto indicator of irrigation’s importance to food security. The study’s finding that this figure is highly uncertain (ranging from 18% to 50%) suggests that measuring this indicator accurately is crucial for tracking progress toward sustainable and productive agriculture.

For SDG Target 6.4

• Implied Indicator: Share of global freshwater withdrawals by the agricultural sector (specifically for irrigation). This is directly related to the official indicator 6.4.2 (Level of water stress). The article questions the “70%” figure for freshwater withdrawals by irrigation, showing a wide range of uncertainty (“from 45% to 90%”). This percentage is a key measure of water-use efficiency and the pressure agriculture places on freshwater resources.

For SDG Target 17.18

• Implied Indicator: Provenance and empirical support for data used in policy and research documents. The article’s methodology is a direct measurement of this. The finding that “only 1.5% of the documents citing these statistics contain original data” and that most sources “lacked evidence” serves as a powerful, albeit informal, indicator of the quality and reliability of the data underpinning global policy discussions on food and water.

4. Create a table with three columns titled ‘SDGs, Targets and Indicators” to present the findings from analyzing the article.

Source: technologynetworks.com