Joseph Manzvera*
Kwabena Asomanin Anaman- Department of Agricultural Economics and Agribusiness, University of Ghana, Accra, Ghana
Introduction: The increasing frequency and intensity of climate-induced extreme weather events, such as droughts, have significantly reduced agricultural productivity worldwide. Enhancing access to agro-meteorological services can mitigate these impacts by improving farmers’ decision-making and adaptive capacity. However, their economic value remains poorly understood, especially in African countries where meteorological services are provided as public goods. Demonstrating their value is critical to stimulate investment and strengthen climate-resilient agriculture.
Methods: This study reviews and synthesises empirical evidence on the economic value of agro-meteorological services through a systematic literature review and meta-analysis of international studies. Both peer-reviewed and grey literature were analysed to assess valuation methods, estimated benefits, and knowledge gaps.
Results: The meta-analysis results indicate that agro-meteorological services generate an estimated annual economic value of US$0.59 billion annually, underscoring their vital role in enhancing agricultural productivity, risk management, and resilience to climate shocks. Access to reliable weather and climate information enables farmers to make informed decisions, optimise resource use, and reduce crop losses, thereby contributing to more sustainable and profitable agricultural systems.
Discussion: Despite their clear economic value, political, institutional, and socio-economic factors influencing investment decisions remain underexplored. The integration of political economy analysis is essential to understand how governance structures, marginalisation, and social inequalities shape access to and benefits from agro-meteorological services.
Recommendations: Governments and development partners should increase investment in advanced meteorological infrastructure, data and communication systems, treating these services as productive investments rather than costs. Researchers should integrate indigenous weather knowledge with modern forecasting to enhance trust and usability. The economic analyses should incorporate secondary benefits across sectors using tools such as input–output analysis and general equilibrium models to better capture the full societal value of agro-meteorological services. There is also a need to incorporate political economy perspectives in future studies to better capture how social and structural inequalities shape the economic value and equitable use of agro-meteorological services.
1 Introduction
The frequency and intensity of extreme weather events, such as droughts and floods, have escalated in recent years, driven in part by climate change resulting from anthropogenic activities. This trend is further exacerbated by the unsustainable human exploitation of Earth’s resources, often carried out with little consideration for the needs of other species sharing the planet. These climatic disruptions disproportionately affect smallholder farmers in developing countries, particularly across Africa, who are among the most vulnerable to climate-induced risks (Bahta, 2022; Chiputwa et al., 2022; Radeny et al., 2019). This is due to heavy reliance on rainfed farming systems and limited capacity to cope with a changing climate (Bahta, 2022; Zinyengere et al., 2011). Indeed, climate change remains one of the key challenges catalysing the food insecurity crisis in Africa (Ayugi et al., 2022; Bedasa and Bedemo, 2022). Nevertheless, increased access and investments in agro-meteorological services can significantly help offset the adverse effects of climate change through the information provided to farmers to improve their production and marketing decisions and to ensure the uptake of optimal adaptation strategies (Bruno Soares, 2017; Hansen et al., 2019; Hewitt and Stone, 2021; Paparrizos et al., 2021; Takle et al., 2014; Tarchiani et al., 2018; Tesfaye et al., 2019; Vaughan, 2022).
Agro-meteorological services and products, encompassing weather forecasts, seasonal climate outlooks, and early warnings, are a key subset of weather and climate services that support agricultural decision-making, typically delivered through the National Meteorological Services (NMS) (Hansen et al., 2019; Hewitt and Stone, 2021; Tarchiani et al., 2018; Vaughan, 2022; WMO, 2021; WMO, 2022). Searching for weather information and products is a common risk management technique employed by African farmers (Anaman, 1988; Amegnaglo et al., 2017; Manzvera et al., 2024). While weather forecasts focus on short- to medium-term conditions and climate services address longer-term trends, the two overlap significantly, and terms such as weather forecasts, seasonal climate services, and climate information are often used interchangeably. Farmers rely on both: short-term forecasts guide daily operations like fertiliser application, while seasonal climate services inform strategic decisions such as crop selection and land preparation (Adams et al., 2003; Amegnaglo et al., 2017; Bert et al., 2006; Chiputwa et al., 2022; Paparrizos et al., 2021; Patt et al., 2005; Roudier et al., 2016; Takle et al., 2014; Tarchiani et al., 2018), underscoring their integrated role in agriculture. For this study, these terms are used synonymously, with the reviewed literature primarily focusing on seasonal weather forecasts.
Despite their potential, the economic value of agro-meteorological services remains poorly understood, particularly in Africa, where forecasts are often treated as public goods (Anaman et al., 2017; Anaman and Lellyett, 1996b; Chiputwa et al., 2022; Vaughan and Dessai, 2014). This lack of recognition limits investments in modern meteorological infrastructure and tailored service delivery (Dinku et al., 2022; Hewitt and Stone, 2021; WMO, 2015, 2021). Greater awareness of their benefits is essential to inform policy and increase funding towards the delivery of these services (Bruno Soares et al., 2018; Freebairn and Zillman, 2002; Perrels et al., 2013; WMO, 2015). Concurrently, increased funding for agro-meteorological services further ensures these services are delivered in formats that are easily understood by end-users and translated into actionable decisions at the farm level (Hewitt and Stone, 2021; Vaughan and Dessai, 2014; Zinyengere et al., 2011).
This study addresses this gap in understanding the economic value of agro-meteorological services through a comprehensive literature review and meta-analysis. The central research question is: What is the economic value of agro-meteorological services? The meta-analysis quantifies benefits at both farmer and societal levels—considering increased agricultural production and improved food availability for consumers (Anaman and Lellyett, 1996b; Bruno Soares, 2017), offering a more rigorous assessment than traditional narrative reviews, which typically provide qualitative summaries. Beyond quantification, the study’s novelty lies in examining research gaps through a political economy lens. Political economy analysis is critical because access to and perceived value of these services are shaped not only by technical quality but also by socioeconomic and institutional factors such as poverty, marginalisation, social exclusion, and infrastructure limitations. For example, smallholder farmers in remote areas often face barriers like poor telecommunications, low literacy, and limited financial resources, which hinder effective use of forecasts even when publicly available. Accounting for these dynamics provides a more complete understanding of the benefits of agro-meteorological services and informs policies and interventions that promote equity, ensure vulnerable groups benefit, and target investments where they are most needed to strengthen climate change adaptation.
The remainder of the paper has four major sections. The first section outlines the materials and methods employed. Secondly, the key findings from the review process are provided. Third, the discussion section is provided, highlighting common trends that emerged as well as areas which need further attention. The conclusion and recommendations, including suggestions made to expand investments in agro-meteorological services, are provided in the fourth section. The list of cited references is provided at the end of the paper.
2 Materials and methods
2.1 Data collection method
This study was based on an in-depth systematic literature review (SLR). The review of literature took stock of and synthesised existing evidence on the economic value of agro-meteorological services. The systematic review was guided by the RepOrting Standards for Systematic Evidence Synthesis (ROSES) framework (Haddaway et al., 2018). As such, the review was based on a well-defined procedure that outlined the main objective, rationale, and methods followed during the review. This framework provides a standard format for selecting and reviewing literature on a specific topic (Haddaway et al., 2018; Ogunyiola et al., 2022; Peñaloza et al., 2022). The review process consisted of five key steps, which are: setting of eligibility criteria, search strategy, screening and selection of research studies, article management, data extraction and then synthesis of findings. These five steps are applied extensively in a systematic review of literature in several socio-economic research studies (Haddaway et al., 2018; Ngwili et al., 2021).
2.2 Review procedure
2.2.1 Inclusion and exclusion criteria
Article selection for this review was guided by clearly defined inclusion and exclusion criteria. To ensure comprehensive coverage, the literature search was not restricted by publication year or geographic region, allowing assessment of both the evolution and global scope of research on the economic value of agro-meteorological services. Only articles published in English were included, reflecting the language proficiency of the researchers, and inclusion was limited to peer-reviewed journal articles to ensure methodological rigour and the quality of evidence.
Eligible studies explicitly examined the economic valuation of agro-meteorological services, such as weather forecasts, seasonal forecasts, or climate services in agriculture. These included studies quantifying economic benefits, net returns, productivity gains, cost–benefit outcomes, or welfare effects attributable to the use of weather or climate information by farmers or society. Both micro-level (farm or household) and macro-level (sectoral or societal) evaluations were considered.
Studies were excluded if they focused solely on technical forecast accuracy, climatological modelling, or meteorological system development without linking to economic outcomes. Similarly, articles addressing perceptions, awareness, or adoption of services without explicit economic valuation were excluded, as were studies on climate services in non-agricultural sectors. Non-peer-reviewed publications were also excluded to ensure consistency and reliability.
2.2.2 Search strategy
A structured search strategy was developed to identify relevant articles from online databases, with Scopus serving as the primary source and AGORA and Ageconsearch used to complement coverage. The use of multiple databases was intended to maximise access to all potentially eligible studies. These databases were selected due to their strong coverage of high-quality applied agricultural economics and social science literature. Boolean logic operators were employed to improve the precision and completeness of the search, with the connectors “AND” and “OR” used to narrow or broaden the search, respectively. The core search string applied was economic value AND (weather forecasts OR seasonal weather forecasts OR climate services OR agro-meteorological services). Additional variants were used to capture a wider range of relevant studies and facilitate filtering, including willingness to pay OR benefit–cost AND (weather forecasts OR seasonal weather forecasts OR climate services OR agro-meteorological services).
The literature search was not restricted by publication year to capture all potentially relevant studies. However, the review was limited to research articles published in English that focused explicitly on the economic valuation of agro-meteorological services, weather forecasts, or climate services in the agricultural sector. Only peer-reviewed journal articles were systematically reviewed. Additional studies were identified through backward citation tracking by examining reference lists of selected articles, a method known to enhance coverage of relevant literature (Reijnders et al., 2008; Roine et al., 2001; Salehi et al., 2021). Grey literature was also reviewed. The grey literature mainly included peer-reviewed reports and case studies on evaluating the economic benefits of climate services, and these were obtained through searches in online databases of United Nations (UN) organisations such as the Food and Agriculture Organisation of the United Nations (FAO), World Food Programme (WFP) and WMO, as well as the World Bank. The keyword searches used in these databases included weather forecast, early action, anticipatory action and early warning and early action. These keywords were searched in combination with study-related key terms, mainly economic benefits and economic value. The literature search was conducted between March and December 2022.
2.2.3 Screening and selection of studies
The inclusion criteria were based on title and abstract, a common procedure used in many review studies (Dukuzumuremyi et al., 2020; Maïga et al., 2020; Ngwili et al., 2021). Once the title and abstract of the article failed to concisely elaborate on issues dealing with the economic valuation of agro-meteorological services, the paper was dropped from the analysis.
2.2.4 Article management, data coding and extraction
The retrieved articles were organised and managed using Zotero, which facilitated the identification and removal of duplicate entries, ensuring a well-curated and streamlined database for analysis. A double review process was conducted. Thus, a single reviewer (one member of the research team) assessed studies for inclusion eligibility based on title and abstract and then the other team member repeated the process independently by randomly select 50% of the articles to reinforce consistency. Exclusion or inclusion eligibility was resolved through consensus where disagreements arose. A data extraction sheet was then developed in Excel to facilitate the organisation and coding of key characteristics essential for addressing the research question. These characteristics included the first author’s name, year of publication, continent, country of study, methodological approach used to estimate the economic value, and the quantified value of agro-meteorological services in US$. This structured approach ensured consistency in data handling, enabling a comprehensive and systematic synthesis of the reviewed literature.
2.2.5 Data analysis and presentation
Key findings from the review were summarised and presented mostly in tables for clear visualisation and interpretation. In addition to descriptive statistics, a meta-analysis was conducted to quantitatively synthesise the data and estimate the aggregated economic value of agro-meteorological services based on existing literature. This dual approach enabled a robust examination of the topic, integrating both qualitative insights and quantitative estimations to provide a comprehensive understanding of the economic value of agro-meteorological services.
Meta-analysis has recently gained prominence as a robust analytical method for synthesising evidence on a specific topic by integrating findings from various published studies (Born et al., 2021; Santeramo and Lamonaca, 2019; Stanley et al., 2008). Using the random effects meta-regression model, this study combines empirical estimates from different but comparable studies to provide a unified understanding of the issue in question. One key tool in meta-analysis is the I2 statistic, which measures heterogeneity or the degree of variation among studies. A value of 0% indicates no heterogeneity, while values of 25–50%, 51–74, and 75% or more indicate low, moderate, and high heterogeneity, respectively (Afshin et al., 2017; Higgins et al., 2003). Larger I2 values suggest greater variability in the findings across studies. Additionally, meta-analysis employs a weighting function approach to generate a forest plot, which in this case, visually represents the estimated economic value from each study (in billion US$) and then the pooled economic value of agro-meteorological services and the heterogeneity among studies.
Unlike the traditional way of literature review, this approach provides a more comprehensive estimation of the economic value of agro-meteorological services while accounting for study-specific variations (Dettori et al., 2021; Verhagen and Ferreira, 2014). The forest plot offers readers a concise and graphical synthesis of the results, making it easier to interpret the aggregated findings. The covariates used in this study were valuation methodology, which includes dummy variables (1-yes and 0 Simulation modelling) on contingent valuation (CV), choice experiment (CE) and benefit–cost analysis (BC). The moderating effect of Year of publication (YP) and level of analysis (Meso and Macro-level vs. Micro-level analysis) were also explored. The regional dummies R (America – AM, Australia-Au, Asia – As, SSA Sub-Saharan Africa-SSA vs. Global) were also used to assess the regional effects. By quantifying economic value while accounting for heterogeneity, meta-analysis offers a powerful and transparent tool for demonstrating the economic value of agro-meteorological services. This evidence-based approach strengthens the case for investment by revealing not only average benefits but also how and why impacts differ across contexts—insights that are not attainable through conventional literature reviews. Nevertheless, due to the limited heterogeneity observed, we reported pooled effect estimates and heterogeneity metrics via the forest plot, as these offer clearer insights aligned with the study objectives.
3 Results
3.1 Characteristics of reviewed articles
A total of 1,406 articles were identified from the literature search. After careful screening and removal of duplicates, 69 articles met the inclusion criteria and were reviewed (including five articles obtained through a reference list), as shown in the flow diagram (Figure 1). For all the eligible articles, the full-text review was conducted, and findings are discussed in succeeding sections and subsections. The following characteristics were extracted and presented in table format (Table 1): first author name, year of publication, country of study, methods of eliciting economic value and total estimated economic value of agro-meteorological services. Out of the 69 articles reviewed, a total of 27 met the additional criteria for inclusion in the meta-analysis.
Figure 1. Number of articles included. Source: authors’ compilation.
Table 1. Summary of reviewed studies based on stated preference approaches.
The temporal distribution of studies reveals a clear concentration in recent years, with approximately 60% of reviewed articles published after 2010. Notable publication peaks occurred in 2002, 2016, and 2021, each featuring multiple studies, while the period between 1995 and 2005 saw relatively few articles, typically one or two per year. This trend reflects growing academic and policy interest in the economic valuation of agro-meteorological services, driven by increasing climate variability, advances in climate modelling, and the integration of agro-meteorological services into agricultural and development planning.
Recent publications also report higher average economic values compared to earlier studies, suggesting a shift toward national-scale assessments and the use of improved modelling techniques. Overall, this temporal pattern indicates that both the volume of research and the magnitude of reported economic benefits have increased over time, underscoring the expanding role of agro-meteorological services in agricultural decision-making and policy decisions. This also concurs with the Sendai Framework for Disaster Risk Reduction (2015–2030) and the United Nations (UN)‘s Early Warnings for All (EW4All) Initiative, which aims to ensure everyone, everywhere, is covered by early warning systems by 2027.
From a geographical perspective, the distribution of studies is highly uneven across continents. The Americas account for the largest share (around 37%), followed by Asia (26%), while Africa (15%), Australia (11%), and Europe (11%) are significantly less represented. This imbalance reflects disparities in research capacity, data availability, and funding for economic valuation studies. Regions with stronger institutional support and established climate services and early warning systems tend to produce more frequent and comprehensive assessments.
Continental differences also emerge in reported economic values. Studies from the Americas report the highest average estimates, largely due to the prevalence of macro-level and national analyses. In contrast, research from Africa and Australia is predominantly micro-level, resulting in lower average valuations. Despite high exposure to climate risks, these regions remain underrepresented in large-scale valuation research, underscoring a critical gap and the need for more comprehensive economic valuation of agro-meteorological services in vulnerable areas.
3.2 Varied benefits of agro-meteorological services
Seasonal agro-meteorological information provides multiple dividends to farmers as an early warning signal for early action. Generally, accurate agro-meteorological forecasts of at least 3 months lead time allow farmers to implement optimal farming decisions in a changing climate context (Bruno Soares, 2017; Jones et al., 2000). Such decisions include scheduling land preparation and planting dates, choosing optimal crop varieties, deciding crop density, irrigation scheduling, rate & timing of fertiliser and pesticide application, as well as managing field operations (Adams et al., 2003; Bert et al., 2006; Patt et al., 2005; Roudier et al., 2016; Takle et al., 2014; Tarchiani et al., 2018). In Zimbabwe, for example, the use of weather forecasts resulted in farmers adjusting farming decisions, including altering planting dates, planting different crop varieties and staggering planting dates (Mudombi and Nhamo, 2014; Patt et al., 2005; Zamasiya et al., 2017). In Senegal, the use of weather forecasts also resulted in the uptake of optimal farming practices, such as planting suitable crop varieties, which led to a 10–25% increase in household incomes (Chiputwa et al., 2022).
Implementation of tailored production strategies and taking advantage of favourable weather conditions, as guided by weather forecasts also helps farmers to minimise losses (Anaman et al., 2017; Jones et al., 2000; Quiroga et al., 2011). For instance, the Food and Agriculture Organisation of the United Nations (FAO) estimated that the use of weather forecasts has the potential to reduce yield loss and variability by 10–30% (FAO, 2019). Weather forecasts help farmers to save time and costs in agricultural activities (Anaman and Lellyett, 1996b). Acting ahead of a predicted drought through the implementation of anticipatory actions such as growing drought-tolerant rice varieties in the Philippines between 2018 and 2019 helped farmers to prevent losses of US$101 in avoided input costs, and households reaped US$4.4 for every dollar invested (FAO, 2020). Cost reduction in agricultural production has also been noted through the use of enhanced weather forecast information. Cotton producers in Australia had a 1% cost reduction between 1994 and 1996 due to the use of improved weather forecasts (Anaman et al., 1998; Anaman and Lellyett, 1996b). Other benefits also include assisting in the planning of household and social activities as well as reducing the risks of assets/property damage and/or human death in case of extreme weather events (Anaman et al., 1998, 2017; Gros et al., 2022; de la Tozier Poterie et al., 2018). Humanitarian actors also rely on weather forecast information to implement anticipatory action interventions such as anticipatory cash transfers (Gros et al., 2019, 2022; de la Tozier Poterie et al., 2022; de la Tozier Poterie et al., 2018).
3.3 The economic value of agro-meteorological services
Since the seminal work by Nelson and Winter (1964) which formalised the theoretical valuation of weather forecasts, based on neoclassical economic theories, the economic valuation of meteorological services has increased substantially. The body of literature in the particular area of agro-meteorological services also increased with the specialised work on agro-meteorology of the World Meteorological Organisation (WMO) and the launch of the Global Framework for Climate Services (GFCS) in 1950 and 2009, respectively (An-Vo et al., 2021; Hewitt et al., 2020; Hewitt and Stone, 2021). The increase of studies which estimate the economic value of agro-meteorological services also signifies the growing awareness about climate change and the need for climate services to strengthen resilience and anticipatory action (Gros et al., 2019; Hewitt et al., 2020; Mjelde et al., 1998; de la Tozier Poterie et al., 2023; de la Tozier Poterie et al., 2018; WMO, 2015, 2022). Furthermore, this is motivated by the increasing momentum and a call for evidence to influence policy towards increased investments in the delivery of improved climate services (WMO, 2015, 2021, 2022). As countries across the globe are at different stages of operationalising National Frameworks for Climate Services in line with GFCS guidelines, research studies assessing the value and use of weather forecasts are also increasing (Hewitt et al., 2020).
Valuation of public goods and services, such as agro-meteorological services require a full understanding of non-rival and non-exclusiveness concepts embodied in goods and services (Anaman et al., 1998, 2017; Anaman and Lellyett, 1996b; Freebairn and Zillman, 2002). Apart from being provided free of charge by Governments, agro-meteorological services are non-rival in consumption since the use by one farmer does not reduce their availability (both in terms of quality and quantity) to the other farmers (Anaman et al., 1998). This concept is sometimes referred to as the indivisibility property of public goods. Again, agro-meteorological services are non-exclusive since an individual farmer cannot exclude other farmers from using the services. Thus, the agro-meteorological services possess the non-rival and non-exclusiveness properties just like any other public goods and services (Anaman et al., 1998, 2017; Freebairn and Zillman, 2002). However, it is important to note that agro-meteorological services are not virtually free although they are provided at no direct cost to farmers through mass media.
Beyond the use of market prices, there are four main broad methods used in literature to estimate the economic value of agro-meteorological services. These are: (1) valuation based on contingent valuation and choice modelling methods (stated preference approaches), (2) valuation based on revealed preference approaches such as the hedonic pricing method and the travel cost or the general expenditures incurred to consume or use resources concept, (3) valuation using simulation modelling, and (4) producer and consumer surpluses mostly based on general equilibrium models, input–output analysis and benefit-transfer approaches. Generally, the reviewed articles showed that there is a substantial economic value of agro-meteorological services in the agriculture sector across the globe.
The meta-analysis estimated the annual economic value of agro-meteorological services at US$0.59 billion (US$590 million), highlighting that these services provide over half a billion dollars in perceived economic benefits to the global agricultural economy annually (Figure 2). This substantial valuation underscores the critical role of agro-meteorological services in enhancing agricultural productivity and supporting the implementation of climate risk reduction strategies globally. This evidence also concurs with other previous studies, which showed that there is a substantial economic value of agro-meteorological services globally (Mjelde et al., 1998). Thus, the use of agro-meteorological services increases the societal net benefits.
Figure 2. Forest plot showing a summary of the estimated economic value of agro-meteorological services. Source: authors’ computation.
The I2 statistic of 48.7% (Figure 2) indicates that the variation between studies is relatively low, suggesting a consistent valuation of agro-meteorological services by farmers across different contexts and valuation approaches. This finding highlights the universally recognised importance of these services in supporting agricultural decision-making and risk-reduction strategies. This consistent valuation also underscores the need for continued investment in agro-meteorological services to support global agricultural productivity and resilience. Given this limited heterogeneity, we did not conduct additional analyses, such as subgroup meta-analysis by valuation approach or exploration of heterogeneity drivers. Instead, we focused on reporting pooled effect estimates and heterogeneity measures (Figure 2), which we consider more informative and aligned with the objectives of this study. The estimated economic values across various countries and based on different valuation techniques are presented in sub-sections 3.3.1 to 3.3.4 below.
3.3.1 Economic value of agro-meteorological services based on stated preference approaches
Under the stated preference approach, a hypothetical market will be created to elicit farmers’ willingness to pay for agro-meteorological services (Amegnaglo et al., 2017). The hypothetical markets are created since there are no observed market transactions. Thus, non-market valuation methods are commonly used in this case. The contingent valuation method and choice modelling are the most used techniques under the stated preference approach (Anaman et al., 1998; Freebairn and Zillman, 2002; Paparrizos et al., 2021; Tesfaye et al., 2019). Under these techniques, the maximum amount farmers are willing to pay is then used as the economic value of agro-meteorological services. On average, an individual farmer is willing to pay US$43.3/year to receive agro-meteorological services rather than to be without the services. The average annual economic value of agro-meteorological services based on stated preference approaches is estimated at US$8.89 billion (Table 1).
3.3.2 Economic value of agro-meteorological services based on revealed preference approaches
In situations where market transactions can be observed, revealed preference methods (market prices) can be applied (Amegnaglo et al., 2022). Under this approach, the travel cost method is the most commonly used technique to value agro-meteorological services in recent years (Amegnaglo et al., 2022; Douglas and Taylor, 1998). In this case, farmers pay for customised agro-meteorological services up to a unit where the marginal value equals the price (Freebairn and Zillman, 2002). As such, the total expenses incurred to acquire the agro-meteorological services are used to reflect the economic value of agro-meteorological services. These total expenses can also include telephone costs and time costs incurred by farmers in searching for and receiving weather forecast information (Anaman and Lellyett, 1996a). In Australia (Anaman and Lellyett, 1996a), the general expenses method was used to estimate the minimum economic value of public weather services. They estimated a minimum value of 2.868 million Australian dollars during the 1994/95 financial year. In Benin (Amegnaglo et al., 2022), the economic value of indigenous weather forecast information was estimated using the travel cost method. They estimated a minimum value of US$7010.75 among 354 maize farmers.
The hedonic pricing approach originally proposed by Rosen (1974) is also a revealed preference technique that can be employed to estimate the economic value of weather forecast information (Anaman et al., 2017). Following Rosen (1974), the price or cost of weather forecast information is a function of a bundle of weather forecast characteristics. For instance, accuracy of weather forecast, lead time, ease of understanding and relevance can influence the price of weather forecast information (Anaman and Lellyett, 1996a; Figini et al., 2022). Thus, farmers’ willingness to pay for improvement in agro-meteorological services can be derived from the relationship between the revealed price of weather forecast information and its associated attributes (Anaman et al., 2017; Rosen, 1974). The hedonic pricing approach has been extensively applied in property and labour markets as well as economic valuation of environmental amenities (Donnelly, 1991; Freeman, 1981; Gibbons et al., 2014; Rosen, 1974). Nevertheless, application of the hedonic pricing approach in estimating the economic value of agro-meteorological services is still limited since most available studies focused on transport and tourism sectors (Anaman et al., 2000; Campos et al., 1999; Leigh, 1995; Leigh et al., 1998; von Gruenigen et al., 2014).
3.3.3 Economic value of agro-meteorological services based on simulation modelling
Simulation modelling can also be employed through cost function analysis and cost-loss ratio techniques to estimate the economic value of agro-meteorological services (Fernandez et al., 2016; Makaudze, 2016; Mjelde et al., 1998). Simulation modelling, particularly ex-ante crop simulation models, is widely used to estimate the impacts of utilizing improved seasonal weather forecast information on agricultural production outcomes, such as production costs, net returns, and crop yields. These models provide valuable insights into the expected benefits of agro-meteorological services, including increased profitability and cost reductions. By aligning with the fundamental principles of economic theory—such as cost minimization and profit maximisation—simulation modelling reinforces the economic rationale for integrating agro-meteorological services into farming practices, demonstrating their utility in optimizing agricultural productivity and resilience (Freebairn and Zillman, 2002; Meza et al., 2008). Simulation modelling can employ either static or dynamic approaches, each offering distinct advantages depending on the context. Static simulation models assume a single climate event, providing a straightforward analysis of its impact on agricultural production.
In contrast, dynamic simulation models allow for adjustments to the production system by incorporating multiple climate events occurring throughout the production period. This dynamic approach enables a more realistic and nuanced understanding of how input decisions can be optimised in response to changing climatic conditions, offering greater insights into the complexities of agricultural systems and the benefits of agro-meteorological services (Hill et al., 2004; Mjelde et al., 1988, 1998). Farm-level ex-ante simulation studies have demonstrated that agro-meteorological services provide substantial economic benefits. On average, the estimated value of these services is approximately US$504.8 per hectare, assuming moderate fluctuations in output prices (Table 2). This valuation highlights the significant potential of agro-meteorological services to enhance agricultural profitability and resilience by optimising decision-making in response to weather and climate variability. This is based on increased net profits through the use of improved weather forecast information.
Table 2. Summary of reviewed studies based on simulation modelling.
3.3.4 Economic value of agro-meteorological services based on producer and consumer surplus
Producer and consumer surpluses provide an alternative approach to estimating the direct economic benefits of agro-meteorological services (Anaman and Lellyett, 1996c). Producer surplus reflects the additional income that farmers gain from utilising improved weather and climate information, while consumer surplus represents the benefits that consumers receive through improved food availability, quality, or affordability resulting from optimised agricultural production. Together, these surpluses offer a comprehensive measure of the economic value generated by agro-meteorological services within agricultural markets. For example, in the USA, Canada and Australia, wheat producers captured economic surpluses through the use of seasonal weather forecasts (Hill et al., 2004). At the macroeconomic level, general equilibrium models and input–output analysis are valuable tools for assessing the broader economic impact of seasonal weather forecasts in agriculture. These approaches capture the interconnectedness between consumers and industries, allowing for a comprehensive evaluation of how improved weather forecasts influence agricultural productivity, supply chains, and market dynamics. By modelling these interdependencies, these methods provide insights into the ripple effects of agro-meteorological services on national and regional economies (Bruno Soares et al., 2018). For example, Frei (2010) estimated a minimum of US$111.75 economic and social benefits of weather forecasts in Switzerland. However, the application of these techniques is limited since data of social accounting matrices, especially in Africa, are of poor quality and, in some cases, difficult to access. The reviewed literature reveals that agro-meteorological services generate significant economic benefits at the societal level. Using various macroeconomic models, the average annual economic benefits attributed to the use of these services are estimated at US$14.24 billion (Table 3).
Table 3. Summary of reviewed studies based on producer and consumer surplus.
3.4 Current and preferred form and type of media to receive weather forecast services among farmers
Print media, television, radios and extension agents are the traditional means mostly used to convey weather forecast messages to farmers (Hansen et al., 2019; Makaudze, 2016; Rasmussen et al., 2014). The relative use and importance of these different forms of media differ from one continent, region and country to another. However, these dissemination platforms are insufficient, especially in Africa, since most farmers do not have access to print media and do not own a television or a radio set. Again, the language used through these communication channels, in most cases, is not convenient for smallholder farmers to decode and translate weather forecasts into action at the farm level (Grey, 2019). Furthermore, dwindling budget allocation towards agricultural extension delivery in most countries also renders challenges for farmers to access weather forecasts through extension agents (Owolabi and Yekinni, 2022). Nevertheless, given an increase in mobile phone penetration in recent years, most farmers are increasingly interested in receiving agricultural advice, including weather forecast information through mobile phones (Djido et al., 2021; Etwire et al., 2017). Farmers prefer mostly text and voice messages of weather forecast information in local languages (Chiputwa et al., 2022; Etwire et al., 2017; Rasmussen et al., 2014).
4 Discussion
Generally, investments in advanced meteorological infrastructure are justified by the magnitude of societal benefits they generate. Consequently, national meteorological and hydrological services increasingly face pressure to provide robust, quantitative evidence demonstrating the returns to public investment, particularly in agriculture, where weather and climate services directly influence production decisions, risk management, and resilience to climate variability. The findings of this review respond directly to this need by synthesising empirical evidence showing substantial and growing economic benefits of agro-meteorological services across diverse contexts. The estimated global annual value of approximately US$590 million reinforces the argument that investments in agro-meteorological services yield returns that far exceed their costs. Such evidence strengthens the policy case for sustained and increased public funding to expand, modernise, and tailor agro-meteorological services to farmers’ needs (Amegnaglo et al., 2017; Perrels et al., 2013; WMO, 2015).
The review revealed that the primary methods used to estimate the economic value of agro-meteorological services include contingent valuation, choice experiments, travel cost methods, and simulation techniques, such as crop yield or net returns modelling (Amegnaglo et al., 2017, 2022; Jones et al., 2000; Makaudze, 2016; Mjelde et al., 1998; Ouédraogo et al., 2018; Paparrizos et al., 2021; Park et al., 2016; Rollins and Shaykewich, 2003; Tesfaye et al., 2019; Yu et al., 2008; Yuan et al., 2016). In addition to these techniques, producer surpluses and benefit–cost ratios were also be employed to estimate the economic value of agro-meteorological services (Anaman and Lellyett, 1996b). Overall, there exists a considerable total economic value of agro-meteorological services globally. Across different locations and contexts, it has been noted that accuracy, a lead time of at least 3 months ahead, ease of understanding and location-specific forecasts are critical attributes for agro-meteorological services to be useful among farmers (Anaman and Lellyett, 1996b; Dinh, 2020; Fernandez et al., 2016; Jones et al., 2000; Makaudze, 2016; Marshall et al., 1996; Mjelde et al., 1998; Yu et al., 2008; Zinyengere et al., 2011). Dissemination channels, especially through mobile phone also enhance farmers’ propensity to use weather forecast information to tailor farming decisions (Chiputwa et al., 2022; Djido et al., 2021; Etwire et al., 2017).
Despite the strong evidence on economic value, the review reveals a notable gap in the literature regarding political economy dimensions that shape both the provision and valuation of agro-meteorological services. Most existing studies are firmly rooted in neoclassical economic frameworks and focus on individual farmers’ willingness to pay, typically estimated through micro-level analyses that assume rational decision-making and well-functioning information environments. While these approaches provide important insights into private benefits, they pay limited attention to broader political, institutional, and social factors that influence who accesses services, how benefits are distributed, and why investments may be constrained. As a result, current valuation estimates may understate or misrepresent the true societal value of agro-meteorological services, particularly in contexts characterised by inequality, weak institutions, or infrastructural deficits.
Funding for agro-meteorological services are inherently political process, shaped by power relations, governance structures, and competing development priorities. As highlighted by this review, economic valuation alone is insufficient to explain investment outcomes without accounting for the political, economic, social, and cultural contexts in which agro-meteorological services are produced and used (Bruno Soares et al., 2018; Mubaya and Mafongoya, 2017; Munoz-Carrier et al., 2020; Tanner and Allouche, 2011). Political economy analysis provides a critical lens for understanding how factors such as marginalisation, social class, trust in institutions, governance quality, and participation in decision-making influence both the perceived and realised value of agro-meteorological services. Integrating political economy perspectives with neoclassical valuation approaches can therefore yield a more comprehensive economic valuation and provide insights on how investments can be better targeted to enhance equity, effectiveness, and resilience (Ruth, 2010; Di Gregorio et al., 2019; Mubaya and Mafongoya, 2017; Vaughan et al., 2018).
Institutional arrangements and governance processes emerge as particularly important determinants of value. Government policies, regulatory frameworks, and coordination among meteorological agencies, extension services, and local institutions can either enable or constrain farmers’ ability to benefit from weather forecasts (Bruno Soares et al., 2018; Hansen, 2002; Jones et al., 2000; Mjelde et al., 1996). For instance, the legitimacy of the weather forecast communicators is fundamental to the value and use of agro-meteorological services (Patt and Gwata, 2002). Religious factors and power dynamics between farmers and producers of weather forecasts, as well as local governance structures, also determine the benefits and use of weather forecasts at the local level (Bruno Soares et al., 2018; Patt and Gwata, 2002). Institutional arrangements in communicating seasonal weather forecasts also imply the value and use of forecasts among farmers (Patt and Gwata, 2002). Thus, a delay in disseminating seasonal weather forecasts to farmers due to bureaucratic communication protocols within government agencies limits the value and relevance of forecasts to farmers. These findings underscore the importance of institutional reforms and improved stakeholder coordination as complementary investments alongside technical improvements in forecasting capacity.
Furthermore, the review also highlights a methodological imbalance in existing research, which predominantly captures farmers’ perspectives while largely overlooking the views of elites and decision-makers. Policymakers, politicians, and traditional leaders play a decisive role in shaping national priorities, allocating resources, and legitimising agro-meteorological services within broader development agendas (Ayittey, 2022). By neglecting these actors, many valuation studies fail to capture the political drivers of investment decisions and service delivery outcomes. Incorporating elite perspectives would improve understanding of how policy priorities are set and how evidence on economic value is translated—or not—into actual investments.
Given the central role of elites in designing and implementing climate service strategies, future research should adopt mixed-methods approaches that integrate quantitative valuation with qualitative political economy analysis (Ayittey, 2022; Soko et al., 2023). Examining the interactions among markets, communities, and the state would enable a more holistic assessment of the factors shaping investment decisions and farmers’ willingness to pay (Manzvera and Anaman, 2024). Such an approach would also help explain observed disparities in geographical coverage and scale of valuation studies, particularly the underrepresentation of vulnerable regions such as Africa in macro-level assessments.
Finally, the review identifies limited empirical attention to the economic value of indigenous weather forecasting systems. Although a few studies have begun to quantify the benefits of indigenous forecasts, this remains an underexplored area despite their widespread use among smallholder farmers, particularly in Africa (Amegnaglo et al., 2022; Gbangou et al., 2021; Gwenzi et al., 2016; Kalanda-Joshua et al., 2011; Radeny et al., 2019; Roncoli et al., 2002). Integrating indigenous knowledge systems with modern agro-meteorological services offers significant potential to enhance trust, relevance, and uptake of forecasts (Hiwasaki et al., 2014; Kalanda-Joshua et al., 2011). Evidence from contexts such as Zimbabwe demonstrates that alignment between scientific and indigenous forecasts can substantially improve usability and decision-making. Expanding research in this area would not only enrich valuation evidence but also support more inclusive and context-sensitive climate service design (Patt and Gwata, 2002).
Like other studies, this research has some limitations. First, the review was limited to articles published in English, which may have excluded relevant studies in other languages and introduced potential language bias. Second, although most reviewed studies reported economic values in US$, estimates from Australia required conversion using prevailing exchange rates at the time of review rather than those applicable when the original studies were conducted. Given exchange rate fluctuations and inflation, this may have led to slight over- or underestimation of reported values. Finally, the review focused exclusively on the agricultural sector; therefore, the economic value of weather and climate services in other sectors, such as transport, aviation, mining, and tourism, was not assessed. Future research incorporating these sectors would provide a more comprehensive understanding of the overall economic value of weather and climate services across the economy.
5 Conclusion and recommendations
This paper synthesises empirical evidence on the economic value of agro-meteorological services through a comprehensive review of international literature and a meta-analysis. The findings demonstrate that agro-meteorological services generate substantial economic value, with an estimated average annual value of US$0.59 billion (US$590 million). These services play a crucial role in enhancing agricultural productivity, improving decision-making, and mitigating the adverse effects of climate variability and change. The evidence highlights the importance of sustained investment in advanced meteorological infrastructure and forecasting capacity, including modern equipment and communication systems, to deliver accurate, timely, and location-specific information. Such investments should be regarded not as recurrent costs, but as high-return public goods that yield significant societal benefits and strengthen agricultural resilience.
Despite the clear economic value identified, the review reveals important gaps in understanding the political, economic, and institutional factors that shape investment decisions and service delivery outcomes. Future research should therefore integrate political economy analysis to better explain how governance structures, power relations, institutional capacity, and policy priorities influence the development, allocation, and funding of agro-meteorological services. Political economy analysis provides a complementary framework to neoclassical valuation approaches by capturing the roles of markets, communities, and the state in shaping access, equity, and effectiveness. Greater attention to the perspectives of policymakers, politicians, and community leaders is particularly important, as these actors play a central role in determining investment priorities and the sustainability of meteorological services.
Another key priority for future research is the systematic integration of modern agro-meteorological services with indigenous weather knowledge systems, especially in regions dominated by smallholder agriculture, such as Africa. While modern forecasting technologies have been widely evaluated, indigenous forecasting systems remain underrepresented in economic valuation studies despite their long-standing use and cultural relevance. Exploring complementarities between scientific and indigenous knowledge can enhance trust, improve relevance, and increase farmers’ uptake of weather information. Such integration also supports co-production approaches that strengthen local ownership of climate services and improve their effectiveness in building climate resilience.
The review also highlights that existing studies place limited emphasis on the broader, economy-wide benefits of agro-meteorological services beyond the farm level. Improved agricultural outcomes can generate significant spillover effects through linkages with agribusiness, food processing, trade, and employment. These indirect benefits can be rigorously quantified using macroeconomic tools such as input–output analysis, social accounting matrices, and computable general equilibrium models. Incorporating these approaches into future research would enable a more comprehensive estimation of the total economic value of agro-meteorological services and strengthen the case for increased public investment in meteorological infrastructure.
In conclusion, while the evidence confirms that agro-meteorological services deliver substantial economic value, a more comprehensive understanding of their impact requires moving beyond narrow valuation frameworks guided by neoclassical theories. Addressing political economy constraints, integrating indigenous and scientific knowledge systems, and quantifying economy-wide spillover effects are critical for improving policy design and investment decisions. Future research that adopts these integrated approaches will better inform policymakers, promote equitable and effective service delivery, and ensure that investments in agro-meteorological services are sustained and aligned with long-term agricultural and development goals.
Data availability statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
Author contributions
JM: Investigation, Writing – review & editing, Conceptualization, Methodology, Software, Funding acquisition, Resources, Project administration, Formal analysis, Visualization, Writing – original draft, Data curation. KA: Supervision, Data curation, Writing – review & editing, Validation, Conceptualization, Investigation.
Funding
The author(s) declared that financial support was received for this work and/or its publication. This work was supported by the Partnership for Skills in Applied Sciences, Engineering and Technology-Regional Scholarship and Innovation Fund (PASET RSIF) PhD Scholarship and Carnegie Corporation of New York.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that Generative AI was not used in the creation of this manuscript.
Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
References
Anaman (1988). African farm management: Principles and applications with examples. Accra: Ghana Universities Press.
Adams, R. M., Bryant, K. J., Mccarl, B. A., Legler, D. M., O’Brien, J., Solow, A., et al. (1995). Value of improved long-range weather information. Contemp. Econ. Policy 13, 10–19. doi: 10.1111/j.1465-7287.1995.tb00720.x
Adams, R. M., Houston, L. L., McCarl, B. A., Tiscareño L, M., Matus G, J., and Weiher, R. F. (2003). The benefits to Mexican agriculture of an El Niño-southern oscillation (ENSO) early warning system. Agric. For. Meteorol. 115, 183–194. doi: 10.1016/S0168-1923(02)00201-0
Afshin, A., Peñalvo, J. L., Gobbo, L. D., Silva, J., Michaelson, M., O’Flaherty, M., et al. (2017). The prospective impact of food pricing on improving dietary consumption: a systematic review and meta-analysis. PLoS One 12:e0172277. doi: 10.1371/journal.pone.0172277,
Amegnaglo, C. J., Anaman, K. A., Mensah-Bonsu, A., Onumah, E. E., and Amoussouga Gero, F. (2017). Contingent valuation study of the benefits of seasonal climate forecasts for maize farmers in the Republic of Benin, West Africa. Clim. Serv. 6, 1–11. doi: 10.1016/j.cliser.2017.06.007
Amegnaglo, C. J., Mensah-Bonsu, A., and Anaman, K. A. (2022). Use and economic benefits of indigenous seasonal climate forecasts: evidence from Benin, West Africa. Clim. Dev. 14, 909–920. doi: 10.1080/17565529.2022.2027740
Anaman, K. A., and Lellyett, S. C. (1996a). Contingent valuation study of the public weather service in the Sydney metropolitan area. Econ. Pap. 15, 64–77. doi: 10.1111/j.1759-3441.1996.tb00123.x
Anaman, K. A., and Lellyett, S. C. (1996b). Assessment of the benefits of an enhanced weather information service for the cotton industry in Australia. Meteorol. Appl. 3, 127–135. doi: 10.1002/met.5060030203
Anaman, K. A., and Lellyett, S. C. (1996c). Producers’ evaluation of an enhanced weather information service for the cotton industry in Australia. Meteorol. Appl. 3, 113–125. doi: 10.1002/met.5060030202
Anaman, K. A., Lellyett, S. C., and Avsar, G. S. (2000). Assessing the effect of aviation weather forecasts on fuel expenditures of an international airline. Int. J. Transport Econ. 27, 257–277. www.jstor.org/stable/42748264
Anaman, K. A., Lellyett, S. C., Drake, L., Leigh, R. J., Henderson-Sellers, A., Noar, P. F., et al. (1998). Benefits of meteorological services: evidence from recent research in Australia. Meteorol. Appl. 5, 103–115. doi: 10.1017/S1350482798000668
Anaman, K. A., Quaye, R., and Amankwah, E. (2017). Evaluation of public weather services by users in the formal services sector in Accra, Ghana. Mod. Econ. 8:87065. doi: 10.4236/me.2017.87065
An-Vo, D.-A., Radanielson, A. M., Mushtaq, S., Reardon-Smith, K., and Hewitt, C. (2021). A framework for assessing the value of seasonal climate forecasting in key agricultural decisions. Clim. Serv. 22:100234. doi: 10.1016/j.cliser.2021.100234
Ayittey, H.-S. (2022). Formulation and implementation of reproductive health policies in Ghana: analysis of the views of key community leaders and state technocrats. Res. World Econ. 13:42. doi: 10.5430/rwe.v13n1p42
Ayugi, B., Eresanya, E. O., Onyango, A. O., Ogou, F. K., Okoro, E. C., Okoye, C. O., et al. (2022). Review of meteorological drought in Africa: historical trends, impacts, mitigation measures, and prospects. Pure Appl. Geophys. 179, 1365–1386. doi: 10.1007/s00024-022-02988-z,
Bahta, Y. T. (2022). Nexus between coping strategies and households’ agricultural drought resilience to food insecurity in South Africa. Land 11:893. doi: 10.3390/land11060893
Barrett, S., Ndegwa, W., and Maggio, G. (2021). The value of local climate and weather information: an economic valuation of the decentralised meteorological provision in Kenya. Clim. Dev. 13, 173–188. doi: 10.1080/17565529.2020.1745739
Bedasa, Y., and Bedemo, A. (2022). The effect of climate change on food insecurity in the horn of Africa. GeoJournal 88:10733. doi: 10.1007/s10708-022-10733-1
Bert, F. E., Satorre, E. H., Toranzo, F. R., and Podestá, G. P. (2006). Climatic information and decision-making in maize crop production systems of the Argentinean pampas. Agric. Syst. 88, 180–204. doi: 10.1016/j.agsy.2005.03.007
Born, L., Prager, S., Ramirez-Villegas, J., and Imbach, P. (2021). A global meta-analysis of climate services and decision-making in agriculture. Clim. Serv. 22:100231. doi: 10.1016/j.cliser.2021.100231
Bruno Soares, M. (2017). Assessing the usability and potential value of seasonal climate forecasts in land management decisions in the Southwest UK: challenges and reflections. Adv. Sci. Res. 14, 175–180. doi: 10.5194/asr-14-175-2017
Bruno Soares, M., Daly, M., and Dessai, S. (2018). Assessing the value of seasonal climate forecasts for decision-making. WIREs Clim. Change 9:523. doi: 10.1002/wcc.523
Campos, M., Drake, L., and Anaman, K. A. (1999). Impact of tropical cyclone warning information on incomes of commercial tourist accommodation operators along the Queensland coast. Econ. Anal. Policy 29, 207–215. doi: 10.1016/S0313-5926(99)50021-0
Changnon, S. A. (2002). Impacts of the midwestern drought forecasts of 2000. J. Appl. Meteorol. 41, 1042–1052. doi: 10.1175/1520-0450(2002)041<>2.0.CO;2
Chen, C. C., McCarl, B., and Hill, H. (2002). Agricultural value of ENSO information under alternative phase definition. Clim. Chang. 54, 305–325. doi: 10.1023/a:1016160218221
Chiputwa, B., Blundo-Canto, G., Steward, P., Andrieu, N., and Ndiaye, O. (2022). Co-production, uptake of weather and climate services, and welfare impacts on farmers in Senegal: a panel data approach. Agric. Syst. 195:103309. doi: 10.1016/j.agsy.2021.103309
Costello, C. J., Adams, R. M., and Polasky, S. (1998). The value of El Niño forecasts in the management of salmon: a stochastic dynamic assessment. Am. J. Agric. Econ. 80, 765–777. doi: 10.2307/1244062
de la Tozier Poterie, A., Castro, E., Rahaman, H., Heinrich, D., Clatworthy, Y., and Mundorega, L. (2023). Anticipatory action to manage climate risks: lessons from the red cross red crescent in southern Africa, Bangladesh, and beyond. Clim. Risk Manag. 39:100476. doi: 10.1016/j.crm.2023.100476
de la Tozier Poterie, A., Clatworthy, Y., Easton-Calabria, E., de Coughlan Perez, E., Lux, S., and van Aalst, M. (2022). Managing multiple hazards: lessons from anticipatory humanitarian action for climate disasters during COVID-19. Clim. Dev. 14, 374–388. doi: 10.1080/17565529.2021.1927659
de la Tozier Poterie, A. S., Jjemba, W. E., Singh, R., de Coughlan Perez, E., Costella, C. V., and Arrighi, J. (2018). Understanding the use of 2015–2016 El Niño forecasts in shaping early humanitarian action in eastern and southern Africa. Int. J. Disaster Risk Reduct. 30, 81–94. doi: 10.1016/j.ijdrr.2018.02.025
Dettori, J. R., Norvell, D. C., and Chapman, J. R. (2021). Seeing the Forest by looking at the trees: how to interpret a Meta-analysis Forest plot. Global Spine Journal 11, 614–616. doi: 10.1177/21925682211003889,
Di Gregorio, M., Fatorelli, L., Paavola, J., Locatelli, B., Pramova, E., Nurrochmat, D. R., et al. (2019). Multi-level governance and power in climate change policy networks. Glob. Environ. Change 54, 64–77. doi: 10.1016/j.gloenvcha.2018.10.003
Dinh, D. T. (2020). Economic valuation of agricultural producers’ WTP for the weather information service – a case study in Central Vietnam. E3S Web of Conferences 203:03016. doi: 10.1051/e3sconf/202020303016
Dinku, T., Faniriantsoa, R., Islam, S., Nsengiyumva, G., and Grossi, A. (2022). The climate data tool: enhancing climate services across Africa. Front. Clim. 3:787519. doi: 10.3389/fclim.2021.787519
Djido, A., Zougmoré, R. B., Houessionon, P., Ouédraogo, M., Ouédraogo, I., and Seynabou Diouf, N. (2021). To what extent do weather and climate information services drive the adoption of climate-smart agriculture practices in Ghana? Clim. Risk Manag. 32:100309. doi: 10.1016/j.crm.2021.100309
Donnelly, W. A. (1991). A survey in applied environometrics: the hedonic valuing of environmental amenities. Environ. Int. 17, 547–558. doi: 10.1016/0160-4120(91)90167-O
Douglas, A. J., and Taylor, J. G. (1998). A new model for the travel cost method: the total expenses approach. Environ. Model. Softw. 14, 81–92. doi: 10.1016/S1364-8152(98)00060-7
Dukuzumuremyi, J. P. C., Acheampong, K., Abesig, J., and Luo, J. (2020). Knowledge, attitude, and practice of exclusive breastfeeding among mothers in East Africa: a systematic review. Int. Breastfeed. J. 15:70. doi: 10.1186/s13006-020-00313-9,
Etwire, P. M., Buah, S., Ouédraogo, M., Zougmoré, R., Partey, S. T., Martey, E., et al. (2017). An assessment of mobile phone-based dissemination of weather and market information in the upper west region of Ghana. Agric. Food Secur. 6:8. doi: 10.1186/s40066-016-0088-y
FAO (2019). Handbook on climate information for farming communities -what farmers need and what is available. Rome: FAO.
Fernandez, M. A., Huang, P., McCarl, B., and Mehta, V. (2016). Value of decadal climate variability information for agriculture in the Missouri River basin. Clim. Chang. 139, 517–533. doi: 10.1007/s10584-016-1807-x
Figini, P., Cicognani, S., and Zirulia, L. (2022). Booking in the rain. Testing the impact of public information on prices. Ital. Econ. J. 9. doi: 10.1007/s40797-022-00199-y
Freebairn, J. W., and Zillman, J. W. (2002). Economic benefits of meteorological services. Meteorol. Appl. 9, 33–44. doi: 10.1017/S1350482702001044
Freeman, A. M. (1981). “Hedonic prices, property values and measuring environmental benefits: a survey of the issues” in Measurement in public choice. ed. S. Strøm (London: Palgrave Macmillan UK), 13–32.
Frei, T. (2010). Economic and social benefits of meteorology and climatology in Switzerland. Meteorol. Appl. 17, 39–44. doi: 10.1002/met.156
Gbangou, T., Van Slobbe, E., Ludwig, F., Kranjac-Berisavljevic, G., and Paparrizos, S. (2021). Harnessing local forecasting knowledge on weather and climate in Ghana: documentation, skills, and integration with scientific forecasting knowledge. Weather. Clim. Soc. 13, 23–37. doi: 10.1175/WCAS-D-20-0012.1
Gibbons, S., Mourato, S., and Resende, G. M. (2014). The amenity value of English nature: a hedonic price approach. Environ. Resour. Econ. 57, 175–196. doi: 10.1007/s10640-013-9664-9
Grey, M. S. (2019). Accessing seasonal weather forecasts and drought prediction information for rural households in Chirumhanzu district, Zimbabwe. Jamba 11:777. doi: 10.4102/jamba.v11i1.777,
Gros, C., Bailey, M., Schwager, S., Hassan, A., Zingg, R., Uddin, M. M., et al. (2019). Household-level effects of providing forecast-based cash in anticipation of extreme weather events: quasi-experimental evidence from humanitarian interventions in the 2017 floods in Bangladesh. Int. J. Disaster Risk Reduct. 41:101275. doi: 10.1016/j.ijdrr.2019.101275
Gros, C., Easton-Calabria, E., Bailey, M., Dagys, K., de Perez, E. C., Sharavnyambuu, M., et al. (2022). The effectiveness of forecast-based humanitarian assistance in anticipation of extreme winters: a case study of vulnerable herders in Mongolia. Disasters 46, 95–118. doi: 10.1111/disa.12467,
Gwenzi, J., Mashonjowa, E., Mafongoya, P. L., Rwasoka, D. T., and Stigter, K. (2016). The use of indigenous knowledge systems for short- and long-range rainfall prediction and farmers’ perceptions of science-based seasonal forecasts in Zimbabwe. Int. J. Clim. Change Strateg. Manag. 8, 440–462. doi: 10.1108/IJCCSM-03-2015-0032
Haddaway, N. R., Macura, B., Whaley, P., and Pullin, A. S. (2018). ROSES reporting standards for systematic evidence syntheses: pro forma, flow-diagram and descriptive summary of the plan and conduct of environmental systematic reviews and systematic maps. Environ. Evid. 7:7. doi: 10.1186/s13750-018-0121-7
Hallegatte, S. (2012). A cost-effective solution to reduce disaster losses in developing countries: hydro-meteorological services, early warning, and evacuation. London: The World Bank.
Hansen, J. W. (2002). Realizing the potential benefits of climate prediction to agriculture: issues, approaches, challenges. Agric. Syst. 74, 309–330. doi: 10.1016/S0308-521X(02)00043-4
Hansen, J. W., Mishra, A., Rao, K. P. C., Indeje, M., and Ngugi, R. K. (2009). Potential value of GCM-based seasonal rainfall forecasts for maize management in semi-arid Kenya. Agric. Syst. 101, 80–90. doi: 10.1016/j.agsy.2009.03.005
Hansen, J. W., Vaughan, C., Kagabo, D. M., Dinku, T., Carr, E. R., Körner, J., et al. (2019). Climate services can support African farmers’ context-specific adaptation needs at scale. Front. Sustain. Food Syst. 3:21. doi: 10.3389/fsufs.2019.00021
Hewitt, C. D., Allis, E., Mason, S. J., Muth, M., Pulwarty, R., Shumake-Guillemot, J., et al. (2020). Making society climate resilient: international progress under the global framework for climate services. Bull. Am. Meteorol. Soc. 101, E237–E252. doi: 10.1175/BAMS-D-18-0211.1
Hewitt, C. D., and Stone, R. (2021). Climate services for managing societal risks and opportunities. Clim. Serv. 23:100240. doi: 10.1016/j.cliser.2021.100240
Higgins, J. P. T., Thompson, S. G., Deeks, J. J., and Altman, D. G. (2003). Measuring inconsistency in meta-analyses. BMJ 327, 557–560. doi: 10.1136/bmj.327.7414.557,
Hill, H. S. J., Mjelde, J. W., Love, H. A., Rubas, D. J., Fuller, S. W., Rosenthal, W., et al. (2004). Implications of seasonal climate forecasts on world wheat trade: a stochastic, dynamic analysis. Can. J. Agric. Econ. 52, 289–312. doi: 10.1111/j.1744-7976.2004.tb00371.x
Hiwasaki, L., Luna, E., Syamsidik,, and Shaw, R. (2014). Process for integrating local and indigenous knowledge with science for hydro-meteorological disaster risk reduction and climate change adaptation in coastal and small island communities. Int. J. Disaster Risk Reduct. 10, 15–27. doi: 10.1016/j.ijdrr.2014.07.007
Jones, J. W., Hansen, J. W., Royce, F. S., and Messina, C. D. (2000). Potential benefits of climate forecasting to agriculture. Agric. Ecosyst. Environ. 82, 169–184. doi: 10.1016/S0167-8809(00)00225-5
Jury, M. R. (2002). Economic impacts of climate variability in South Africa and development of resource prediction models. J. Appl. Meteorol. 41, 46–55. doi: 10.1175/1520-0450(2002)041<>2.0.CO;2
Kalanda-Joshua, M., Ngongondo, C., Chipeta, L., and Mpembeka, F. (2011). Integrating indigenous knowledge with conventional science: enhancing localised climate and weather forecasts in Nessa, Mulanje, Malawi. Phys. Chem. Earth 36, 996–1003. doi: 10.1016/j.pce.2011.08.001
Kim, M. K., and McCarl, B. A. (2005). The agricultural value of information on the North Atlantic oscillation: yield and economic effects. Climatic Change, 71, 117–139.
Kull, D., Riishojgaard, L. P., Eyre, J., and Varley, R. A. (2021). The value of surface-based meteorological observation data [working paper]. London: World Bank.
Lazo, K., Morss, E., and Demuth, J. (2009). 300 billion served: sources, perceptions, uses, and values of weather forecasts. Bull. Am. Meteorol. Soc. 90, 785–798. doi: 10.1175/2008BAMS2604.1
Lechthaler, F., and Vinogradova, A. (2017). The climate challenge for agriculture and the value of climate services: application to coffee-farming in Peru. Eur. Econ. Rev. 94, 45–70. doi: 10.1016/j.euroecorev.2017.02.002
Leigh, R. J. (1995). Economic benefits of terminal aerodrome forecasts (TAFs) for Sydney airport, Australia. Meteorol. Appl. 2, 239–247. doi: 10.1002/met.5060020307
Leigh, R. J., Drake, L., and Thampapillai, D. J. (1998). An economic analysis of terminal aerodrome forecasts with special reference to Sydney airport. J. Transp. Econ. Policy 32, 377–392. www.jstor.org/stable/20053780
Lin, H.-I., Liou, J.-L., and Hsu, S.-H. (2019). Economic valuation of public meteorological information services—a case study of agricultural producers in Taiwan. Atmos. 10:753. doi: 10.3390/atmos10120753
Luu, T. T. G., Whitney, C., Biber-Freudenberger, L., and Luedeling, E. (2022). Decision analysis of agro-climate service scaling – a case study in Dien Bien District, Vietnam. Clim. Serv. 27:100313. doi: 10.1016/j.cliser.2022.100313
Maïga, W. H. E., Porgo, M., Zahonogo, P., Amegnaglo, C. J., Coulibaly, D. A., Flynn, J., et al. (2020). A systematic review of employment outcomes from youth skills training programmes in agriculture in low- and middle-income countries. Nature Food 1, 605–619. doi: 10.1038/s43016-020-00172-x,
Makaudze, E. M. (2016). “Assessing the economic value of El Niño-based seasonal climate forecasts for smallholder farmers in Zimbabwe” in Climate change and multi-dimensional sustainability in African agriculture. eds. R. Lal, D. Kraybill, D. O. Hansen, B. R. Singh, T. Mosogoya, and L. O. Eik (Cham: Springer International Publishing), 591–612.
Manzvera, J., and Anaman, K. A. (2024). Political economy analysis of the dissemination and use of seasonal weather forecasts and services in Zimbabwe. J. Infrastruct. Policy Dev. 8:8098. doi: 10.24294/jipd8098
Manzvera, J., Anaman, K. A., Mensah-Bonsu, A., and Barimah, A. (2024). The economic value of seasonal weather and climate services for maize farmers in Manicaland Province of Zimbabwe. Heliyon 10:e40781. doi: 10.1016/j.heliyon.2024.e40781,
Marshall, G. R., Parton, K. A., and Hammer, G. L. (1996). Risk attitude, planting conditions and the value of seasonal forecasts to a dryland wheat grower*. Aust. J. Agric. Econ. 40, 211–233. doi: 10.1111/j.1467-8489.1996.tb00595.x
Meza, F. J., Hansen, J. W., and Osgood, D. (2008). Economic value of seasonal climate forecasts for agriculture: review of ex-ante assessments and recommendations for future research. J. Appl. Meteorol. Climatol. 47, 1269–1286. doi: 10.1175/2007JAMC1540.1
Mitter, H., and Schmid, E. (2019). Computing the economic value of climate information for water stress management exemplified by crop production in Austria. Agric. Water Manag. 221, 430–448. doi: 10.1016/j.agwat.2019.04.005
Mjelde, J. W., Hill, H. S. J., and Griffiths, J. F. (1998). A review of current evidence on climate forecasts and their economic effects in agriculture. Am. J. Agric. Econ. 80, 1089–1095. doi: 10.2307/1244210
Mjelde, J. W., Penson, J. B., and Nixon, C. J. (2000). Dynamic aspects of the impact of the use of perfect climate forecasts in the corn belt region. J. Appl. Meteorol. 39, 67–79. doi: 10.1175/1520-0450(2000)039<>2.0.CO;2
Mjelde, J. W., Sonka, S. T., Dixon, B. L., and Lamb, P. J. (1988). Valuing forecast characteristics in a dynamic agricultural production system. Am. J. Agric. Econ. 70, 674–684. doi: 10.2307/1241506
Mjelde, J. W., Thompson, T. N., and Nixon, C. J. (1996). Government institutional effects on the value of seasonal climate forecasts. Am. J. Agric. Econ. 78, 175–188. doi: 10.2307/1243789
Mubaya, C. P., and Mafongoya, P. (2017). The role of institutions in managing local level climate change adaptation in semi-arid Zimbabwe. Clim. Risk Manag. 16, 93–105. doi: 10.1016/j.crm.2017.03.003
Mudombi, S., and Nhamo, G. (2014). Access to weather forecasting and early warning information by communal farmers in Seke and Murewa districts, Zimbabwe. J. Hum. Ecol. 48, 357–366. doi: 10.1080/09709274.2014.11906805
Munoz-Carrier, G., Thomsen, D., and Pickering, G. J. (2020). Psychological and experiential factors affecting climate change perception: learnings from a transnational empirical study and implications for framing climate-related flood events. Environ. Res. Commun. 2:045003. doi: 10.1088/2515-7620/ab89f9
Mushtaq, S., Chen, C., Hafeez, M., Maroulis, J., and Gabriel, H. (2012). THE economic value of improved agrometeorological information to irrigators amid climate variability: the economic value of improved agrometeorological information. Int. J. Climatol. 32, 567–581. doi: 10.1002/joc.2015
Nelson, R. R., and Winter, S. G. (1964). A case study in the economics of information and coordination: the weather forecasting system. Q. J. Econ. 78, 420–441. doi: 10.2307/1879475
Nguyen, T. C., Robinson, J., Kaneko, S., and Komatsu, S. (2013). Estimating the value of economic benefits associated with adaptation to climate change in a developing country: a case study of improvements in tropical cyclone warning services. Ecol. Econ. 86, 117–128. doi: 10.1016/j.ecolecon.2012.11.009
Ngwili, N., Johnson, N., Wahome, R., Githigia, S., Roesel, K., and Thomas, L. (2021). A qualitative assessment of the context and enabling environment for the control of Taenia solium infections in endemic settings. PLoS Negl. Trop. Dis. 15:e0009470. doi: 10.1371/journal.pntd.0009470,
Ogunyiola, A., Gardezi, M., and Vij, S. (2022). Smallholder farmers’ engagement with climate smart agriculture in Africa: role of local knowledge and upscaling. Clim. Pol. 22, 411–426. doi: 10.1080/14693062.2021.2023451
Ouédraogo, M., Barry, S., Zougmoré, R., Partey, S., Somé, L., and Baki, G. (2018). Farmers’ willingness to pay for climate information services: evidence from cowpea and sesame producers in northern Burkina Faso. Sustainability 10:611. doi: 10.3390/su10030611
Owolabi, A. O., and Yekinni, O. T. (2022). Utilisation of information and communication technologies for agricultural extension service delivery in public and non-public organisations in southwestern Nigeria. Heliyon 8:e10676. doi: 10.1016/j.heliyon.2022.e10676,
Paparrizos, S., Kumar, U., Amjath-Babu, T. S., and Ludwig, F. (2021). Are farmers willing to pay for participatory climate information services? Insights from a case study in peri-urban Khulna, Bangladesh. Clim. Serv. 23:100241. doi: 10.1016/j.cliser.2021.100241
Park, S.-Y., Lim, S.-Y., and Yoo, S.-H. (2016). The economic value of the National Meteorological Service in the Korean household sector: a contingent valuation study. Sustainability 8:834. doi: 10.3390/su8090834
Park, S.-Y., and Yoo, S.-H. (2018). The public value of improving a weather forecasting system in Korea: a choice experiment study. Appl. Econ. 50, 1644–1658. doi: 10.1080/00036846.2017.1368995
Patt, A., and Gwata, C. (2002). Effective seasonal climate forecast applications: examining constraints for subsistence farmers in Zimbabwe. Glob. Environ. Change 12, 185–195. doi: 10.1016/S0959-3780(02)00013-4
Patt, A., Suarez, P., and Gwata, C. (2005). Effects of seasonal climate forecasts and participatory workshops among subsistence farmers in Zimbabwe. Proc. Natl. Acad. Sci. 102, 12623–12628. doi: 10.1073/pnas.0506125102,
Peñaloza, D., Mata, É., Fransson, N., Fridén, H., Samperio, Á., Quijano, A., et al. (2022). Social and market acceptance of photovoltaic panels and heat pumps in Europe: a literature review and survey. Renew. Sust. Energ. Rev. 155:111867. doi: 10.1016/j.rser.2021.111867
Perrels, A., Frei, T., Espejo, F., Jamin, L., and Thomalla, A. (2013). Socio-economic benefits of weather and climate services in Europe. Adv. Sci. Res. 10, 65–70. doi: 10.5194/asr-10-65-2013
Petersen, E. H., and Fraser, R. W. (2001). An assessment of the value of seasonal forecasting technology for Western Australian farmers. Agric. Syst. 70, 259–274. doi: 10.1016/S0308-521X(01)00012-9
Quiroga, S., Garrote, L., Fernandez-Haddad, Z., and Iglesias, A. (2011). Valuing drought information for irrigation farmers: potential development of a hydrological risk insurance in Spain. Span. J. Agric. Res. 9:63. doi: 10.5424/sjar/20110904-063-11
Radeny, M., Desalegn, A., Mubiru, D., Kyazze, F., Mahoo, H., Recha, J., et al. (2019). Indigenous knowledge for seasonal weather and climate forecasting across East Africa. Clim. Chang. 156, 509–526. doi: 10.1007/s10584-019-02476-9
Rasmussen, L. V., Mertz, O., Rasmussen, K., Nieto, H., Ali, A., and Maiga, I. (2014). Weather, climate, and resource information should meet the needs of Sahelian pastoralists. Weather. Clim. Soc. 6, 482–494. doi: 10.1175/WCAS-D-14-00010.1
Reijnders, J. S. A. M., Ehrt, U., Weber, W. E. J., Aarsland, D., and Leentjens, A. F. G. (2008). A systematic review of prevalence studies of depression in Parkinson’s disease: the prevalence of depression in PD. Mov. Disord. 23, 183–189. doi: 10.1002/mds.21803
Roine, R., Ohinmaa, A., and Hailey, D. (2001). Assessing telemedicine: a systematic review of the literature. CMAJ 165, 765–771.
Rollins, K. S., and Shaykewich, J. (2003). Using willingness-to-pay to assess the economic value of weather forecasts for multiple commercial sectors. Meteorol. Appl. 10, 31–38. doi: 10.1017/S1350482703005048
Roncoli, C., Ingram, K., and Kirshen, P. (2002). Reading the rains: local knowledge and rainfall forecasting in Burkina Faso. Soc. Nat. Resour. 15, 409–427. doi: 10.1080/08941920252866774
Rosen, S. (1974). Hedonic prices and implicit markets: product differentiation in pure competition. J. Polit. Econ. 82, 34–55. doi: 10.1086/260169
Roudier, P., Alhassane, A., Baron, C., Louvet, S., and Sultan, B. (2016). Assessing the benefits of weather and seasonal forecasts to millet growers in Niger. Agric. For. Meteorol. 223, 168–180. doi: 10.1016/j.agrformet.2016.04.010
Ruth, M. (2010). Economic and Social Benefits of Climate Information: Assessing the Cost of Inaction, 53 Procedia Environmental Sciences. World Climate Conference-3. 1, 387–394.
Salehi, M., Amanat, M., Mohammadi, M., Salmanian, M., Rezaei, N., Saghazadeh, A., et al. (2021). The prevalence of post-traumatic stress disorder related symptoms in coronavirus outbreaks: a systematic-review and meta-analysis. J. Affect. Disord. 282, 527–538. doi: 10.1016/j.jad.2020.12.188,
Santeramo, F. G., and Lamonaca, E. (2019). The effects of non-tariff measures on Agri-food trade: a review and meta-analysis of empirical evidence. J. Agric. Econ. 70, 595–617. doi: 10.1111/1477-9552.12316
Soko, N. N., Kaitibie, S., and Ratna, N. N. (2023). Does institutional quality affect the impact of public agricultural spending on food security in sub-Saharan Africa and Asia? Glob. Food Secur. 36:100668. doi: 10.1016/j.gfs.2022.100668
Solow, A. R., Adams, R. F., Bryant, K. J., Legler, D. M., O’Brien, J. J., McCarl, B. A., et al. (1998). The value of improved ENSO prediction to U.S. agriculture. Clim. Chang. 39, 47–60. doi: 10.1023/A:1005342500057
Stanley, T. D., Doucouliagos, C., and Jarrell, S. B. (2008). Meta-regression analysis as the socio-economics of economics research. J. Socio-Econ. 37, 276–292. doi: 10.1016/j.socec.2006.12.030
Takle, E. S., Anderson, C. J., Andresen, J., Angel, J., Elmore, R. W., Gramig, B. M., et al. (2014). Climate forecasts for corn producer decision making. Earth Interact. 18, 1–8. doi: 10.1175/2013EI000541.1
Tanner, T., and Allouche, J. (2011). Towards a new political economy of climate change and development. IDS Bull. 42, 1–14. doi: 10.1111/j.1759-5436.2011.00217.x
Tarchiani, V., Camacho, J., Coulibaly, H., Rossi, F., and Stefanski, R. (2018). Agrometeorological services for smallholder farmers in West Africa. Adv. Sci. Res. 15, 15–20. doi: 10.5194/asr-15-15-2018
Tesfaye, A., Hansen, J., Kassie, G. T., Radeny, M., and Solomon, D. (2019). Estimating the economic value of climate services for strengthening resilience of smallholder farmers to climate risks in Ethiopia: a choice experiment approach. Ecol. Econ. 162, 157–168. doi: 10.1016/j.ecolecon.2019.04.019
Vaughan, C. (2022). Growing evidence of the use and utility of climate services for smallholder farmers: A summary of recent findings from the CCAFS flagship on climate services and safety nets. Cham: Springer.
Vaughan, C., and Dessai, S. (2014). Climate services for society: origins, institutional arrangements, and design elements for an evaluation framework: climate services for society. Wiley Interdiscip. Rev. Clim. Chang. 5, 587–603. doi: 10.1002/wcc.290,
Vaughan, C., Dessai, S., and Hewitt, C. (2018). Surveying climate services: what can we learn from a bird’s-eye view? Weather. Clim. Soc. 10, 373–395. doi: 10.1175/wcas-d-17-0030.1
Verhagen, A. P., and Ferreira, M. L. (2014). Forest plots. J. Physiother. 60, 170–173. doi: 10.1016/j.jphys.2014.06.021,
von Gruenigen, S., Willemse, S., and Frei, T. (2014). Economic value of meteorological services to Switzerland’s airlines: the case of TAF at Zurich airport. Weather. Clim. Soc. 6, 264–272. doi: 10.1175/wcas-d-12-00042.1
Wehde, W., Ripberger, J. T., Jenkins-Smith, H., Jones, B. A., Allan, J. N., and Silva, C. L. (2021). Public willingness to pay for continuous and probabilistic hazard information. Nat. Hazards Rev. 22:04021004. doi: 10.1061/(ASCE)NH.1527-6996.0000444
Wilks, D. S., and Wolfe, D. W. (1998). Optimal use and economic value of weather forecasts for lettuce irrigation in a humid climate. Agric. For. Meteorol. 89, 115–129. doi: 10.1016/S0168-1923(97)00066-X
WMO (2015). Valuing weather and climate: Economic assessment of meteorological and hydrological services. Geneva: WMO.
Yu, Q., Wang, E., and Smith, C. J. (2008). A modelling investigation into the economic and environmental values of ‘perfect’ climate forecasts for wheat production under contrasting rainfall conditions. Int. J. Climatol. 28, 255–266. doi: 10.1002/joc.1520
Yuan, H., Sun, M., and Wang, Y. (2016). Assessment of the benefits of the Chinese public weather service. Meteorol. Appl. 23, 132–139. doi: 10.1002/met.1539
Zamasiya, B., Nyikahadzoi, K., and Mukamuri, B. B. (2017). Factors influencing smallholder farmers’ behavioural intention towards adaptation to climate change in transitional climatic zones: a case study of Hwedza District in Zimbabwe. J. Environ. Manag. 198, 233–239. doi: 10.1016/j.jenvman.2017.04.073,
Keywords: agro-meteorological services, climate change, meta-analysis, meteorological services, weather forecasts
Citation: Manzvera J and Anaman KA (2026) Economic value of agro-meteorological services: a review of the international literature. Front. Clim. 8:1741828. doi: 10.3389/fclim.2026.1741828
Edited by:
Anthony Lupo, University of Missouri, United StatesReviewed by:
Muhammad Shahid, Brunel University London, United KingdomMori W. Gouroubera, Alliance Bioversity International and CIAT, Senegal
Copyright © 2026 Manzvera and Anaman. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Joseph Manzvera, bWFuenZlcmFqb3NlcGhAZ21haWwuY29t