The Lewis model is one of the key theories explaining agricultural transformation in the context of dual economy for poor countries. According to Lewis, a poor/ developing country consists of two sectors including 1) a small capitalist sector and 2) a large traditional agricultural sector. Lewis argues that employers in the capitalist sector take up labour to make money while those in the traditional sector are not profit oriented and therefore hire too many people leading to low productivity ( Lewis, 1979 ).

Based on this, Lews argues that one way to catalyse development in poor countries is to move labour to manufacturing where it is more productive. He argues that capitalist save out of their profits and use these savings to expand, which leads to growth. Lewis assumed that workers in agriculture save nothing and that the only way to save was through the capitalists in manufacturing. Lewis used this model to explain the pattern of growth in poor countries outlining different growth stages based on a country’s income level. In poor countries, growth is slow because of a small or non-existent manufacturing sector. Middle income countries record higher growth because the manufacturing sector is pulling labour out of agriculture. At the high-income level, growth slows as the gains from diverting labour out of agriculture are almost fully realised ( Lewis, 1979 ).

Lewis further argued that poor countries engaged in trade would get little benefit from increasing their exports, as the benefits would go to consumers in richer countries. He recommended that poor countries should instead focus on food production rather than exports ( Lewis, 1979 ).

Mellor divided the agricultural development process into three phases including 1) traditional agriculture 2) technologically dynamic agriculture and 3) high capital agriculture. According to Mellor, the traditional phase is comprised of small family farms with low productivity. At this stage, farming is mainly subsistence oriented, labour intensive and farm centred. The transition to the second phase requires institutional and educational reforms to enable farmers adopt better and more efficient farming methods such as the use of improved seeds, fertilizer and irrigation. The third phase involves high capital agriculture, utilizing mechanisation and larger farm sizes, supported by a developed non-farm sector ( MELLOR, 1969 ).

Mellor’s model is key in that it emphasizes the critical role of institutional and educational reforms to transition from phase one to phase 2. Failure to achieve these reforms would result in a premature shift to phase three which could lead to structural issues as the country’s institutional capacity may not support specialised agriculture effectively ( MELLOR, 1969 ).

The first phase of agricultural transformation process is marked by improvements in productivity per unit of land and labour. These improvements are achieved through the adoption of improved seed varieties, mechanisation, better soil management practices and improved access to extension services. Agricultural output expands as productivity increases which leads to surplus production beyond subsistence needs ( Douglas, 2021 ).

During this phase, farmers transition from traditional, low-yield farming methods to more efficient and market responsive practices. However, it is crucial to note that sustained productivity growth requires investments in infrastructure such as rural roads, irrigation systems and post-harvest storage facility. Without such investments, productivity gains may be short-lived due to input inefficiencies, post-harvest losses and market failures.

During the second phase, countries utilise the surplus agricultural output in stage one which creates opportunities for reinvestment in the economy. The surplus can be utilized in several ways including 1) through increased household food security, improved nutrition and income which in turn can stimulate local demand of goods and services. 2) through taxation, government interventions or investments in public goods such as rural electrification and market development. 3) through value addition, agro processing and integration into supply chains ( Timmer, 1988 ).

The third phase involves deeper integration of agriculture into the national and global economy. This can only be achieved with operational agricultural markets, financial services and policy frameworks that support competitive agribusiness development.

Based on the above analysis, this study will measure agricultural transformation into four stages as depicted in Figure 1 :

In assessing countries over time, it is important to consider the context and dynamics surrounding it. The critical development question remains: how long should agriculture transformation last? Various studies have shown that many developing countries have experienced prolonged or incomplete agricultural transformation which continues to hamper their broader economic development.

According to the African Development Bank, crop yields remain three times lower than in Asia despite efforts to introduce improved seeds and fertilizers in Africa ( Adamon, Andinet, Adeleke, & Simpasa, 2017 ). Further, mechanisation remains low with over 60% of farming in sub-Saharan Africa still rainfed ( Dong, Chunlai, & Christopher, 2023 ).

Unlike in Asia, Africa’s labour migration from agriculture is not fuelling the growth of the manufacturing sector. Instead, many workers move into low productivity urban services and informal employment ( AFDB, 2017 ). As noted by ( Abedullah, Shujaat, & Farah, 2023 ) this results in “urbanisation without industrialisation”, where cities grow without corresponding increases in high value economic activity.

A study by ( Dong, Chunlai, & Christopher, 2023 ), notes that Africa’s demographic transition is slower because rural populations continue to grow, creating pressure on land and food systems. Furthermore ( Abedullah, Shujaat, & Farah, 2023 ) notes that many Africa countries still have over 50% of their population engaged in agriculture compared to 10 to 20% in industrialised Asian economies.

Several studies have shown that limited rural infrastructure such as roads and electricity prevent market integration and value addition ( World Bank, 2019 ). Unlike in Asia where agricultural Transformation created a dynamic agribusiness sector, Africa’s agribusiness sector remains underdeveloped ( AFDB, 2017 ).

There is increasing evidence that shows that Africa is more severely affected by climate risks such as droughts, floods and land degradation than in Asia and other regions ( Abedullah, Shujaat, & Farah, 2023 ). Other studies indicate that Africa has been slower in adopting climate smart agriculture which is necessary to sustain long term productivity growth.

Developed by the International Food and Policy Institute (IFPRI), the Agricultural Transformation Index (ATI) is one of the most widely used indices for measuring progress in agricultural transformation across countries ( International Food Policy and Research Institute (IFPRI), 2024 ).

The IFPRI ATI measures productivity growth by assessing agricultural output per worker, land productivity and total factor productivity. It further measures market integration by capturing commercialisation and the proportion of produce sold in markets. It tracks structural transition by measuring the declining share of agriculture in GDP and employment as well as movement of labour to non-farm sectors. In terms of policy and institutional support, the index considers investments in rural infrastructure, access to credit and policy effectiveness ( International Food Policy and Research Institute (IFPRI), 2024 ).

Limitations

In view of the unique challenges affected by the Africa continent, the IFPRI ATI lacks indicators to track climate resilience, land degradation and biodiversity loss. Furthermore, the index fails to capture the rural non-farm economy which is crucial in understanding transformation in the context of the African continent.

The IISD proposed a measure of agricultural Transformation which categorises transformation into six distinct phases ranging from subsistence farming to full industrialisation. Building on Timmer’s (1988) framework, the classification is based on 45 years of empirical data from 45 countries. One of the key findings from the model is that transformation is nonlinear and that countries progress at different speeds depending on policy priorities, investments and economic linkages ( Laborde, Lallemant, Kieran, Smaller, & Traore, 2019 ).

The framework categorised countries into six phases reflecting different levels of agricultural transformation as follows;

Indicators used to measure agricultural transformation include, agricultural productivity, labour transitions, market integration, public investments and infrastructure and policy and institutional reforms.

Limitations

While the IISD framework provides a structured classification of agricultural transformation, its methodology does not fully capture the unique challenges of African agriculture particularly climate vulnerability, informal market structures and demographic pressures.

A recent study by ( Meimei, Libang, & Haojian, 2020 ) utilised TFP to assess the agricultural transformation stages in Gansu Province in China. They employed the DEA-Malmquist index model to measure TFP for 87 countries from 1988 to 2017. The study identified three distinct stages of agricultural transformation including:

More recently, ( Mulubrhan, Priyanka, & Trung, 2023 ) conducted a comparative analysis between Southeast Asia (SEA) and Sub-Saharan Africa (SSA) to identify micro level indicators of agricultural transformation. The study examined how the changes in agricultural income influenced various factors including:

The study established that increases in agricultural income in SEA were associated with higher non-farm income and more investment in mechanisation. This indicated a complementary relationship between farm and non-farm sectors. In contract, SSA exhibited a substitute effect, where increased agricultural income led to reduced non-farm income suggesting differing pathways of transformation between the regions.

The index is computed by normalising the values of each dimension to a uniform scale in a given year. Each normalised score is then multiplied by a predetermined weight relative to its importance (25% for Agricultural Productivity and Efficiency, 20% for market integration and Value Addition, 15% for Structural Economic Shifts, 15% Rural Infrastructure and Financial Services, 15% for Climate Resilience and Sustainability and 10% Policy and Institutional Effectiveness) and the weighted scores are summed to produce the overall HIATI score.

A major limitation in developing the HIATI has been the lack of publicly available, agriculture-specific data across African countries. To address this challenge and ensure cross-country comparability, the index draws primarily from the World Bank Development Indicators and other internationally recognized sources. While this approach ensures consistency and replicability, it has necessitated the use of proxy indicators in some dimensions, particularly where more granular or sector-specific data (e.g., on technology adoption, extension reach, or climate-smart practices) are not readily available.

Despite these limitations, the HIATI presents a robust conceptual and analytical framework for assessing agricultural transformation in Africa. It provides valuable insights into the key drivers of transformation, including productivity, market integration, structural shifts, infrastructure, sustainability, and policy effectiveness. As more detailed and disaggregated data become available over time, the index can be further refined, enhancing its diagnostic power and relevance for decision-makers.

Agricultural productivity is a foundational driver of transformation. According to neoclassical growth theory ( Solow, 1956 ), increases in total factor productivity (TFP) raise output per unit of input, which is essential for economic expansion. The Lewis dual-sector model ( Lewis, 1954 ) also emphasized the release of surplus labor from agriculture into higher-productivity sectors as a mechanism for structural transformation.

Gollin, Hansen, & Wingender (2021) show that in low-income countries, agricultural productivity remains significantly lower than in other sectors, constraining national income growth. Bridging the agricultural productivity gap enables higher rural incomes, reduces poverty, and catalyzes labor mobility ( Gollin, Lagakos, & Waugh, 2014 ). However, the nature of technological change matters: labor-saving technologies may displace workers unless complemented by rural non-farm employment ( Bustos, Caprettini, & Ponticelli, 2016 ).

Direct indicators such as crop yields and livestock productivity, and proxy indicators such as technology adoption rates, help capture both system efficiency and innovation uptake.

Ideal Indicators

Direct and Proxy Indicators used

This dimension tracks the extent to which agriculture is integrated into domestic and international markets and contributes to value-adding processes such as agro-processing, packaging, and commercialization. Market integration and value addition are key features of agricultural transformation, enabling a shift from subsistence to a market-driven agricultural system that is productive, competitive, and profitable.

From the lens of transaction cost economics ( Williamson, 1985 ), effective integration into markets reduces information asymmetries and coordination failures, thereby incentivizing producers to specialize and invest. In a transforming system, farmers are not only producers but also participants in value chains that link them to input suppliers, processors, distributors, and final consumers.

Empirical studies reinforce this importance. Marwa et al. (2017) , in a study of rice markets in Indonesia, show that integrated markets lead to more stable prices and efficient resource allocation. Similarly, initiatives like the AfDB’s AMVAT project in South Sudan demonstrate how support to agro-processing and export development can strengthen food systems, boost employment, and enhance value retention in rural areas ( Marwa, Abdelraouf, & Abuarab, 2017 ).

Agricultural transformation also entails vertical and horizontal integration: farmers increasingly engage in contractual relationships, aggregation models, and structured markets. These arrangements improve market access, reduce post-harvest losses, and allow for product differentiation, steps that are essential for upgrading within regional and global value chains.

Ideal Indicators

Direct and Proxy Indicators used

A defining feature of agricultural transformation is its contribution to broader structural economic change, wherein labor and resources shift from low-productivity agriculture to higher-productivity sectors like manufacturing and services. However, transformation does not imply the abandonment of agriculture. Rather, it involves the modernization of agriculture, improved labor productivity, and efficient reallocation of labor and capital across the economy.

The theoretical basis for this transition is rooted in the Lewis dual-sector model ( Lewis, 1954 ), which posits that the movement of surplus labor from traditional agriculture to the modern sector underpins early industrial growth. Kuznets emphasized that such a shift is accompanied by urbanization, income growth, and changing consumption patterns ( Kuznets, 1957 ). Later, Johnston and Mellor (1961) argued that a productive agricultural sector provides essential capital and food to fuel urban development and economic diversification.

Empirically, countries such as Vietnam and Ethiopia have demonstrated how rising agricultural productivity and urban demand lead to diversification of both rural and urban economies, supporting off-farm employment, food system modernization, and reduced poverty ( Christiaensen & Martin, 2018 ). Yet, if labor exits agriculture without accompanying productivity gains, the result may be “distress-driven” migration, persistent underemployment, and urban informality, a challenge documented across parts of sub-Saharan Africa ( McMillan, Rodrik, & Sepúlveda, 2017 ).

As such, this dimension of the ATI captures the scale and direction of labor and demographic shifts, providing insight into whether a country’s transformation path is sustainable, inclusive, and productivity led.

Ideal Indicators

Direct and Proxy Indicators used

The availability and quality of rural infrastructure and financial services are essential enablers of agricultural transformation. These services reduce transaction costs, improve productivity, and enable farmers to access markets, technologies, and capital. Without investments in rural infrastructure, such as roads, irrigation, and electricity and inclusive financial systems, agriculture remains trapped in subsistence and low-value production.

According to endogenous growth theory ( Romer, 1990 ), public goods such as infrastructure increase the returns on private investment and contribute to long-term economic growth. In agriculture, these investments are especially crucial for enabling scale, commercial viability, and resilience. Transaction cost theory ( Williamson, 1985 ) also highlights how the lack of physical and financial infrastructure increases barriers to market entry and reduces the efficiency of input-output systems.

Empirical studies show that feeder roads and irrigation are strongly correlated with increased farm productivity and income. For instance, ( Dercon, Gilligan, Hoddinott, & Woldehanna, 2009 ) found that rural road development in Ethiopia significantly improved consumption growth and poverty reduction. Access to electricity enables agro-processing and cold storage, reducing post-harvest losses and supporting value chains. On the financial side, studies by IFPRI (2016) highlight the transformative role of agricultural credit and insurance in enhancing technology adoption, risk management, and commercialization.

Thus, this dimension evaluates the extent to which enabling infrastructure and financial systems are in place to support farmers’ transition from subsistence to a commercially viable and modern agriculture.

Ideal Indicators

Direct and Proxy Indicators used

This dimension addresses the extent to which agricultural systems are equipped to manage environmental risks and contribute to long-term ecological sustainability. As agricultural transformation progresses, systems must not only become more productive and market-oriented but also resilient to climate variability and environmentally sustainable. Failure to embed climate resilience and resource conservation can reverse gains and expose livelihoods to shocks.

The relevance of this dimension is underscored by environmental production theory, which extends the neoclassical production function to include environmental assets as both inputs and outputs ( Barrett, Ortiz-Bobea, & Pham, 2021 ). Moreover, the sustainable livelihoods framework ( DFID, 1999 ) highlights environmental stewardship as a key form of capital, alongside human, social, and economic resources.

Climate change disproportionately affects smallholder-dominated systems through erratic rainfall, droughts, and temperature extremes, particularly in rainfed regions of sub-Saharan Africa and South Asia. Ortiz-Bobea et al. (2021) found that climate change has already reduced global agricultural total factor productivity (TFP) by up to 20% since 1961. Simultaneously, agriculture contributes significantly to climate change through emissions, land degradation, and water use, necessitating a dual focus on adaptation and mitigation ( Ortiz-Bobea, Ault, Carrillo, Chambers, & Lobell, 2021 ).

Sustainable transformation requires widespread adoption of climate-smart practices (e.g., conservation agriculture, drought-resistant varieties, rotational grazing), supported by policies and investments that encourage low-emission development pathways.

Ideal Indicators

Direct and Proxy indicators used

Government commitment and institutional quality are among the most decisive factors in determining the success or failure of agricultural transformation. Policies set the strategic direction, while institutions implement reforms, regulate markets, and coordinate investments. This dimension evaluates the strength, coherence, and effectiveness of agricultural policy frameworks and institutional systems, which are essential for fostering a stable, enabling environment for transformation.

Empirical evidence shows that policy consistency, decentralization, and inclusive governance significantly influence transformation outcomes. For instance, the experience of Bangladesh demonstrates how long-term agricultural strategies, extension reforms, and public-private coordination enabled sustained productivity and commercialization gains. Conversely, fragmented policies and weak enforcement have been key constraints in countries where transformation has stalled ( Moin & Salam, 2021 ).

Strong institutional support also matters for cross-sectoral coordination (e.g., between ministries of agriculture, finance, environment, and trade), local implementation, and monitoring. In the face of growing complexity, from climate change to nutrition and youth employment, agriculture requires agile, adaptive institutions that are politically and technically empowered.

Ideal Indicators

Direct and Proxy Indicators used

To ensure comparability, the ATI will be computed using publicly available datasets from the World Bank Development Indicators.

Given that countries report agricultural data in different units and scales, indicators must be normalised. The min-max scaling will be used to ensure comparability:

$X^{\prime }=\frac{X-X_{\min }}{X_{\max }-X_{\min }}\times 100$

Where:

*This transformation scales all indicators to a 0 – 100 range ensuring consistent aggregation across different metrics.

The HIATI dimensions will be weighted based on their importance in driving agricultural transformation as follows:

In line with economic theory and other studies, the weights reflect prioritisation of productivity and market factors but also recognise the role of sustainability and policy support ( Paula, Bruno, & Jacopo, 2016 ). The weighting scheme reflects Conesus in development economics that improvements in productivity and market linkages are foundational to agricultural transformation aligning with the structural transformation theory. The theory emphasizes the gradual shift from subsistence to commercial agriculture as the economy grows and diversifies.

To assess the robustness of the HIATI and validate its insights, a comparative review was conducted with other well-established indices and conceptual frameworks on agricultural transformation. This includes IFPRI’s Agricultural Transformation Index (ATI) developed by Diao et al. (2024) , Timmer’s foundational work on agricultural transformation ( Timmer, 1988 ), and the IISD’s sustainability-based indicators ( Čičkušić, Domuz, Topalović, & Bećirović, 2012 ).

The IFPRI ATI provides a compelling point of comparison due to its similar structure and focus on composite measurement. Built around four core indicators staple crop productivity, diversification, labor productivity, and food system expansion IFPRI’s ATI is methodologically aligned with HIATI in tracking system-wide change. However, HIATI introduces two additional dimensions (infrastructure and financial inclusion, and policy/institutional effectiveness), offering a more comprehensive lens tailored to the African context. While IFPRI’s index draws strongly on macroeconomic and welfare correlations, HIATI places greater emphasis on integrating climate resilience, governance, and institutional effectiveness, which are particularly critical for Africa’s agricultural transformation.

Timmer’s (1988) framework remains a gold standard in understanding the stages of agricultural transformation. His emphasis on “getting agriculture moving,” followed by integration into the macroeconomy, is reflected in HIATI’s structure particularly in dimensions such as productivity, structural change, and market integration. Where HIATI advances this narrative is by operationalizing these theoretical constructs into measurable indicators that allow for comparative analysis across African countries, grounded in recent data and reflecting present-day development priorities such as sustainability and policy alignment.

The International Institute for Sustainable Development (IISD) approach emphasizes systems thinking and sustainability, focusing on interlinkages between agriculture, environment, and social well-being. While IISD’s framework is broader and not agriculture-specific, it reinforces the importance of including climate and institutional dimensions, a principle that HIATI adopts explicitly. HIATI’s inclusion of environmental indicators such as methane emissions and forest coverage echoes IISD’s emphasis on the environmental footprint of development processes.

The HIATI score for each country will be computed as:

$\text{HIATI}=\sum \left(W_i\times {X^{\prime }}_i\right)$

Where:

$W_i$= Weight assigned to dimension I;

${X^{\prime }}_i$= Normalised score of dimension i.

8.5 Interpretation of HIATI Scores

The HIATI scores were generated at three time periods (2000, 2010, 2020) in order assess the trends over time. The study reveals some notable changes in the agricultural development stages of African countries. As shown in Figure 2 , 21 countries were classified as being at an “early stage” of transformation in 2000. By 2020, this number had decreased to only 7 including South Sudan, Congo Dem. Rep. Somalia, Djibouti, Lesotho, Libya and Burkina Faso. Meanwhile, the number of countries identified as “emerging” increased from 30 in 2000 to 46 in 2020 ( Figure 2 and Figure 3 ) showing a gradual shift from subsistence based agricultural systems to more structured and market driven economies.

Among the 16 Countries that transitioned from early stage to emerging, Mali, Ethiopia, Guinea and Kenya were among the countries that recorded the highest HIATI scores. During the 20-year period, only one country was categorized as transitioning and none as advanced.

The findings are similar to the findings of the other indices and frameworks including the IFPRI Agricultural Transformation Index and Timmer’s theoretical stages of transformation. For instance, countries such as Ethiopia, Ghana,

Rwanda, and Malawi appear across all three frameworks as experiencing significant progress in agricultural transformation. For instance, in HIATI, Ethiopia’s score rose from 31 (early stage) in 2000 to 47 (emerging) in 2020, signaling strong gains in productivity and market integration. This aligns with IFPRI ATI findings, where Ethiopia recorded one of the highest score increases among Feed the Future countries.

Similarly, Ghana and Rwanda are shown to have sustained improvements in both indices. Ghana maintained an emerging transformation status in HIATI with a consistent score rise from 37 to 43 between 2000 and 2020. Rwanda also showed upward momentum, rising from 32 to 40 during the same period ( Figure 4 ). The IFPRI ATI supports this trend, noting Rwanda’s gains exceeding 0.30 points over two decades driven primarily by improvements in food system expansion and labor productivity.

The HIATI scores for all countries are indicated in Table 1 . The data shows a general trend of improvement over the 20-year period as follows:

While the data points to a positive outlook, it is important to further interrogate the factors driving these changes. In particular, the study analyses the transformation drivers for countries that progressed from the early stage to the emerging category. The study also assesses if the countries within the emerging category have experienced regression and the dimensions of the index that account for the reduced growth. Finally, the study examines the countries that have experienced slow growth over the 20 years period.

Countries moving from Early Stage to Emerging (2000-2020)

A total of 16 Countries transitioned from “early stage” to “emerging” during the period 2000 and 2020. In this category, 13 countries recorded an increase in the HIATI score by more than 5 points with an average increase of 9.42.

As shown in Figure 5 , Mali and Ethiopia recorded the highest increase in their HIATI scores with 18.3 and 16.1 points respectively. Three countries recorded an HIATI growth of less than 5 points with an average increase of 2.46 as illustrated in Figure 6 .

Angola and Congo Rep. recorded the least improvements in their HIATI scores by 2.8 and 0.9 points respectively.

To assess the drivers of transformation for countries that moved from early stage to emerging, countries were assessed against six dimensions of transformation ranging from agricultural productivity to structural economic shifts. Figure 7 depicts which dimensions accounted for transformation for countries that moved from early stage to emerging category.

The analysis shows that “Agricultural Productivity and Efficiency” and “Rural Infrastructure and Financial Services” are the two top dimensions contributing to agricultural transformation accounting for 31.5 points and 18.1 points respectively. This shows that advancements in agricultural productivity through enhancements in crops yields, improvements in farming techniques and adoption of

new technologies plays an important role in driving agricultural transformation. Furthermore, improved rural infrastructure such better road network, irrigation systems and better access to financial services have facilitated access to markets and easier access to capital for farmers.

Despite these improvements, the findings also indicate that agricultural progress was not uniform. For instance, some countries within the emerging category stagnated and recorded reduced HIATI scores.

As shown in Figure 8 , Equatorial Guinea, Gambia, Liberia, Namibia, Sao Tome, Sudan and Zimbabwe experienced a decline in their HAITI scores within the emerging category.

Sao Tome recorded the most decline (-14.30 points), followed by Zimbabwe (-6.16 points) and Equatorial Guinea (-5.01 points). These findings are consistent with IPFRIs index. Both HIATI and IFPRI highlight countries that have stagnated or regressed, such as Liberia and Mali. HIATI places them among the group whose transformation scores plateaued, while IFPRI attributes this to declining crop diversification and staples productivity, particularly in Mali, Liberia, and Uganda. These shared insights underscore the fragility of transformation when diversification and environmental resilience are not sustained​.

Moreover, Timmer’s framework suggests that countries early in their development should exhibit gains through “getting agriculture moving” typically through input use and basic infrastructure. This maps well onto HIATI results where countries like Kenya, Guinea, and Mali recorded some of the highest score jumps, moving from early-stage to emerging transformation largely due to improvements in productivity and institutional support, echoing Timmer’s early transformation phase

In order to establish the dimensions that influence the HIATI scores, a correlation heatmap was used. The results indicate varying degrees of correlation between different dimensions of the HIATI scores:

As demonstrated in Figure 9 , these findings underscore the complexity of factors influencing agricultural transformation in Africa. The slow structural transformation of the economy shows the low efficiency of the primary sectors in catalysing the growth of secondary economic sectors including labour movements from agriculture to other non-agricultural sectors. Additionally, climate related challenges such as extreme weather events and water scarcity have exacerbated vulnerabilities leading to reduced agricultural productivity in some regions. This indicates that while agriculture transformation is progressing in certain parts of Africa, it remains fragile in the absence of climate adaptation measures.

Rural Infrastructure and Financial Services showed a weak positive correlation of (0.17), which suggests a slight positive impact on agricultural transformation. Agricultural Productivity and Efficiency showed no correlation implying that changes in this dimension did not significantly affect the HIATI scores.

While Zambia has recorded some progress in the agriculture sector since independence, the agriculture sector has not transformed to the levels required to catalyse structural change. The country’s agriculture sector is heavily dependent on rain with limited agricultural mechanisation, low efficiency, and inadequate market integration. These challenges slow the rate of agricultural transformation and restrict the sector’s potential to drive economic growth.

The study findings show that Zambia’s HIATI scores have increased from 28 in 2000 to 34 in 2020 reflecting a gradual improvement as depicted in Figure 10 below.

As shown in Figure 11 , structural economic shifts and Policy and Institutional Effectives were the main drivers behind this improvement. This was followed by Market integration and value addition contributing about 16.77. These results align with insights from IAPRI (2020) and other studies, which have long pointed to Zambia’s strong macro-policy frameworks, such as the Second National Agricultural Policy (NAP II) and recent reforms under the Comprehensive Agricultural Transformation Support Programme (CATSP) ( Mason, Jayne, Chapoto, & Weber, 2009 ). These policy shifts emphasize public-private partnerships, enabling environments for irrigation, and development of agro-industrial corridors ( Chapoto, Mulenga, Kabisa, & Muyobela, 2020 ).

Despite these improvements, the country recorded low scores on some other critical dimensions of transformation such Agricultural Productivity and Efficiency (12.32) and Rural Infrastructure and Financial Services (15.86).

These findings are consistent with conclusions from Food Security Research Project ( FSRP, 2011 ) and the AFRICAP participatory scenario planning report ( GCRF-AFRICAP, 2019 ). Both sources highlight low mechanisation, rain-fed dependency, limited irrigation (only 156,000 ha irrigated out of 2.75 million ha potential), and low maize yields (~2 t/ha vs. a 3 t/ha target). This is also echoed in Zulu et al. (2000) who note stagnation in smallholder maize production and weak market orientation, which corroborates the HIATI findings of poor performance in productivity and infrastructure dimensions ( Zulu, Ayne, & Beaver, 2000 ).

These results highlight the need for immediate action to improve agriculture productivity and rural infrastructure and financial services. As shown in Figure 12 , Zambia lags behind the regional average on a number of indictors including Market Integration, Rural Infrastructure and Climate Resilience.

Market integration, while improving slightly in HIATI (contributing 16.77% to Zambia’s score), is another area of partial alignment. Studies have shown that while Zambia has expanded export markets (e.g., soybean, cotton, horticulture), marketing inefficiencies and inadequate infrastructure continue to constrain full integration. For example, ( Tschirley & Jayne, 2010 ) note that better-performing smallholders tend to dominate markets, but the majority remain disengaged due to lack of infrastructure and support services ( Tembo, 2010 ).

The HIATI has provided good insights into the status and trends of agricultural transformation in Africa during the period 2000 to 2020. The findings show a significant shift from subsistence based agricultural systems to more structured and market driven economies signalling a continent-wide progression towards improved agricultural transformation and economic integration. The analysis shows that “Agricultural Productivity and Efficiency” and “Rural Infrastructure and Financial Services” are the two top dimensions contributing to agricultural transformation.

Despite these improvements, the findings also indicate that agricultural progress was not uniform. For instance, some countries within the emerging category stagnated and recorded reduced HIATI scores. The reduced performance is due to the low scores for 2 dimensions including (i) Climate Resilience and (ii) Structural Economic Shifts. This indicates that while agriculture transformation is progressing in certain parts of Africa, it remains fragile in the absence of climate adaptation measures.

For Zambia, the index indicates a gradual but positive trend in agricultural transformation with high scores in policy and institutional effectiveness and structural economic shifts. Despite the gains, the country scores low on critical drivers of transformation including agricultural productivity and rural infrastructure.

Enhance implementation capacity of agricultural policies and programmes: While Zambia performs well on policy and institutional frameworks (as reflected in the HIATI score), implementation remains uneven. Strengthening institutional capacity at both national and subnational levels—including better coordination among ministries and increased agricultural budget execution—will be essential. Monitoring mechanisms should be institutionalized to track performance of flagship programmes like FISP and CATSP, and ensure alignment with farmer needs and emerging development priorities.