Wheat Production Across East Africa: Trend, Instability, and Decomposition Analysis Using Time Series Approach
ABSTRACT
Measuring agricultural growth and variability is key to tracking output changes. East African wheat production is below its potential, with limited data and analysis over time. As a result, this study examines the growth patterns, variability, and instability of wheat production in East Africa, specifically in Ethiopia, Kenya, Uganda, Rwanda, and Tanzania, between 1993 and 2023. To analyse and estimate wheat production trends, instability with regional disparity, and decomposition across East Africa's top wheat-producing countries, a 30-year data series with different secondary data, mostly the FAOSTAT database, was divided into three sub-periods: Period I (1993/94-2002/03), Period II (2003/04-2012/13) and Period III (2013/14-2022/23), even though compound growth rates, a semi-logarithmic trend model, a differential equation approach for decomposition analysis, and the Cuddy-Della Valle Index were utilised. Wheat production and productivity in Eastern Africa exhibited a general upward trend, primarily attributed to land expansion rather than breakthroughs in yield. Ethiopia became the leading producer, whereas Uganda has shown consistent and significant growth. Conversely, Kenya and Tanzania experienced decreasing trends in productivity within cultivated areas. Instability analysis indicates that Uganda exhibited the highest stability in production at 7.32%, whereas Rwanda and Tanzania taught greater volatility, with rates of 46.74% and 32.15%, respectively. The decomposition analysis reveals that the increase in farming areas contributed to 73.1% of the recent production growth. East African countries must prioritise productivity-enhancing wheat production by implementing modern farming technologies, employing improved varieties, increasing irrigation, and encouraging climate-resilient practices to ensure sustainability and improve regional food security, regional trade connectivity and for further encroachment of East Africa community.
1 Introduction and Background
Wheat is significant among global crops due to its historical significance and role as a staple food for humanity (Birch et al. 2012). Wheat comes from Central and Near East Asia at the time of 9600 BC. It was a prised cereal in prehistoric Europe, Persia, Greece and Africa, such as Egypt and Ethiopia (Heiser 1990). Indian farmers cultivated heterogeneous landraces and mixtures, called ‘sorts,’ until Sir Albert Howard and Lady G.L.C. Howard initiated systematic wheat improvement efforts Lady G.L.C. Howard in the early 1900s (Singh et al. 2024). The Wheat Improvement Program has undergone several phases, with the Green Revolution showing tremendous wheat output growth (Pinstrup-Andersen and Hazell 1985).
In 2022, global wheat production reached 808 million tons, with Africa contributing 27.31 million tons, representing just 3.37%. Within Africa, East Africa produced 7.87 million tons, accounting for 28.8% of the continent's wheat output. Much of this came from Ethiopia, Tanzania, Uganda, Rwanda, and Kenya, collectively producing 7.38 million tons, 93.88% of East Africa's total wheat production. Among these, Ethiopia was the dominant producer, contributing 7 million tons, or 88.9% of the region's wheat output (FAO 2025).
Despite this production, Africa spends approximately $85 billion annually on food imports, with 15% explicitly allocated for wheat imports (FAO 2021). Northern Africa accounts for the most significant of wheat import costs at 59%, followed by Western Africa at 19% and Eastern Africa at 14% (FAO 2021). Over the past two decades, Africa's wheat import expenses have grown at an annual rate of 9%, driven by factors such as population growth, urbanisation, and a decline in the consumption of coarse grains (Noort et al. 2022; Reardon et al. 2021; Tadesse et al. 2018).
Consequently, wheat production, trends, and imports have become essential to address the increasing disparity between wheat consumption and production on the continent and the region (Bentley et al. 2022). Reliance on imported wheat presents a significant challenge due to recent anthropogenic and natural crises that disrupt global production and trade systems (Gutiérrez-Moya et al. 2021). Overreliance on wheat imports could threaten food and national security, so short- and medium-term policies are needed (Bentley et al. 2022).
A recent report from UNCTAD (UNCTAD 2023) indicates that numerous countries in sub-Saharan Africa and Eastern Africa rely on food imports. These countries face two significant issues: a substantial increase in food import prices and a depreciation of their currencies relative to the USD or purchasing power parity of imported wheat. This situation places millions of individuals, primarily those in poverty, at an elevated risk of hunger. Enhancing domestic wheat production via improved practices and effective technology transfer is essential for maintaining affordable wheat prices for consumers (Grote et al. 2021).
The experience associated with the 2008 wheat price spike indicates that many African countries, particularly sub-Saharan Africa and the eastern part of Africa, reacted to the heightened prices by increasing wheat production in the subsequent year because they produce only about 30% of their wheat needs, leading to a heavy reliance on imports (Negassa et al. 2013). For instance, compared to 2008 and 2022, the effect of post-Covid-19 and the conflict between Russia and Ukraine, the two biggest wheat exporters, raised input prices and food security worldwide, and simultaneously in Eastern Africa, the dominant wheat producers such as Ethiopia, Kenya, Rwanda Uganda, and Tanzania concise short term strategies as expanded the area under wheat production, which resulted into a tangible increase in wheat production (Behnassi and El Haiba 2022). Simultaneously, 2022, the wheat prices in the international market increased by 89% (UNCTAD 2023). Additionally, the average exchange rate between the USD and respective national currencies in food import-dependent countries increased by 10%–46%, and the actual food import prices increased between 106% and 176% (UNCTAD 2023).
Wheat production in East Africa exhibits notable trends and disparities attributed to diverse socio-economic policies, climatic conditions, market access among producers, and technological adoption, especially in Ethiopia, the second-largest producer in Africa, encounters difficulties in attaining self-sufficiency, attributed to an increasing disparity between production and consumption that overview examines trends in wheat production, disparities in technology adoption, and the decomposition analysis of output changes (Nigus et al. 2022; Semahegn et al. 2021; Gichangi et al. 2022). Then, to address the increasing gap between wheat consumption and production, the country has had to bring in significant amounts of wheat for almost 50 years, utilising commercial imports and food aid (Sununtnasuk 2013; Van Ittersum et al. 2016).
Currently, the utilisation of wheat is increasing at a rate surpassing that of other food crops, driven by rapid population growth, rising incomes, urbanisation, climate change, and evolving consumption preferences for wheat-based food products (Hodson et al. 2020). The insufficiency of foreign exchange reserves for financing food imports is a significant issue, and the ongoing disruptions in natural and artificial production and trade have substantial implications for a country that is among the most vulnerable to food insecurity by 2050, particularly regarding its reliance on wheat imports (Silva et al. 2023; Guo et al. 2024).
Achieving food self-sufficiency and minimising the expenditure of limited foreign currency reserves on wheat imports is a national priority. For example, Ethiopia's wheat production is on the rise at a rate of 7.8% annually; however, demand exceeds supply, and growth is insufficient to meet the 9% annual increase, leading to a need for imports leading to a reliance on imports for 30% of consumption (Senbeta and Worku 2023), in Kenya, the demand for food is increasing due to population growth and evolving dietary preferences. Yet, production growth is only 5% annually, in contrast to an 8% rise in consumption (Gichangi et al. 2022; Anne et al. 2024), even though in Tanzania, despite having 44 million hectares of arable land, only 24% is being used. Wheat production has fluctuated, with a declining yield per hectare (Mkonda and He 2016).
This article aims to examine trends, instability disparities, and decomposition analysis in wheat production across the top five wheat-producing countries in East Africa that government initiatives for achieving wheat self-sufficiency due to its rising demand, significant contribution to food imports, and heightened government attention towards its production to initiatives seek to achieve self-sufficiency in wheat production through land expansion, irrigation and the closure of yield gaps.
2 Methodology
This study examines the trends in wheat production regarding area, production, and yield. It also assesses the relative contributions of cultivated areas and yields to the overall production. Additionally, the study analyses the direction of the trend, decomposition, instability disparity, and wheat self-sufficiency (2013–2022) across East Africa at country level. The study employed the following methodological approach to achieve the desired objectives.
2.1 Source of Data
The data were collected on the area, production, and yield of top wheat grown in Eastern Africa, namely the countries of Ethiopia, Uganda, Kenya, Rwanda and Tanzania, a region about the period from 1993 to 2023 (30 years), from the FAOSTAT database series, and to analyse growth and instability, the study divided the entire study period into three 10-year long sub-periods, as follows: Period I: 1993–94 to 2002–03; Period II: 2003–04 2012–13, Period III: 2013–14, 2022–23, and Overall: 1993–94 to 2022–23, and this approach is based on the works of (Senbeta and Worku 2023; Gujarati 2002; Gujarati and Porter 2009; Wainwright 2005).
2.2 Statistical Analysis
The study successfully realised its stated objectives through a systematic data processing and analysis approach. The secondary data were carefully sorted, edited, coded, organised, and summarised before being analysed. Descriptive statistics were employed to measure variability using standard deviation, the coefficient of variation, and instability indices. Parametric tests were conducted to compute the Composite Growth Rate, while econometric models were applied to decompose the production growth rate. Data analysis and estimation of parameters for SPSS Version 27, STATA Version 17, and Microsoft Excel provided reliability and preciseness.
2.2.1 Computation of the Composite Growth Rate
Yt = the quantity of wheat produced/hectarage/yield in year t; Y0 = the quantity produced/hectarage/yield in the base year; r = the compound growth rate of Y; t = time in chronological years.
Substituting lnY0 with β1 and ln(1 + r) with β2,
When the b2 value is positive and statistically significant, growth accelerates, and when it is negative and statistically significant, growth slows down. If b2 is not statistically significant, the growing process becomes stagnant.
2.2.2 Analysis of Instability
In this formula, R denotes the coefficient of determination from time trend regression and is used with the number of degrees of freedom to evaluate the statistical significance of R. The CVDI value varies between 1 and the CV value of the time series. With a considerable R2 value, the CVDI value tends to be smaller, meaning that the time series fits closely with the temporal trend, and variation is slight, that is, the series is stable. With a small R2 value, the value of CVDI is close to the CV value, suggesting that the instability of the series is not influenced much by the temporal trend R.
2.2.3 Decomposition of the Growth Rate of Production
3 Result
3.1 Trends of Harvested Area, Production and Productivity
As revealed in Figure 1, the area production and yield of wheat in East Africa have increased from 1993 to 2022, showing a steep growing trend, while wheat productivity and area show a decreasing grain trend. There has not been much of an increase in the area under wheat. Still, there has been a significant increase in production, which reflects East Africa's self-sufficiency in wheat production and the potential to export wheat to other countries. Trend analysis of wheat production in East African countries showed that the productivity peaked at 29.42 t/ha in 2022, while the lowest recorded productivity was 14.91 t/ha in 1993 (Figure 1).
3.1.1 Production, Area and Yield of Wheat in East Africa
Table 1 shows that wheat production across Ethiopia, Kenya, Rwanda, Tanzania, and Uganda shows notable variations in production, cultivated area, and yield from 1993 to 2023. Ethiopia's output went from 11.60 Mt in Period I to 51.12 Mt in Period III, which shows a remarkable increase, driven by both more land being farmed (0.92–1.79 Mha) and much higher yields (12.61–28.56 t/ha), which were caused by improvements in technology and farming methods. In contrast, Kenya's production sharply declined from 26.64 to 2.94 Mt despite a stable cultivated area and fluctuating yield, likely due to climate variability, policy shifts, or reduced investment in wheat farming, for the other countries, Rwanda, Tanzania and Uganda display relatively stable but low wheat production levels over the three periods. Rwanda's production remains minimal, with slight fluctuations, while Tanzania's output is nearly constant despite slight improvements in yield. Uganda also maintains a low and stable production, with a gradual decline in yield from 17.80 t/ha in Period I to 16.00 t/ha in Period III, which suggests challenges in sustaining productivity. Meanwhile, Rwanda's modest yield growth suggests slight efficiency improvements.
| Country | Average for Period I (years 1993–2002) | Average for Period II (years 2003– 2012) | Average for Period III (years 2013–2023) |
|---|---|---|---|
| Production (Mt.) | |||
| Ethiopia | 11.60 | 24.68 | 51.12 |
| Kenya | 2.64 | 3.56 | 2.94 |
| Rwanda | 0.08 | 0.17 | 0.11 |
| Tanzania | 0.80 | 0.85 | 0.84 |
| Uganda | 0.12 | 0.17 | 0.16 |
| Area (Mha) | |||
| Ethiopia | 0.92 | 1.43 | 1.79 |
| Kenya | 0.14 | 0.14 | 0.13 |
| Rwanda | 0.01 | 0.02 | 0.01 |
| Tanzania | 0.06 | 0.07 | 0.07 |
| Uganda | 0.01 | 0.01 | 0.01 |
| Yield (t/ha) | |||
| Ethiopia | 12.61 | 17.26 | 28.56 |
| Kenya | 18.86 | 25.43 | 22.62 |
| Rwanda | 8.00 | 8.50 | 11.00 |
| Tanzania | 13.33 | 12.14 | 12.00 |
| Uganda | 17.80 | 17.00 | 16.00 |
- Source: Authors' own computation based on data of (FAO 2025).
3.2 Wheat Self-Sufficiency (2013–2022) Across East Africa
A country is considered self-sufficient in wheat when its domestic production equals or exceeds its total consumption. On the other hand, countries with low levels of self-sufficiency need to import wheat to meet their populations' food and industrial demands.
Table 2 show that the level of wheat self-sufficiency in Ethiopia, Kenya, Rwanda, Uganda and Tanzania as follows,
| Year | Item | Ethiopia | Kenya | Rwanda | Uganda | Tanzania |
|---|---|---|---|---|---|---|
| 2013 | Production | 3.93 | 0.45 | 0.01 | 0.02 | 0.10 |
| Consumption | 4.51 | 1.36 | 0.07 | 0.37 | 0.78 | |
| % Import | 18.45 | 78.36 | 129.07 | 118.84 | 100.74 | |
| 2014 | Production | 4.23 | 0.23 | 0.01 | 0.02 | 0.17 |
| Consumption | 4.78 | 1.73 | 0.12 | 0.47 | 0.89 | |
| % Import | 19.97 | 87.33 | 114.29 | 110.67 | 113.24 | |
| 2015 | Production | 4.65 | 0.24 | 0.01 | 0.02 | 0.07 |
| Consumption | 4.76 | 1.67 | 0.09 | 0.44 | 0.89 | |
| % Import | 22.84 | 91.26 | 147.50 | 107.45 | 97.60 | |
| 2016 | Production | 4.54 | 0.21 | 0.01 | 0.02 | 0.08 |
| Consumption | 5.14 | 1.74 | 0.11 | 0.52 | 0.99 | |
| % Import | 42.33 | 92.93 | 116.67 | 105.01 | 85.00 | |
| 2017 | Production | 4.64 | 0.17 | 0.01 | 0.02 | 0.05 |
| Consumption | 4.50 | 1.95 | 0.11 | 0.61 | 0.86 | |
| % Import | 20.36 | 99.53 | 182.68 | 107.94 | 75.11 | |
| 2018 | Production | 4.84 | 0.34 | 0.01 | 0.02 | 0.06 |
| Consumption | 4.59 | 2.00 | 0.12 | 0.60 | 0.90 | |
| % Import | 21.73 | 93.23 | 169.29 | 105.17 | 84.71 | |
| 2019 | Production | 5.32 | 0.37 | 0.02 | 0.02 | 0.06 |
| Consumption | 5.14 | 2.05 | 0.12 | 0.61 | 0.98 | |
| % Import | 22.12 | 94.94 | 154.68 | 113.00 | 92.65 | |
| 2020 | Production | 5.48 | 0.40 | 0.01 | 0.03 | 0.08 |
| Consumption | 4.54 | 2.10 | 0.12 | 0.59 | 0.83 | |
| % Import | 18.09 | 90.31 | 99.11 | 106.51 | 85.11 | |
| 2021 | Production | 5.81 | 0.25 | 0.01 | 0.03 | 0.07 |
| Consumption | 5.10 | 2.05 | 0.11 | 0.58 | 0.74 | |
| % Import | 28.59 | 85.77 | 187.80 | 116.88 | 97.25 | |
| 2022 | Production | 7.00 | 0.27 | 0.01 | 0.03 | 0.08 |
| Consumption | 5.23 | 2.12 | 0.13 | 0.58 | 0.88 | |
| % Import | 20.19 | 77.08 | 172.03 | 111.09 | 96.57 |
- Source: Authors' own computation based on data of (FAO 2025) (Production and Consumption with million ton).
Ethiopia consistently demonstrates relatively high self-sufficiency in wheat production compared to its regional counterparts. Over the 5 years, Ethiopia's wheat production has generally exceeded its domestic consumption in 2022; production reached seven million metric tons, while consumption stood at 5.23 million. The nation's dependency on wheat imports ranged from 18.09% to 28.59%, implying a modest dependence on imports but a clear trend toward growing self-sufficiency. Notably, in 2020 and 2022, Kenya's wheat production was just 0.27 million metric tons compared to a consumption level of 2.12 million metric tons. Despite this disparity, Kenya's import reliance has shown a promising trend, diminishing from 93.23% in 2018 to 77.08% in 2022. This improvement suggests a potential change in Kenya's wheat production and imports dynamics. Rwanda showed a considerable disparity between production and consumption in 2022: 0.01 million metric tons of wheat were produced while consumption relocated at 0.13 million. With an exceptionally high import reliance on grain, the nation's level peaks in 2021 at 187.80% and rises above 150% in most years. Such numbers imply Rwanda's almost complete dependence on imports, maybe bolstered by stock adjustments or re-exports.
Furthermore, Uganda's wheat output is small compared to its demand of 0.58 million metric tons of consumption. Uganda generated just 0.03 million metric tons of wheat in 2022. Though somewhat less than Rwanda's, the import dependency swung between 105% and 117% during the period, suggesting a strong and consistent reliance on imports.
Though it still falls short of meeting its wheat demand through local production, Tanzania fares better than Rwanda and Uganda. Production in 2022 was 0.08 million tons; consumption came to 0.88 million metric tons. The nation constantly relied heavily on foreign wheat supplies, with import dependency ranging from 85% to 97%. However, with the right strategies and support, there is a potential for growth in Tanzania's wheat production, which could reduce its import dependency.
Therefore, Ethiopia is the most self-sufficient country in wheat production among Kenya, Rwanda, Uganda and Tanzania, as it consistently produces more wheat than it consumes in several years. Rwanda and Uganda are the most import-dependent, with import ratios exceeding 100% in all observed years. Kenya and Tanzania demonstrate moderate but persistent dependency on imports, with some slight improvements in Kenya's case.
3.3 Growth Rate for Wheat Across East Africa
The growth rate of the harvested area, production, and productivity during each period was statistically significant at the 1% level, and the area harvested during period III (2013/14-2022/23) was at 5%; in contrast, the yield of wheat production across East Africa during period I (1993/4-2002/3) was not statistically significant (Table 3). This shows that the farmed area has been steadily decreasing, primarily because of changes in how crops are grown. The CAGR of production and yield was (6.7%) and (3.2%) per annum during Period II, whereas the CAGR of both production and yield declined to (1.7%) and (2.5%) during Period III; therefore, the result reveals that the growth rate over the period under study was positive but declined gradually (Table 3).
| Period | Area | Production | Yield |
|---|---|---|---|
| I. 1993–94 to 2002–03 | 4.60* | 4.50* | (2.00) |
| II. 2003–04 to 2012–13 | 3.40* | 6.70* | 3.20* |
| III. 2013–14 to 2022–23 | 1.70** | 4.30* | 2.50* |
| All. 1993–94 to 2022–23 | 2.90* | 6.20* | 3.30* |
- Source: Author's computation; based on data of (FAO 2025).
- * means significant at 1 percent.
- ** means significant at 5 percent.
Over the period 1993–2023, wheat cultivation and production showed consistent growth, though at varying rates. In Period I (1993/94–2002/03), both wheat area (4.6%) and production (4.5%) experienced substantial and statistically significant growth. In period II (2003/04–2012/13) saw continued expansion, with positive and significant growth in wheat area (3.4%), yield (3.2%), and production (6.7%). However, in period III (2013/14–2022/23) recorded a slower but steady increase, with wheat area (1.7%), yield (2.5%), and production (4.3%) still showing significant growth. Overall, from 1993 to 2023, wheat area (2.9%), yield (3.3%) and production (6.2%) all grew significantly, although the time trend did not notably influence yield growth.
3.4 Growth Rate of Area, Production and Yield of Wheat by Country
Country-wise, the estimation of wheat production shows considerable variation in the growth rates of the area under wheat (Table 4). During the 1990s, Ethiopia, Uganda, and Rwanda were the top three nations with high CGRs in areas under wheat cultivation.
| Country | Variables | Period I | Period II | Period III | Overall |
|---|---|---|---|---|---|
| Ethiopia | Area | 5.90*** | 3.50*** | 2.30*** | 3.40*** |
| Yield | 0.20 | 4.50*** | 2.30*** | 3.70 *** | |
| Production | 5.90*** | 8.00*** | 4.70*** | 7.10 *** | |
| Kenya | Area | −1.30 | −0.80 | −2.60** | −0.50 ** |
| Yield | 0.70 | 0.50 | 3.50 | 0.80 | |
| Production | −0.60 | −0.30 | 0.90 | 0.30 | |
| Uganda | Area | 5.91*** | 5.20*** | 1.00*** | 4.40*** |
| Yield | −0.40 | −0.70 | 1.10*** | −0.50 *** | |
| Production | 5.50*** | 16.30*** | 2.10*** | 3.90 *** | |
| Rwanda | Area | 10.45*** | −14.10*** | 4.50*** | 2.20 ** |
| Yield | −5.72** | 2.50*** | 2.60*** | 1.10 ** | |
| Production | 5.20 | −11.60** | 7.10*** | 3.30 ** | |
| Tanzania | Area | − 2.20 | 15.40*** | −5.30 | 1.00 |
| Yield | 3.00 | −13.50*** | 1.70 | − 0.70 | |
| Production | 0.90 | 1.90 | −3.60 | 0.30 |
- Source: Author's computation.
- *** means significant at 5 percent.
- ** means significant at 1 percent.
Ethiopia recorded a substantial decrease in area under wheat production from 1993–94 to 2022–23 when the CGR of the area of wheat decreased from 5.9% during period I to 2.3% in period III. In Uganda, there is a continuous, strict decline in the CGR of the wheat area. In Rwanda, there was an extreme decrement during Period II and a slight increment during Period III. Consequently, the CGR of the wheat area in East Africa experienced a decrease during the same decade. Although the CGR for the area under wheat cultivation has been positive, there has been a declining trend over the years.
Ethiopia exhibits strong and consistent wheat production growth (7.10% overall CGR), driven by moderate area expansion (3.40%) and improving yield (3.70%). Higher productivity has been helped by improvements in agriculture, such as more irrigation and better wheat varieties. This was especially true in Period II (8.00%), when wheat investments soared, and strong area growth (4.40%) led to wheat production growth (3.90%). However, yield remains stagnant (−0.50%), indicating that increases in production come mainly from cultivated land expansion rather than efficiency gains. Even though the production CGR was an impressive 16.30% in Period II, low productivity raises long-term sustainability concerns.
Lastly, Rwanda's wheat cultivation area changed a lot, growing by 10.45% in Period I, dropping sharply by 14.10% in Period II, and then rising again by 4.50% in Period III. Despite this, wheat production (3.30%) and yield (1.10%) have remained relatively stable, suggesting that productivity measures have mitigated land losses through developing high-yield, disease-resistant wheat varieties, and adoption of climate-resilient and drought-tolerant wheat can help mitigate climate-related production risks.
3.5 Instability Index for Wheat Cultivation Across East Africa
Growth rates only explain growth over time, not whether there is stable growth for that variable. Thus, to have a deeper understanding of the magnitude and pattern of changes in the level of production, cultivation area, and yield of the crops, instability of the region, production, and yield has been worked out by coefficient of variation and Cuddy-Della Valle measure instability and volatility index for wheat across Ethiopia, Kenya, Uganda, Rwanda, and Tanzania over different periods in the (Table 5).
| Country | Item | Period I | Period II | Period III | Overall | ||||
|---|---|---|---|---|---|---|---|---|---|
| CV | CDVII | CV | CDVII | CV | CDVII | CV | CDVII | ||
| Ethiopia | Area | 19.73 | 10.61 | 12.78 | 12.72 | 10.54 | 5.69 | 29.52 | 8.45 |
| Production | 20.00 | 9.66 | 24.44 | 6.54 | 19.46 | 16.58 | 60.30 | 15.14 | |
| Yield | 11.48 | 12.17 | 14.09 | 3.58 | 8.06 | 7.16 | 35.76 | 11.70 | |
| Kenya | Area | 7.88 | 7.28 | 12.27 | 8.17 | 11.96 | 5.50 | 10.85 | 10.28 |
| Production | 16.50 | 17.38 | 23.24 | 17.49 | 30.69 | −1.72 | 26.80 | 27.04 | |
| Yield | 15.34 | 16.19 | 19.33 | 16.13 | 28.97 | 1.19 | 25.08 | 24.33 | |
| Uganda | Area | 21.81 | 11.42 | 16.03 | 4.83 | 3.45 | 2.90 | 35.90 | 8.11 |
| Production | 20.20 | 17.97 | 14.54 | 9.10 | 8.13 | 7.39 | 32.75 | 7.32 | |
| Yield | 1.97 | 1.69 | 5.98 | 1.97 | 4.40 | 3.01 | 6.18 | 4.68 | |
| Rwanda | Area | 39.89 | 23.26 | 16.59 | 26.70 | 16.59 | 11.78 | 52.23 | 51.78 |
| Production | 29.65 | 26.37 | 47.10 | 23.16 | 26.10 | 20.51 | 51.15 | 46.74 | |
| Yield | 20.94 | 15.26 | 8.24 | 10.15 | 10.25 | 7.06 | 16.58 | 13.59 | |
| Tanzania | Area | 30.83 | 32.72 | 31.79 | 24.82 | 31.79 | 11.13 | 42.99 | 42.90 |
| Production | 28.09 | 29.40 | 27.74 | 28.91 | 37.19 | 4.28 | 31.70 | 32.15 | |
| Yield | 34.28 | 32.78 | 52.22 | 21.55 | 20.30 | −1.08 | 40.62 | 40.39 | |
- Source: Author's computation; based on data of (FAO 2025).
Ethiopia's area stability has improved over time, with the CV decreasing from 19.73% to 10.54%. However, overall production remains highly volatile, as indicated by a CV of 60.30%. In Ethiopia, the area of production (8.45%) CDVI value shows moderate stability, with slight fluctuations in Period II (12.72%) but more consistency in Period III (5.69%). In the case of production (15.14%), it is more volatile, particularly in Period III (16.58%), indicating inconsistent wheat production trends. In the yield aspect (11.70%), it implies moderate instability, with high stability in Period II (3.58%). Therefore, this fluctuation in wheat production and productivity could be attributed to climate variability, input use, or policy changes, resulting in less stable production growth than the expansion of land in East Africa.
In the case of Uganda, the CDVI values for area coverage (8.11%), production (7.32%), and yield (4.68%), in the same, have the low yield variation coefficient of variation (6.18%) this indicates that the lowest instability compared to other East African countries, which shows that Uganda has the most stable wheat production trends, with minimal fluctuations. The stability is likely attributed to consistent climate conditions and improved farming practices, and strengthening subsidies and providing financial support for small-scale farmers could further enhance wheat production stability. Even though the instability index of wheat yield is based on the short-term and long-term effect of the coefficient of variation and the Cuddy-Della Valle instability index across East Africa, as shown in (Figure 2).
The Figure 2 shows the Cuddy-Della Valle instability index (CDVII) and the coefficient of variation (CV) for wheat yield at the country level. Ethiopia exhibits the highest CV, indicating substantial short-term yield fluctuations relative to its mean production; in the other case, its CDVII is significantly lower, suggesting that, despite frequent variations, the long-term instability trend remains moderate. In Tanzania, on the other hand, the relationship between CV and CDVII are more balanced, means that Tanzania's wheat yield is affected by both short-term changes and structural inconsistencies. On the contrary, Kenya and Uganda show moderate instability in both indices, reflecting a relatively stable yield pattern compared to Ethiopia and Tanzania, whereas Rwanda has the lowest values, indicating the least instability in wheat production.
3.6 Decomposition Analysis of Wheat Production Across East Africa
The relative contributions of harvested area, productivity, and their interaction effects on the total wheat production variability of wheat production growth across East Africa are presented in (Table 6). The result reveals that an increase in wheat production during period III (2013/14–2022/3) was mainly due to harvested area, with a contribution of 73.1%. However, the contribution of productivity (93.09%) and the interaction effect between harvested area and productivity (16.04%) for wheat production growth was minimal during periods I and II, respectively. Only the harvested area effect had a less positive contribution to wheat production growth in the East Africa period (1993/94–2002/03) and was followed by the entire study period (1993–2023), and period III was the highest of all the remaining effects in all study periods.
| Period | Area | Yield | Interaction |
|---|---|---|---|
| I. 1993–94 to 2002–03 | 4.53 | 93.09 | 2.39 |
| II. 2003–04 to 2012–13 | 41.20 | 42.76 | 16.04 |
| III. 2013–14 to 2022–23 | 73.10 | 21.01 | 5.89 |
| All, 1993–94 to 2022–23 | 23.93 | 37.57 | 38.50 |
- Source: Author's computation; based on data of (FAO 2025).
For the study period (1993–2023), it added up to about a 38.5% interaction effect between harvested area and productivity (Table 6). Enhancing local wheat production through joint agricultural policies and technology transfer in irrigation systems would help stabilise production and increase wheat yields.
Based on Figure 3, the decomposition analysis of wheat at the country level of Ethiopia, Uganda, and Rwanda prioritises yield improvements. In contrast, Kenya is shifting from land expansion to intensification, and Tanzania has experienced extreme fluctuations but is now focusing on yield-driven growth. Ethiopia's overall agricultural development has been primarily driven by yield improvements (40.48), with a moderate reliance on land expansion (18.50) and increasing complementarity between the two (41.02). This indicates a strong focus on agricultural intensification rather than mere land expansion. Policies promoting productivity, such as improved irrigation, high-quality seeds, and mechanisation, should be maintained and strengthened to sustain and enhance this progress.
Though a somewhat negative area effect (−4.17) and interaction effect (−8.33), improvements in yield (112.50) drive Uganda's agricultural growth almost entirely. This confirms a yield-driven growth model, emphasising productivity overland expansion. With a positive area effect (17.22), a positive yield effect (58.08), and a positive interaction effect (24.70), Rwanda's agricultural growth has shifted from land loss to stable expansion. This shift towards balanced land expansion requires continued investment in intensification strategies like improved inputs and soil conservation. With a significant area effect (252.59) and a negative yield effect (−71.03), Kenya's previous land expansion dependency proved unsustainable. The negative interaction impact (−81.55) reinforced the ineffectiveness of past land expansion policies. The recent shift toward yield-driven growth is promising and should be supported by policies focusing on soil fertility, irrigation, and extension services. Tanzania's growth trend has a positive but unstable area effect (41.32), a strong but fluctuating yield effect (49.20), and a positive interaction effect (9.48). This shows how important it is to improve farming methods for long-term stability in productivity.
4 Discussion
The study specifically included Ethiopia, Kenya, Uganda, Rwanda and Tanzania across East African wheat crop growth trends, compound growth rates, and instability. The study exposed significant variations in the growth trend, unstable area output, and wheat yield.
Over the years, total wheat production increased despite a decline in the cultivated area. Still, in 2023, wheat production decreased for many reasons, including spreading diseases like smuts, rusts, Septoria leaf blotch, foot and root diseases, and fusarium head blight (Grüter et al. 2022).
Other contributing factors included adverse climatic conditions, regional and cross-border conflicts, the post-effect of the COVID-19 pandemic, climate shocks, foreign currency shortages, the economic impact of the Russia-Ukraine crisis, and limited irrigation initiatives in major wheat-growing areas of East Africa (de Siqueira et al. 2022), this study consistent with the study of (Sihmar 2014) on growth and instability in agricultural production in Haryana a district level analysis.
East Africa's wheat production has seen Ethiopia's cultivated area and output grow faster than expected, with Uganda close behind. This improvement is because of balanced land growth and yield efficiency, the availability of high-yielding varieties of seeds, and new technologies in cultivation methods, which aligns with the study (Unjia et al. 2021). Kenya and Tanzania, on the other hand, are seeing their land use decline and production stay the same; on the other hand, Uganda and Rwanda have unpredictable but promising production trends that require more yield optimisation measures. It indicates that enhancements in efficiency, driven by increased government investment and initiatives in advanced agricultural practices and the expansion of irrigation initiatives, have played a crucial role in this production growth (CIMMYT 2023; World-Grain 2024). Conversely, other regions have experienced relatively stagnant progress, leading to a heightened dependence on imports to satisfy domestic demand.
During the decade of the 2010s, CGRs of the wheat area in Tanzania were relatively better than the others in East Africa and the 1990s, whereas, during the 2020 s, there had been a marginal decline in the CGR of the wheat area. On a national level, wheat CGR has decreased in some states and increased in others, which happened due to the use of different hybrid wheat, chemical fertilisers, and technical knowledge of the farmers (Gairhe et al. 2018), which was revealed in the study by (Pullabhotla et al. 2019) that showed the production growth rate of rice and wheat declined during the period from 2001 to 2019 in Nepal.
Hence, wheat is one of the imported commodities of East Africa and is not getting enough attention to improve its production status when it constitutes 28.8% of the of Africa (FAO 2025). Even though the instability of wheat production has decreased in terms of area and productivity in the current decade, this is because farmers do not have enough access to inputs, new technologies, and technical knowledge, which has affected production and prevented average yields (de Siqueira et al. 2022; CIMMYT 2023), from reaching their potential, impacting food security, income stability, and farmers' disposable income, similar to the growth and instability of oilseed production in India (Ramoliya 2022; Kanta Kaushik 1993). Therefore, the coefficient of variation (CV) examines relatively short-term changes to identify yearly fluctuations in wheat production caused by climatic conditions, diseases, or policy changes.
In contrast, CDVII considers trends and better understands wheat production's structural and long-term instability. Countries with a high CV but a lower CDVII, like Ethiopia, frequently experience yield fluctuations without a significant long-term deviation. Conversely, countries with high CV and CDVII, like Tanzania, face persistent instability that necessitates structural interventions. These countries with high CV values require short-term solutions, such as improved forecasting and risk management strategies. In contrast, those with high CDVII should invest in long-term improvements, such as irrigation infrastructure, soil health management, and climate-resilient wheat varieties, consistent with our findings and the studies seen by (Bisht and Kumar 2018), the growth and instability analysis of pulse production in India.
On the decomposition analysis of wheat production across East Africa based on the contributions of harvested area and the yield effect, Kenya has a dominant area effect (252.59) that is much more significant than the other countries. This indicates that changes in wheat production are primarily driven by the increase or decrease of farmland, which aligns with the findings in anticipated (Baviskar et al. 2020) regarding trends and decomposition of wheat production in western Maharashtra.
In contrast, Rwanda and Uganda exhibit a pronounced yield effect, with values of 112.50 and 58.08, respectively, indicating that enhancements in yield have significantly increased wheat production in these nations. Conversely, Ethiopia and Tanzania demonstrate a more equitable contribution from both area and yield effects, with Ethiopia slightly favoring yield influence. In contrast, Tanzania maintains a nearly equal distribution between area and yield. This finding is congruent with the study of (Ikuemonisan et al. 2020; Kumari and Singh 2024) aligns with research on trends, instability, and analysis from 1970 to 2018 regarding cassava production in Nigeria as well as the growth and volatility of maize production in Himachal Pradesh.
Although the interaction effect is relatively small across all nations, it remains significant in Ethiopia (41.02) and Kenya (81.55), where it reflects the combined influence of changes in area and yield. Kenya's negative interaction effect implies that although the nation's territory has expanded dramatically, its yield has not changed at the same rate. This variation could result from ineffective land use or declining returns. Ethiopia and Tanzania, on the other hand, show positive interaction effects, meaning that concurrently increasing the planted area and yield has helped drive growth in wheat output. Rwanda is the most yield-dependent nation in our study, even if its small area effect is the only one that influences it considerably. These variations draw attention to the different agricultural policies and restrictions between countries; some rely on land development, while others concentrate on yield optimisation to increase wheat production.
5 Conclusion and Recommendation
Emphasising Ethiopia, Kenya, Uganda, Rwanda, and Tanzania, this paper examined trends and instability across East African wheat production between 1993 and 2023. Though national growth patterns vary greatly, the results show a general rise in wheat output driven mainly by expanded cultivated acreage rather than productivity advances. The findings of this study revealed that Ethiopia has maintained steady production growth (7.10% CGR), supported by both area expansion (3.40%) and yield improvements (3.70%). In contrast, Kenya's wheat farming has declined (−0.50% CAGR) due to stagnant production (0.30%) and marginal yield gains (0.80%), reflecting inefficiencies in its wheat sector, whereas Uganda has recorded moderate growth, primarily from land expansion (4.40%), though its yield remains unchanged (−0.50%), underscoring the need for productivity enhancements. In the same way, Rwanda's wheat production has been highly volatile, with a sharp decline in the cultivated area during Period II (−14.10%), followed by a partial recovery in Period III (4.50%). Tanzania exhibits the highest level of instability, with fluctuations in land use and a declining yield (−0.70% CGR).
According to the CDVI result, Rwanda and Tanzania showed the most extreme volatility; Uganda had the steadiest development. Policy changes, climate shocks, and inconsistent farming methods most likely caused this variance. The decomposition study emphasises how land expansion, not efficiency improvements, mainly increases wheat production growth. In Period III (2013–2023), 73.1% of production growth stemmed from increased cultivated land, while only 21.01% resulted from yield enhancements, emphasising the limited impact of efficiency improvements in East Africa's wheat sector.
East African governments must shift from land expansion-based growth to productivity-driven strategies to achieve wheat self-sufficiency and food security. Such success requires investment in research, technology adoption, irrigation infrastructure and supportive agricultural policies. While Ethiopia and Uganda are progressing, Kenya, Rwanda, and Tanzania must address production instability to strengthen regional wheat security. Governments should fund research and extension programs to increase output and create disease-resistant, highly yielding wheat cultivars. Using drought-tolerant and climate-resilient wheat will help reduce climate change risks and improve long-term production.
Furthermore, modern irrigation systems must be expanded if production is to be stabilised and yields raised, as the area is still mostly dependent on rain-fed farming and is thus quite sensitive to temperature changes. Moreover, improving agricultural policies and supporting systems for small-scale farmers will help stabilise wheat production, and it should prioritise regional cooperation, with cross-border trade agreements facilitating food security and price stability to further expand the East African community.
Lastly, East Africa needs to diversify its wheat import sources while increasing its production to make its food system more stable and long-lasting during global price changes and political unpredictability (like the conflict between Russia and Ukraine).
Author Contributions
Habtamu Mossie Andualem: conceptualisation (equal), investigation (lead), formal analysis (equal), methodology (equal), project administration (lead), writing – original draft preparation (equal), writing – review and editing (equal), visualisation (equal), software, validation (equal). Mesfin Bekele Gebbisa: conceptualisation (equal), investigation (supporting), formal analysis (equal), methodology (equal), project administration (supporting), writing – original draft preparation (equal), writing – review and editing (equal), visualisation (equal), software, validation (equal). Zsuzsanna Bacsi: conceptualisation (equal), investigation (supporting), formal analysis (supporting), methodology (supporting), project administration (supporting), writing – original draft preparation (equal), writing – review and editing (supporting), visualisation (supporting).
Conflicts of Interest
The authors declare no conflicts of interest.
Open Research
Data Availability Statement
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.
