Ibrahim L. Kadigi- Department of Business Management, College of Humanities and Business Studies, Mbeya University of Science and Technology (MUST), Mbeya, Tanzania
Introduction: Rice is a major staple and commercial crop in Tanzania, playing a vital role in ensuring food security, supporting rural livelihoods, and contributing to national economic growth. However, smallholder productivity remains constrained by suboptimal input use and agroecological variability. Among the strategies debated to improve yields is the use of fertilizers, both organic and inorganic, whose effectiveness varies with climate, soil type, and farming system.
Methodology: This study employed a non-parametric stochastic simulation approach to model the impact of fertilizer use on rice productivity using nationally representative data from the 2019/20 National Sample Census of Agriculture (NSCA). Farmers were grouped into three categories: non-fertilizer users, organic fertilizer users, and inorganic fertilizer users. Yield performance was simulated and evaluated against two benchmarks: the national threshold of 3.0 t/ha and the global standard of 4.5 t/ha. Simulations were stratified by Agroecological Zone (AEZ) and administrative region, including Mainland Tanzania and Zanzibar.
Results: Simulation results indicate that inorganic fertilizer users achieved the highest probabilities of exceeding both productivity thresholds: 28% for yields >3.0 t/ha and 11% for yields >4.5 t/ha. Organic fertilizer users followed closely with 26 and 10% probabilities, respectively. In contrast, non-fertilizer users showed significantly lower probabilities: 19 and 5%. Downside risk (the likelihood of yield falling below the threshold) was also lowest among inorganic users. Spatial differences were observed, with farms in Mainland Tanzania generally performing better than those in Zanzibar. Variability across AEZs further emphasized the influence of site-specific factors.
Discussion: The findings underscore that both inorganic and organic fertilizers significantly enhance rice productivity, although their impacts vary by region. Inorganic fertilizers have a more pronounced effect, particularly in minimizing downside yield risks. However, a one-size-fits-all strategy may be ineffective due to regional heterogeneity. Therefore, policies should prioritize region-specific fertilizer strategies, integrated soil fertility management (ISFM), input subsidy reforms, and strengthened agricultural extension services. These interventions can help advance climate-resilient rice production and contribute meaningfully to Sustainable Development Goals (SDGs) 2 (Zero Hunger) and 13 (Climate Action).
1 Introduction
Rice (Oryza sativa L.) is a vital staple crop for millions of households in Sub-Saharan Africa, serving as both a crucial food source and a key economic commodity. In Tanzania, rice ranks as the second most important cereal crop after maize in terms of production volume, area under cultivation, and household consumption (The United Republic of Tanzania – (URT), 2021). It significantly contributes to food security, rural livelihoods, and poverty reduction, particularly in regions such as Mbeya, Morogoro, Shinyanga, and Mwanza, where favorable agroecological conditions are ideal for its production. Despite this importance, rice productivity in Tanzania remains markedly low compared to global averages, primarily due to the limited adoption of modern inputs such as inorganic fertilizers and improved seed varieties (Kadigi et al., 2020a; Kangile et al., 2018; Suvi et al., 2021; Sheahan et al., 2014; Sheahan and Barrett, 2017; Ariga et al., 2019; Twine et al., 2023).
National agricultural statistics indicate that the average rice yield in Tanzania ranges from 1.6 to 2.5 t ha−1, compared to global averages of 4.5 to 5.0 t ha−1 (FAO, 2023). This significant yield gap has raised concerns among policymakers, researchers, and development partners about the productivity constraints faced by Tanzanian rice farmers. Among the critical determinants of rice yield is the use of inorganic fertilizers, which supply essential macronutrients, particularly nitrogen (N), phosphorus (P), and potassium (K), to enhance plant growth and grain formation (Vanlauwe et al., 2014; Sommer et al., 2014; Giller et al., 2009; Lahmar et al., 2012). However, the uptake of inorganic fertilizers among smallholder rice producers remains low due to affordability constraints, limited access to credit, inadequate extension services, and underdeveloped input markets (Mwangi and Kariuki, 2015; Yokamo, 2020; Liverpool-Tasie et al., 2017; Yanggen et al., 1998; Burke et al., 2017).
The empirical relationship between fertilizer use and crop productivity has been extensively documented in agricultural economics literature. Inorganic fertilizers have been shown to substantially increase yields in cereals, including rice, when applied with the correct types, amounts, and timing (Jayne and Rashid, 2013; Jayne et al., 2018; Holden, 2019; Matsumoto and Yamano, 2011; Burke et al., 2017). In Tanzania, the government and donor agencies have implemented various programs, such as the National Input Voucher Scheme (NAIVS) and the Southern Agricultural Growth Corridor of Tanzania (SAGCOT), to promote the use of inputs among farmers. Nevertheless, the results have been mixed, with some areas showing substantial gains while others continue to experience stagnant productivity levels (Ires, 2025; Baumüller and Addom, 2020; Lin et al., 2020; Jararweh et al., 2023).
A key policy question that arises is: what is the actual yield gap between fertilizer users and non-users in rice farming, keeping other factors constant? Answering this question is crucial for evidence-based policy formulation, particularly as Tanzania seeks to transition from a subsistence to a more market-oriented agricultural system. Yield gaps are not merely a function of biophysical potential but also reflect differences in farmers’ access to inputs, knowledge, and infrastructure (Tittonell and Giller, 2013; Mungai et al., 2016). Isolating the effect of inorganic fertilizers using robust data and methods can provide insights into the potential gains achievable through increased adoption.
The National Sample Census of Agriculture conducted in 2019/2020 offers an unparalleled opportunity to investigate this issue. With data covering over 6,000 rice farms across Tanzania, the NSCA provides comprehensive farm-level information on input use, yields, production practices, and socio-economic characteristics. By leveraging this dataset, we can control for confounding variables and estimate the fundamental yield differences attributable to fertilizer use. This approach aligns with prior studies that used household panel and cross-sectional data to assess technology adoption and its productivity impacts (Duflo et al., 2004, 2008; Yanggen et al., 1998; Kassie et al., 2010; Mwangi et al., 2006; Ouma et al., 2014; Khonje et al., 2015).
When assessing yield gaps, it is also crucial to consider regional agroecological heterogeneity. Fertilizer responsiveness can vary widely across zones due to differences in soil fertility, rainfall patterns, and other environmental factors (Hillocks, 2014; Henderson et al., 2016; Leitner et al., 2020). For instance, areas in the Southern Highlands, such as Mbeya and Iringa, may exhibit higher yield gains from fertilizer application than coastal or semi-arid regions. This spatial variation has substantial implications for targeting and tailoring fertilizer policies. Furthermore, the study contributes to the broader discourse on sustainable intensification and climate-resilient agriculture in Sub-Saharan Africa. With increasing population pressures and land constraints, yield improvements through input intensification rather than area expansion are seen as more sustainable and climate-smart (Pretty et al., 2012; Vanlauwe et al., 2014, 2019; Schut and Giller, 2020; Garnett et al., 2013; Kuyper and Struik, 2014; Rosegrant and Cline, 2003). Fertilizers, when used appropriately, can significantly enhance nutrient-use efficiency and support sustainable rice intensification strategies (SRI) being promoted across East Africa (Toungos, 2018; Dobermann, 2004; Wu et al., 2015; Hussain et al., 2020).
The findings of this study are therefore expected to inform three main policy objectives. First, they can guide the design of fertilizer subsidy and distribution programs by identifying regions and farm typologies with the highest yield response. Second, they can support the development of integrated soil fertility management (ISFM) strategies that combine inorganic and organic sources for improved soil health. Third, they can help align Tanzania’s rice productivity targets with the African Union’s Malabo Declaration on agricultural transformation and Sustainable Development Goal 2 (SDG2) on zero hunger. The study examines the role of inorganic fertilizers in closing the rice yield gap in Tanzania, utilizing data from the 2019/20 NSCA. By focusing on fertilizer use while controlling for other farm-level characteristics, the study aims to provide robust evidence to inform policy decisions that improve rice productivity and rural livelihoods. Given the centrality of rice in Tanzania’s food system and its potential to uplift millions of smallholder farmers, unlocking its productivity through informed input use policies is both timely and necessary.
2 Methodology
2.1 Study area
This study examines rice-producing farms across both Mainland Tanzania and Zanzibar, with a focus on assessing the impact of fertilizer use, specifically organic and inorganic fertilizers, on rice productivity, while holding all other variables constant. To capture regional differences in farming conditions, rice farms in Mainland Tanzania were further categorized into distinct Agroecological Zones (AEZs). These zones are characterized by unique climatic conditions, diverse soil types, and varied farming systems, all of which significantly impact agricultural outcomes. By analyzing rice productivity across these AEZs, the study provides a nuanced understanding of how fertilizer application performs under varying ecological contexts. This stratification allows for more accurate estimation of fertilizer effects by controlling for environmental heterogeneity. Furthermore, it enhances the practical relevance of the findings by ensuring that conclusions drawn are not only statistically representative but also applicable across Tanzania’s diverse agricultural regions.
Segmenting the study area into agroecological zones also facilitates a comparative evaluation of fertilizer effectiveness across different environmental settings. This is essential for designing region-specific recommendations that optimize fertilizer use and improve yield outcomes. Moreover, understanding how productivity responds to organic and inorganic fertilizers in each zone informs the development of climate-resilient farming strategies and localized agricultural policies. Such detailed insights are crucial for supporting evidence-based interventions aimed at boosting rice yields, ensuring food security, and promoting sustainable agricultural growth in Tanzania. Figure 1 is a map of Tanzania, showing the regions and their production in metric tons (MT) for the 2019/20 growing season as adopted from The United Republic of Tanzania – URT (2021).
Figure 1. The map of Tanzania showing the quantity of paddy harvested by smallholder farmers by region during 2019/20 agricultural year, Tanzania (The United Republic of Tanzania – URT, 2021).
In this study, the initial classification of rice-producing farms was based on geographic location, dividing them into two broad categories: Mainland Tanzania and Zanzibar. Farms located in Mainland Tanzania were then further disaggregated into eight agroecological zones (AEZs) to reflect regional variations in climate, soil, and farming practices. The Central Zone (CZ) includes the regions of Dodoma and Singida, while the Eastern Zone (EZ) comprises Morogoro. The Coastal Zone (CSZ) encompasses Dar es Salaam, Pwani (Coast), and Tanga regions. The Southern Zone (SZ) consists of Lindi and Mtwara. The Northern Highlands Zone (NHZ) includes Arusha, Kilimanjaro, and Manyara regions. The Southern Highlands Zone (SHZ) comprises Iringa, Mbeya, Songwe, Njombe, and Ruvuma. The Lake Zone (LZ) includes Geita, Shinyanga, Simiyu, Mara, Mwanza, and Kagera, while the Western Zone (WZ) encompasses Katavi, Kigoma, Tabora, and Rukwa regions. This zonal classification enabled a more refined analysis of rice productivity and input use across Tanzania’s diverse agroecological landscapes.
2.2 Data sources
This study draws on nationally representative data obtained from the 2019/20 National Sample Census of Agriculture (NSCA), with additional reference to the 2007/08 NSCA to support normalization and adjustment of selected variables. The NSCA is implemented by Tanzania’s National Bureau of Statistics (NBS) and serves as the country’s primary statistical instrument for generating comprehensive information on the agricultural sector. The census collects detailed data on farming households, including landholding size, crop output, livestock ownership, and the application of key production inputs such as fertilizers and improved seeds. Beyond production-related variables, the NSCA also captures information on rural infrastructure, access to agricultural services, and household welfare indicators. These data provide an important empirical basis for assessing variations in agricultural productivity and for evaluating the outcomes of policy and programmatic interventions implemented within the agricultural sector. In this regard, the census offers a robust framework for examining the effectiveness of initiatives undertaken by government institutions, including the Agricultural Sector Lead Ministries (ASLMs), as well as other development stakeholders.
The NSCA adopted a two-stage stratified sampling design to ensure full national coverage and representativeness. In the first stage, Census Enumeration Areas (CEAs) defined under the 2012 Population and Housing Census were selected as Primary Sampling Units (PSUs). These PSUs were allocated proportionally across regions and districts to reflect spatial and agroecological diversity. In the second stage, agricultural households were randomly drawn from each selected PSU, with eligibility restricted to households engaged in crop and/or livestock production. The sampling framework comprised 2,820 PSUs, of which 2,670 were located in Mainland Tanzania and 150 in Zanzibar. Although the census covers both jurisdictions, the present analysis focuses exclusively on Mainland Tanzania, consistent with the study’s scope and objectives. The sampling design applied a Probability Proportional to Size (PPS) approach, ensuring that areas with greater agricultural activity had a higher likelihood of selection. This strategy enhanced the statistical precision and reliability of the resulting estimates. A detailed description of the sampling design and data collection procedures is provided in the official NSCA methodological report (The United Republic of Tanzania – URT, 2021).
2.3 Data processing and simulation
2.3.1 Data processing
To support the simulation analysis, rice production records were extracted from the 2019/20 National Sample Census of Agriculture (NSCA). The dataset was first filtered to isolate rice farming households and then classified into three mutually exclusive treatment groups based on their reported use of fertilizer: non-fertilizer users (F₀), organic fertilizer users (F₁), and inorganic fertilizer users (F₂). Treatment classification was based on self-reported input usage from the NSCA survey instruments. Farmers in the F₁ category (organic system) were those who reported using organic soil amendments such as compost, farmyard manure, green manure, or other non-synthetic materials. Conversely, farmers in the F₂ category (inorganic system) reported applying synthetic fertilizers such as urea, diammonium phosphate (DAP), or NPK formulations. Respondents were only classified under F₁ or F₂ if they used the specified type of fertilizer exclusively. Mixed users were excluded to preserve treatment integrity in the simulation model.
The dataset was further disaggregated by geographical region (Mainland Tanzania and Zanzibar) and Agroecological Zones (AEZs) using the classification scheme proposed by Kadigi et al. (2025a, 2025b). This zonal classification accounts for climatic, edaphic, and elevation-related differences in agricultural potential across the country. Table 1 presents the number of observations per fertilizer-use category within each AEZ. This structured breakdown enabled a context-specific stochastic analysis of rice productivity across diverse farming systems and input regimes.
Table 1. Number of observations for rice farming practices under three fertilizer-use categories per agroecological zone (AEZ).
2.3.2 Data simulation
This study applies a stochastic simulation approach (SSA) to model rice yield variability under different fertilizer-use practices across Tanzania’s agroecological zones. The simulation framework is rooted in a non-parametric Monte Carlo technique, drawing from methodologies established by Richardson et al. (2007, 2008) and adapted to Tanzanian contexts by Kadigi et al. (2020a, 2020b, 2025a, 2025b). Stochastic simulation enables the modeling of uncertain agricultural systems by generating a wide range of possible outcomes derived from empirical probability distributions. It is particularly effective in assessing variability due to external factors such as climatic shocks, input choices, or management practices (Nielsen et al., 2000; Bishwal, 2008).
The first stage of the simulation involved transforming observed yield data from the NSCA 2019/20 into a stochastic form. Farmers were grouped into distinct management categories based on fertilizer use (F₀: no fertilizer, F₁: organic, F₂: inorganic), and their yields were further disaggregated by agroecological zone (AEZ). This classification enabled the development of 30 unique stochastic models, each corresponding to a specific fertilizer-use type within a given AEZ (as shown in Table 1). These models represent the core farming systems assessed in the simulation. To model the yield distribution realistically, we adopted the Multivariate Empirical (MVE) distribution technique (Richardson et al., 2000). This method allows for the simultaneous simulation of multiple correlated variables and ensures that simulated values remain within feasible bounds, for instance, avoiding negative yield estimates. The parameters of the MVE distributions were estimated using percentage deviations (residuals) of observed yields from their respective means. This method effectively captures the historical variability and probabilistic characteristics of rice yields across Tanzania’s diverse production environments.
Stochastic yields in this study reflect the inherent variability farmers encounter due to rainfall variability, pest infestations, differences in soil fertility, and varying management intensity. While deterministic yields represent average observed yields from the NSCA dataset, stochastic yields integrate this uncertainty, thereby producing a more realistic representation of yield distributions for simulation purposes. The mathematical transformation from deterministic to stochastic yields is outlined in Supplementary material section (Supplementary A1 and A2).
The simulation process employed Latin Hypercube Sampling (LHS), which is well-suited for stochastic modeling due to its efficiency in sampling from empirical distributions with limited computational iterations. For each of the 30 farming system combinations, 500 simulations were conducted, resulting in 15,000 yield scenarios (30 models × 500 draws). The simulations were executed using the SIMETAR® software package1, which is widely used for stochastic risk analysis in agriculture.
To ensure the credibility of the simulated results, a validation step compared the simulated yield distributions with their corresponding observed distributions. This was done through visual inspection of probability density functions (PDFs) and numerical comparison of summary statistics (mean, standard deviation, coefficient of variation, minimum, and maximum values). The close alignment between observed and simulated distributions confirms that the stochastic models accurately replicate historical yield behavior without imposing restrictive parametric assumptions. Ultimately, this modeling approach enables a rigorous assessment of how rice productivity responds to different fertilizer-use practices across Tanzania’s ecological zones, providing a robust platform for evidence-based decision-making.
2.4 Ranking of target probabilities using the stoplight function
The Stoplight Chart function was used to rank the probabilities that rice farms would achieve the maximum yield thresholds and fall below the minimum values per unit area (hectares, or ha, in this study). The stoplight function calculates the probabilities of (a) exceeding the upper target (green), (b) being less than the lower target (red), and (c) falling between the targets (yellow). The views from various rice actors, particularly farmers, and literature review, including the current report of the National Sample Census of Agriculture (NSCA) The United Republic of Tanzania – URT (2021), revealed that the average yield in Tanzania ranges between 1–3.3 t ha−1, hence, we set our minimum threshold to be 1.5 t ha−1 and the maximum being 3.0 t ha−1. However, since the aim of most national initiatives, such as the ASDP-II (The United Republic of Tanzania (URT), 2016) and the Tanzania Seed Sector Development Strategy – 2030 (TSSDS 2030) (Minde et al., 2024), is to double the productivity of major crops, including rice and maize, by 2030, we also tested another scenario with high threshold values. This scenario encompassed a minimum value of 2.0 t ha−1 and a maximum value of 4.5 t ha−1, aligning with the global standard of 3.5–4.66 t ha−1 (Bin Rahman and Zhang, 2023; FAO, 2024) (Figure 2).
Figure 2. Stoplight chart for ranking of the target probabilities. under two simulation scenarios: (a) Scenario with lower target = 1.5 t ha-¹ and upper target = 3.0 t ha-1; (b) Scenario with lower target = 2.0 t ha-¹ and upper target = 4.5 t ha-¹.
3 Results and discussion
3.1 Model validation
Figure 3 presents a detailed comparison of the observed and simulated yield distributions for rice farmers across three fertilizer use categories: no fertilizer (F₀), organic fertilizer (F₁), and inorganic fertilizer (F₂). The validation exercise covers both the national level (ALL. TZ) and a regional subset for Zanzibar (ZNZ), incorporating key statistical metrics, including mean, standard deviation (STD), coefficient of variation (CV), minimum, median, and maximum values. For rice farmers who did not apply any fertilizer (F₀) at the national level (Figure 3a), the observed mean yield was 2.16 t ha−1, closely matched by the simulated mean of 2.17 t ha−1. The standard deviations (1.12 observed vs. 1.15 simulated) and CVs (51.78% vs. 52.92%) also align closely. Furthermore, the simulated distribution mirrors the observed data in terms of minimum, median, and maximum yield values, indicating that the model effectively captures the central tendency and variability for non-fertilizer users.
Figure 3. Comparison of observed and simulated distributions for yield of rice farms using no fertilizer under different fertilizer treatments: (a) No fertilizer (F₀) in all of Tanzania; (b) Organic fertilizer (F₁) in all of Tanzania; (c) Inorganic fertilizer (F₂) in all of Tanzania; and (d) No fertilizer (F₀) in Zanzibar (ZNZ).
For organic fertilizer users (F₁), as shown in Figure 3b, the simulation model performs exceptionally well. Both the observed and simulated means are exactly 2.48 t ha−1, with matching standard deviations of 1.28 t ha−1. The CVs are nearly identical (51.80% vs. 51.57%), and other distributional parameters, including minimum, median, and maximum values, are consistent across observed and simulated datasets. This suggests high accuracy in modeling yield outcomes for farmers using organic fertilizer. For inorganic fertilizer users (F₂), illustrated in Figure 3c, the observed and simulated mean yields are also perfectly aligned at 2.50 t ha−1. The standard deviation (1.38 observed vs. 1.40 simulated) and CV (55.31% vs. 55.90%) are closely matched, despite slightly higher observed variability. The similarity in yield distribution metrics further confirms the model’s robustness in capturing yield dynamics under inorganic fertilizer application.
Finally, for Zanzibar-based farmers using no fertilizer (Figure 3d), the model demonstrates strong predictive ability. The observed and simulated mean yields are 1.88 t ha−1 and 1.87 t ha−1, respectively, and the standard deviations are 0.94 and 0.93, respectively. The CVs (50.07% vs. 49.73%) and other descriptive statistics also show close agreement. This consistency highlights the model’s reliability even in geographically distinct regions with unique agroecological conditions. Overall, the strong alignment between observed and simulated yield distributions across all fertilizer use categories and regions provides robust validation of the model. The near-identical means, standard deviations, and distributional shapes suggest that the stochastic simulation accurately reproduces real-world variability and central tendencies in rice yields. These findings confirm the model’s suitability for further scenario analysis and policy simulations aimed at improving input use efficiency and rice productivity in Tanzania.
3.2 Impact of fertilizer application on rice productivity across all farms in Tanzania
3.2.1 Scenario A: (lower thresholds: max 3.0 t/ha and min 1.5 t/ha)
Figure 4a offers a detailed comparative visualization of rice yield distributions across three fertilizer-use categories: non-fertilizer users, organic fertilizer users, and inorganic fertilizer users in both Zanzibar (ZNZFarms) and Mainland Tanzania (MTZFarms), as well as the combined national sample (ALL.TZFarms). The yield outcomes are segmented into three distinct probability classes: yields greater than 3.0 t ha−1 (green), yields between 1.5 and 3.0 t ha−1 (yellow), and yields less than 1.5 t ha−1 (red). These productivity thresholds provide a helpful benchmark for assessing the likelihood that rice farmers will achieve low, medium, or high productivity under varying fertilizer application regimes.
Figure 4. (a) Probabilities of rice farm’s productivity being greater than 3.0 t ha−1 (green), less than the lower cut-off value of 1.5 t ha−1 (red), and the probabilities of falling between 1.5 and 3.0 t ha−1 for farmers using no fertilizers (F0), organic fertilizers (F1) and inorganic fertilizers (F2) for farms in Tanzania Zanzibar (ZNZFarms_) and Mainland (MTZFarms_). (b) Probabilities of rice farm’s productivity being greater than 4.5 t ha−1 (green), less than the lower cut-off value of 2.0 t ha−1 (red), and the probabilities of falling between 2.0 and 4.5 t ha−1 for farmers using no fertilizers (F0), organic fertilizers (F1), and inorganic fertilizers (F2) for farms in Tanzania, Zanzibar, and Mainland.
Starting with the non-fertilizer user group (F0), the data reveal a significantly higher probability of poor yields across all regions. In Zanzibar (ZNZFarms_F0), the probability of achieving yields below 1.5 t ha−1 is a staggering 53%. In contrast, only 37% of farms fall within the moderate productivity bracket (1.5–3.0 t ha−1), and only 10% exceed 3.0 t ha−1. This distribution highlights the significant productivity challenges faced by fertilizer-deprived farms in the region. Comparatively, the Mainland (MTZFarms_F0) shows modest improvements, with 37% of farms falling below 1.5 t ha−1, 43% in the moderate range, and 20% attaining high productivity. The aggregate national data (ALL.TZFarms_F0) follow a similar pattern, with 38% of farms in the lowest productivity category, 42% in the moderate category, and 19% in the high-yielding category. Collectively, these figures suggest that the absence of fertilizer use is strongly correlated with higher chances of suboptimal yield performance, especially in Zanzibar.
In contrast, the distribution under organic fertilizer use (F1) shows a noticeable upward shift in productivity outcomes. For Zanzibar, the percentage of low-yielding farms drops to 46%, with 30% achieving moderate yields and 24% attaining yields greater than 3.0 t/ha. On the Mainland, performance is even better: only 29% of farms fall below 1.5 t ha−1, 45% lie in the moderate range, and 27% achieve high yields. The aggregated national sample (ALL.TZFarms_F1) shows a consistent improvement, with 33% of the yields being low, 41% moderate, and 26% high. This shift illustrates the beneficial role of organic fertilizers in boosting rice productivity. It is particularly noteworthy that the use of organic fertilizer substantially reduces the proportion of farms producing below the food security threshold (1.5 t ha−1) and enhances the probability of exceeding the high-yield threshold.
The most impressive yield performance is observed among users of inorganic fertilizers (F2). In Zanzibar, the proportion of low-yielding farms further declines to 44%, with 41% in the moderate category and 16% achieving high yields. Although the share of low-yield farms remains somewhat high in Zanzibar, even under inorganic fertilizer usage, the distribution still reflects a performance gain compared to F0 and F1 users. Interestingly, organic fertilizers appear to outperform inorganic fertilizers in achieving yields exceeding 3.0 t/ha. On the Mainland (MTZFarms_F2), only 27% of farms fall below 1.5 t/ha, 41% are in the middle range, and 32% surpass the 3.0 t/ha mark, the highest share among all categories. This demonstrates the substantial effectiveness of inorganic fertilizer in boosting productivity when conditions permit. The national-level distribution (ALL.TZFarms_F2) also supports this conclusion, with 31% of farms underperforming, 41% in the moderate range, and 28% achieving high yields.
Figure 4a clearly demonstrates that the use of fertilizer (organic or inorganic) significantly influences rice productivity in Tanzania. Non-users are far more likely to produce yields below 1.5 t/ha, particularly in Zanzibar, which suggests potential concerns about food insecurity. Organic fertilizers offer moderate improvements, helping a greater share of farmers reach both the 1.5 t ha−1 and 3.0 t ha−1 thresholds, with the impact of organic fertilizers being more noticeable in Zanzibar. However, inorganic fertilizers yield the most substantial productivity gains, particularly in Mainland Tanzania (the country’s central rice-producing region), where enabling conditions, such as infrastructure, input markets, and agronomic support, may be more favorable. These insights highlight the importance of promoting access to and adoption of fertilizers, particularly inorganic options, to narrow the yield gap and support food security objectives in Tanzania. The regional disparities further underscore the need for location-specific policy interventions to address the underlying structural constraints that limit fertilizer effectiveness, especially in Zanzibar. A summary of statistics for rice yield distribution under different fertilizer use is also presented in the Supplementary material section (Supplementary B).
3.2.2 Scenario B (higher thresholds: max 4.5 t/ha and min 2.0 t/ha)
Scenario B was also tested to see the probability of surpassing the recommended global higher threshold (4.5 t ha−1). This scenario aligns with international yield targets and provides deeper insights into the capacity of Tanzanian rice farms to meet or exceed global standards under various fertilizer regimes. Figure 4b presents the probabilities of rice yield distributions across the three fertilizer usage categories (F0, F1, and F2), using a more stringent set of yield thresholds. The high productivity benchmark is set at 4.5 t ha−1, the moderate range is between 2.0 and 4.5 t ha−1, and low productivity is defined as anything below 2.0 t ha−1.
The results reveal that among non-fertilizer users, the productivity distribution is overwhelmingly skewed toward low performance, especially in Zanzibar, where 80% of farms yield less than 2.0 t ha−1, only 17% fall within the 2.0–4.5 t ha−1 range, and a mere 3% exceed 4.5 t ha−1. This sharply highlights the dire productivity challenges in Zanzibar when no fertilizers are applied. On the Mainland, performance slightly improves, with 61% of farms producing below 2.0 t ha−1, 33% falling within the moderate range, and 5% achieving yields above 4.5 t ha−1. The national average follows a similar trend, with 62% of farms underperforming, 32% moderately performing, and only 5% meeting or exceeding the global standard. These outcomes highlight a significant yield gap among non-users, which is further accentuated under stricter thresholds.
For organic fertilizer users, the distribution indicates a moderate yet notable shift toward improved productivity, particularly in terms of the probability of achieving the global threshold of more than 4.5 t ha−1. In Zanzibar, 63% of farms fall below the 2.0 t ha−1 threshold, while 30% fall within the 2.0–4.5 t ha−1 range, and 6% exceed 4.5 t ha−1. On the Mainland, productivity is more favorable: only 43% of farms fall below 2.0 t ha−1, 44% fall in the moderate range, and 12% surpass the 4.5 t ha−1 benchmark. This trend continues in the national aggregate, where 48% of farms are low-yielding, 42% fall in the moderate range, and 10% exceed 4.5 t ha−1. These figures confirm that organic fertilizers enhance productivity and significantly increase the probability of achieving internationally acceptable yields, especially on the Mainland.
Inorganic fertilizer users (F2) exhibit slightly promising results under the high-threshold scenario, particularly in the probability of achieving high yield in the Mainland. In Zanzibar, a significant proportion (68%) of farms remain below 2.0 t ha−1, 28% achieve moderate yields, and 4% exceed the global target. While Zanzibar still underperforms relative to the Mainland, the use of inorganic fertilizer provides a slightly lower productivity boost than organic fertilizer. Although Mainland farms (MTZFarms_F2) perform considerably better, with 14% exceeding 4.5 t ha−1, the probability of producing below 2.0 t/ha is slightly higher (48%) compared to only 43% for organic fertilizer users. The national distribution aligns with this improvement, showing a slight increase in production above 4.5 t/ha, compared to 10% for organic farms. However, the likelihood of falling below 2.0 t ha−1 is relatively higher at 53%. These patterns strongly suggest that both organic and inorganic fertilizers are more effective alternatives at pushing yields beyond the global standard, especially where enabling agronomic conditions exist.
The scenario tested in Figure 4b shows that while fertilizer use generally improves yield outcomes, the magnitude of the benefit varies significantly by fertilizer type and regional context. Non-users are overwhelmingly unable to meet global benchmarks, while users of organic fertilizer show promising improvements, particularly in helping farmers avoid the risk of producing below the minimum thresholds. Inorganic fertilizer users, particularly on the Mainland, show the highest probabilities of achieving yields above 4.5 t ha−1. This has significant policy implications: to close the yield gap and meet international food security and competitiveness standards, enhancing access to and use of inorganic fertilizers should be prioritized, particularly through subsidy programs, input delivery systems, and tailored agronomic advisory services. The persistent lag in Zanzibar across all fertilizer categories suggests a need for more targeted support, including infrastructure development and improved access to quality inputs.
3.3 Impact of fertilizer application on rice productivity across agroecological zones under Scenario A
3.3.1 Non-fertilizer users across AEZs under Scenario A
Figure 5 presents a disaggregated analysis of rice productivity probabilities for non-fertilizer users across various AEZs of Tanzania, using standard yield thresholds. This visualization provides nuanced insights into spatial disparities in rice productivity, highlighting the impact of agroecological conditions in the absence of fertilizer use. In terms of achieving the maximum value of 3.0 t ha−1 without fertilizer application, the Southern Highlands zone (SHZFarms_F0) has the highest probability, at 36%, followed by the Central zone (33%) and the Northern zone at 28%. Of all the zones, the Western zone presents the highest probability of falling below 1.5 t ha−1, at 56%, followed by the Southern zone (55%) and the Coastal zone (45%). The remaining zones have probabilities ranging from 25 to 33%. The Northern zone presented a unique case: it was the only zone with none of the farms falling below the minimum threshold, and it had a high concentration (72%) of farms in the moderate productivity range.
Figure 5. Probabilities of rice farm’s productivity being greater than 3.0 t ha−1 (green), less than the lower cut-off value of 1.5 t ha−1 (red), and the probabilities of falling between the two thresholds for farmers using no fertilizers (F0), across the AEZs of Tanzania.
The analysis by AEZs underscores the heterogeneity in rice productivity outcomes for non-fertilizer users. While zones like the Eastern and Southern Highlands demonstrate latent agronomic potential, others, especially the Coastal, Southern, and Western zones, struggle to reach even moderate yield levels. These findings underscore the importance of tailoring agricultural input policies and development strategies to the specific agroecological realities. Fertilizer distribution, improved seed access, and supportive extension services must be spatially targeted to bridge productivity gaps and enhance food security in underperforming zones.
3.3.2 Organic fertilizer users across AEZs under Scenario A
Figure 6 provides a detailed analysis of rice yield distributions for farmers utilizing organic fertilizers (F1) across Agroecological Zones (AEZs) in Tanzania, under the standard yield threshold scenario. The zones with farmers using organic fertilizers include the CZ, EZ, SHZ, LZ, and WZ. According to the results, the EZ stands out with exceptional performance among users of organic fertilizers. Only 9% of EZ farms fall below the minimum threshold of 1.5 t ha−1, while a significant proportion (62%) achieve moderate productivity, and 29% attain yields beyond 3.0 t ha−1. These statistics suggest that the Eastern Zone offers favorable agroecological and soil conditions, enabling organic fertilizers to deliver substantial yield gains. Similarly, the SHZ demonstrates firm productivity among users of organic fertilizers. Only 20% of farms in this zone are classified as low-yielding, while 39% fall within the moderate range, and a significant 41% surpass the 3.0 t ha−1 benchmark. These figures imply that organic fertilizers are highly effective in SHZ, likely due to the region’s inherently fertile soils, favorable topography, and relatively consistent rainfall patterns.
Figure 6. Probabilities of rice farm’s productivity being greater than 3.0 t ha−1 (green), less than the lower cut-off value of 1.5 t ha−1 (red), and the probabilities of falling between the two thresholds for farmers using organic fertilizers (F1), across the AEZs of Tanzania.
The CZ exhibits moderate success. While 46% of farms achieve moderate yields and 19% exceed the high-yield threshold, a notable 34% fall into the low-productivity category, indicating a variable response to organic fertilization, possibly due to nutrient leaching, soil salinity, or other coastal soil constraints. Performance in the LZ mirrors that of the CZ. Here, 32% of farms using organic fertilizers produce less than 1.5 t ha−1, 42% achieve yields between 1.5 and 3.0 t ha−1, and 25% exceed 3.0 t ha−1. This suggests that while organic fertilizers offer a moderate yield boost, they also have limitations, potentially linked to soil fertility depletion or delayed nutrient release. The WZ, however, shows the weakest performance under organic fertilization. An alarming 43% of farms fall below the 1.5 t ha−1 cut-off, 57% fall within the moderate range, and notably, none exceed the high-yield threshold. The total absence of high-performing farms in WZ signals severe agronomic or soil fertility constraints that render organic inputs alone ineffective.
The results in Figure 6, therefore, highlight significant variation in the effectiveness of organic fertilizers across AEZs in Tanzania. While SHZ and EZ demonstrate the highest yield potential under organic fertilization, the low probabilities in WZ and CZ underscore the limitations of organic inputs under less favorable conditions. These findings underscore the need for context-specific fertilizer policies, potentially integrating organic and inorganic nutrient sources and tailoring extension services by AEZ to ensure equitable productivity growth. This zonal differentiation is essential for achieving national food security goals and aligning with sustainable agricultural development frameworks.
3.3.3 Inorganic fertilizer users across AEZs under Scenario A
Figure 7 provides a comparative overview of rice farm productivity across AEZs in Tanzania for farmers using inorganic fertilizers (F2), categorized under the standard thresholds (yields greater than 3.0 t ha−1, yields less than 1.5 t ha−1, and moderate yields between 1.5 and 3.0 t ha−1). The findings underscore the effectiveness of inorganic fertilizers in significantly improving productivity, especially when compared to farms using organic or no fertilizers, as shown in previous figures. In the Northern zone, farmers stand out with the most favorable productivity profile, 62% of farms exceed the 3.0 t ha−1 threshold, and only 4% fall below 1.5 t ha−1, signaling high fertilizer efficiency and a low probability of yield failure. Similarly, the CZ demonstrates robust outcomes, with 50% of farms exceeding 3.0 t ha−1, and only 19% at risk of underperforming, reinforcing the potential of inorganic fertilizers to enhance food security in these areas. Followed by the CSZ, which performs slightly better with 42% of farms achieving high productivity, though 20% still fall below the lower threshold.
Figure 7. Probabilities of rice farm’s productivity being greater than 3.0 t ha−1 (green), less than the lower cut-off value of 1.5 t ha−1 (red), and the probabilities of falling between the two thresholds for farmers using inorganic fertilizers (F2), across the AEZs of Tanzania.
The LZ and SHZ also show promising results. In LZ, 36% of farms exceed 3.0 t ha−1, and just 6% fall below 1.5 t ha−1, while the majority (58%) yield moderately. SHZ displays a similar pattern, with 35% of farms attaining high yields and 27% underperforming, placing this zone among the relatively successful adopters of inorganic fertilizers. These results suggest that in these zones, factors such as improved seed adoption, adequate water availability, and better agronomic practices may complement fertilizer use to enhance productivity. In contrast, the WZ, SZ, and EZ show mixed performance. WZ has 30% of farms producing over 3.0 t ha−1, but still has a significant 44% falling below 1.5 t ha−1, reflecting persistent challenges in translating fertilizer use into consistent yield gains. SZ records the lowest probability (14%) of farms exceeding 3.0 t ha−1 and 37% underperforming, with nearly half (49%) of farms in the moderate category. In EZ, 16% of farms surpass 3.0 t/ha, while 29% remain below 1.5 t ha−1, and 56% fall in the middle range. These findings suggest a moderate impact of inorganic fertilizers in these zones, potentially limited by issues such as soil degradation, poor fertilizer application practices, or limited access to complementary inputs, including irrigation.
These results demonstrate that inorganic fertilizers significantly enhance the probability of achieving high rice yields in most AEZs, especially in NZ, CZ, SHZ, and LZ. However, yield variability remains a concern in WZ, SZ, and EZ, where a substantial proportion of farmers still fall below critical yield benchmarks, despite the use of fertilizers. These disparities underscore the importance of context-specific fertilizer recommendations, enhanced extension services, and integrated soil fertility management strategies to ensure that the use of inorganic fertilizers yields equitable productivity gains across Tanzania’s diverse agroecological landscape.
3.4 Impact of fertilizer application on rice productivity across agroecological zones under Scenario B
3.4.1 Non-fertilizer users across AEZs under Scenario B
Figure 8 explores the productivity probabilities of rice farms using no fertilizers (F0) across Tanzania’s agroecological zones, but with higher threshold criteria: specifically, yields greater than 4.5 t ha−1 as a benchmark for high productivity (aligned with global performance standards), and yields less than 2.0 t ha−1 indicating poor productivity. This figure offers critical insight into how AEZ-specific productivity responds when stricter performance benchmarks are applied. The most glaring finding is the substantial expansion in the proportion of low-yielding farms (red zones) under these upper thresholds. For instance, the SZ shows that 93% of rice farms produce less than 2.0 t ha−1 without fertilizer, while only 1% exceed 4.5 t ha−1. This stark result indicates that without fertilizer application, achieving global-standard yields in SZ is nearly impossible, highlighting the zone’s dependency on external inputs to meet food security targets. Similarly, the CSZ reports that 76% of farms fall within the low-yield bracket, with only 6% achieving more than 4.5 t/ha, indicating climatic and soil limitations. Similarly, the WZ shows a troubling profile: 74% of farms yield below 2.0 t/ha, and only 6% exceed 4.5 t ha−1, echoing trends observed in other constrained AEZs.
Figure 8. Probabilities of rice farm’s productivity being greater than 4.5 t ha−1 (green), less than the lower cut-off value of 2.0 t ha−1 (red), and the probabilities of falling between the two thresholds for farmers using no fertilizers (F0), across AEZs of Tanzania.
Of all zones, the NZ has the second-highest probability (12%) of exceeding 4.5 t ha−1, after the SHZ (13%). NZ has 50% of farms in the 2.0–4.5 t ha−1 middle range, with the smallest probability (37%) of underperforming, confirming room for improvement but relative stability in yields. Despite having the highest probability of surpassing the global maximum threshold, SHZ still has a greater than 50% probability that yield will fall below the minimum cut-off. Notably, LZ shows promising results, with 6% of farms reaching the higher productivity threshold. However, more than half of its farms (51%) still yield below 2.0 t ha−1. This suggests that even in zones with stronger biophysical characteristics, non-fertilizer users are at risk of underperformance without external nutrient support. The CZ also demonstrates moderate outcomes, with 10% of farms achieving above 4.5 t ha−1 and 43% falling short of 2.0 t ha−1. While not as critical as CSZ or SZ, the CZ still showcases vulnerability in the absence of fertilizers.
These results show that when higher yield benchmarks are applied, the incidence of low-productivity farms sharply increases across all AEZs for non-fertilizer users, with limited capacity to attain high yields (4.5 t ha−1 or more). This analysis highlights the crucial role of fertilizers, particularly in environmentally disadvantaged zones like CSZ, SZ, and WZ. It underscores the need for policymakers to implement targeted fertilizer interventions and support to close the yield gap and achieve national rice production goals. Without such input support, many Tanzanian farmers will remain locked into suboptimal productivity, thereby undermining efforts to achieve food security and alleviate poverty.
3.4.2 Organic fertilizer users across AEZs under Scenario B
Figure 9 shifts the focus to evaluate the performance of rice farms using organic fertilizers (F1) across Tanzania’s AEZs under higher productivity thresholds. The results highlight stark differences in how well organic fertilizers enable farms to reach global productivity standards in different zones. For example, in the SHZ, farmers demonstrate the most promising results with organic fertilizers. Here, 28% of farms surpass the 4.5 t ha−1 threshold, while only 31% fall below 2.0 t ha−1, and the rest (41%) lie in the moderate range. These results reinforce the suitability of organic fertilizers in SHZ, which may be attributed to the rich soils, favorable altitude, and reliable rainfall. The EZ also performs relatively well, with 10% of farms exceeding 4.5 t ha−1, 39% underperforming, and a majority (51%) achieving moderate yields. This zone demonstrates that organic fertilizers can help reduce yield risk, but may need to be complemented by other inputs to push more farms beyond the global standard.
Figure 9. Probabilities of rice farm’s productivity being greater than 4.5 t ha−1 (green), less than the lower cut-off value of 2.0 t ha−1 (red), and the probabilities of falling between the two thresholds for farmers using organic fertilizers (F1), across the AEZs of Tanzania.
On the contrary, the CZ and Western Zone (WZ) show concerning outcomes. Although the CZ has a 15% probability of exceeding 4.5 t ha−1, 66% of farms fall below 2.0 t ha−1. Similarly, WZ shows no farms reaching high productivity, with 67% of farms producing less than 2.0 t ha−1. The LZ also appears to struggle under the higher threshold scenario. Here, 41% of farms produce below 2.0 t ha−1, 59% fall into the moderate category, and, notably, no farms exceed 4.5 t/ha. These findings suggest that while organic fertilizers may help prevent extremely low yields in LZ, they are insufficient to propel farms to high productivity levels on their own. Generally, these results suggest a serious performance gap for organic fertilizers in these zones, potentially due to inherent agroecological limitations such as salinity, poor soil structure, or reduced organic matter retention. In these areas, exclusive reliance on organic fertilizers may expose farmers to low yields and food insecurity.
The findings in Figure 9 reveal that while organic fertilizers can help raise average yields, especially in SHZ and EZ, they rarely enable farms to surpass the upper productivity benchmark of 4.5 t ha−1, except in favorable zones like SHZ. The significant proportions of farms falling below the 2.0 t ha−1 threshold in CZ and WZ indicate limited fertilizer efficiency, poor nutrient uptake, or structural constraints in farming. These findings suggest the need for targeted fertilizer strategies, such as blending organic and inorganic inputs, enhancing composting practices, or providing location-specific soil amendments. Additionally, policymakers should recognize the spatial heterogeneity of fertilizer impacts and tailor support mechanisms and extension services accordingly to unlock higher productivity potential across all AEZs in Tanzania.
3.4.3 Inorganic fertilizer users across AEZs under Scenario B
Figure 10 explores the probabilities of rice productivity exceeding the upper threshold of 4.5 t ha−1 falling below 2.0 t ha−1, or remaining within the moderate range of 2.0–4.5 t ha−1, among farmers applying inorganic fertilizers across Tanzania’s AEZs. The results indicate that although inorganic fertilizers improve productivity in several zones, achieving the global benchmark of 4.5 t ha−1 remains a challenge in many areas. The NZ and the LZ stand out as the most promising AEZs with a minimum probability of falling below 2.0 t/ha with inorganic farming practices. In the NZ, a promising 21% of farmers exceed 4.5 t ha−1, with only 11% falling below 2.0 t ha−1, and the majority (68%) achieving moderate yields, demonstrating that this zone stands out in converting inorganic inputs into high-end yields. The LZ follows with 25% of farms reaching high productivity, and a relatively low 21% falling short of 2.0 t/ha, indicating a balanced yet efficient productivity profile. The CZ also shows some promising trends: 13% of farms reach high productivity, 32% fall short of the minimum threshold, and the majority (55%) remain in the moderate range.
Figure 10. Probabilities of rice farm’s productivity being greater than 4.5 t ha−1 (green), less than the lower cut-off value of 2.0 t ha−1 (red), and the probabilities of falling between the two thresholds for farmers using inorganic fertilizers (F2), across the AEZs of Tanzania.
Although the CSZ has the highest probability (29%) of yield surpassing the maximum threshold, it has a significant proportion (50%) of farms underperforming, with only 21% between the thresholds. Similarly, the SHZ records that 16% of farms surpass 4.5 t ha−1, 49% underperform, while 35% achieve intermediate yields, highlighting moderate success with substantial room for improvement. On the other hand, the WZ and SZ display less favorable outcomes. In WZ, only 14% of farms meet the upper threshold, while a concerning 60% fall below 2.0 t ha−1. The situation is more critical in SZ, where only 9% of farms exceed 4.5 t ha−1, and 72% are below 2.0 t ha−1, suggesting significant inefficiencies in fertilizer use, poor agronomic conditions, or structural constraints such as water stress and limited seed-fertilizer synergy. Collectively, the results from Figure 10 reveal that while inorganic fertilizers are instrumental in increasing rice yields, only a limited fraction of farmers consistently achieve the upper productivity benchmark across most AEZs. High-performing zones, such as NZ and LZ, reflect the potential of inorganic inputs when supported by favorable conditions. In contrast, zones like SZ, WZ, and EZ highlight the need for integrated interventions, such as improved seed varieties, irrigation support, and enhanced extension services, to bridge the productivity gap and ensure more equitable yield gains across regions.
4 Discussion
The findings from this study reveal the critical role that fertilizers, both organic and inorganic, play in enhancing rice productivity among farmers in Tanzania. Through the application of stochastic simulation models and yield probability distributions across different agroecological zones (AEZs), it is evident that the combined use of fertilizers, particularly inorganic fertilizers, consistently improves the likelihood of achieving yields that exceed both standard (3.0 t ha−1) and upper productivity thresholds (4.5 t ha−1). However, while inorganic fertilizers demonstrate relatively higher productivity outcomes, organic fertilizers also offer promising results compared to non-fertilizer users, particularly in reducing the risk of extremely low yields.
The national-level simulations showed that rice farmers using inorganic fertilizers (F2) have a 28% probability of achieving yields above 3.0 t/ha, compared to 19% for those using organic fertilizers (F1) and only 10% for non-users (F0). Moreover, under high-yield thresholds, the probabilities drop sharply for all groups. Yet, inorganic fertilizer users still outperform the others with a yield success probability of 11%, compared to 6% for organic users and 5% for non-users. These results align with previous studies that highlight the superior performance of inorganic fertilizers in supplying readily available nutrients, leading to an immediate plant response and increased yields (Morris, 2007; Kelly et al., 2007; Finck, 2008; Sheahan et al., 2014; Sheahan and Barrett, 2017; Ariga et al., 2019; Marenya and Barrett, 2009a, 2009b; Ricker-Gilbert and Jayne, 2012, 2017).
Nonetheless, the role of organic fertilizers should not be undervalued. They significantly contribute to soil structure, long-term fertility, and sustainable productivity through enhanced microbial activity and improved water retention (Palm et al., 2001a, 2001b; Vanlauwe et al., 2005, 2014; Pretty et al., 2012; Branca et al., 2022). Our findings showed that organic fertilizer users experienced a 29% probability of exceeding 3.0 t ha−1 in certain AEZs (notably the Eastern Zone), far surpassing that of non-users. Organic inputs have also been linked to environmental benefits and cost savings, which enhance the resilience of smallholder systems (Tittonell and Giller, 2013; Mungai et al., 2016; Lekasi et al., 2001a, 2001b, 2002; Waithaka et al., 2007). In the Southern Highlands and Lake Zones, organic users had higher probabilities of productivity than non-users, confirming that organic fertilization can be adequate when combined with proper agronomic practices (Nandwa, 2001; Bationo et al., 2007; Bayala et al., 2018; Snapp et al., 2010).
The agroecological disaggregation provided further insights. For instance, in the Northern Zone, 62% of farmers using inorganic fertilizers surpassed the standard 3.0 t ha−1 threshold, the highest among all AEZs, indicating the influence of favorable climatic and soil conditions in maximizing fertilizer efficiency (Nduwimana et al., 2020; Ichami et al., 2019; Kaizzi et al., 2012; Jama et al., 2017). Meanwhile, the Coastal and Western Zones displayed relatively lower performance even with fertilizer use, suggesting that complementary interventions, such as irrigation, drought-tolerant varieties, or conservation agriculture, are necessary to unlock full productivity potential (Rockström et al., 2010, 2013; Kassie et al., 2008a, 2008b, 2009; Thornton et al., 2006, 2008). Furthermore, while inorganic fertilizers are vital for meeting short-term productivity targets and addressing food insecurity (Jayne and Rashid, 2013; Jayne et al., 2018; Ricker-Gilbert et al., 2013; Xu et al., 2009; Ragasa and Chapoto, 2017), their misuse or overdependence may lead to long-term soil degradation, environmental contamination, and increased input costs (Gruhn et al., 2000; Nair, 2019; Reardon et al., 2009; McCullough et al., 2012; Jayne et al., 2019). Therefore, promoting Integrated Soil Fertility Management (ISFM), a combination of organic and inorganic sources tailored to local conditions, is essential for sustainable intensification (Vanlauwe et al., 2010; Smaling and Dixon, 2006; Vanlauwe and Zingore, 2011; Mugwe et al., 2019; Place et al., 2003; Sommer et al., 2013; Zingore et al., 2007).
Lastly, policy implications are clear. To optimize productivity, agricultural extension services must promote fertilizer education, subsidy programs must target vulnerable farmers, and research institutions should enhance access to regionally adapted organic formulations (Kelly et al., 2003; Duflo et al., 2008, 2011; Dorward, 2009; Wiggins and Brooks, 2012; Takeshima and Nkonya, 2014). Furthermore, climate-smart strategies that integrate both fertilizer use and resilience mechanisms must be scaled up to ensure that Tanzania meets its food security objectives under SDG 2 (Constas et al., 2021; Hazell, 2017; Afenyo, 2012; Holmén and Hyden, 2011; Poulton et al., 2008).
5 Conclusion
This comprehensive analysis reveals that the application of both inorganic and organic fertilizers significantly enhances rice productivity across Tanzania’s agroecological zones, underscoring their crucial role in bridging yield gaps and transforming the rice sector. Notably, while inorganic fertilizers demonstrate superior returns in terms of yield probability and production stability, organic fertilizers also yield promising results when compared to non-users, offering a sustainable and cost-effective alternative, especially for resource-limited farmers. These insights are critical for achieving Sustainable Development Goal 2 (Zero Hunger), as improving rice yields directly contributes to national food self-sufficiency and reduces households’ vulnerability to chronic food insecurity.
Given rice’s strategic importance as both a staple crop and a source of income for smallholder farmers, enhancing productivity through increased access to fertilizers directly supports SDG 1 (No Poverty) by improving rural livelihoods and household incomes. Moreover, the findings advocate a shift toward Integrated Soil Fertility Management (ISFM), in which the synergistic use of inorganic and organic inputs can optimize productivity while maintaining soil health. Such sustainable practices not only build resilience to climate-related shocks but also contribute to SDG 13 (Climate Action) by promoting soil carbon retention and reducing long-term dependency on synthetic inputs.
To catalyze this transformation, policy action should prioritize investments in input delivery systems, agro-dealer networks, and farmer extension services to promote the efficient and equitable use of fertilizers. Government support should also focus on incentivizing climate-smart technologies and supporting agroecological research to adapt fertilizer strategies to specific local conditions. By aligning national agricultural strategies with the SDGs, Tanzania can position its rice sector as a vehicle for inclusive growth, rural poverty alleviation, sustainable resource use, and global market competitiveness.
Despite the robustness of the stochastic simulation framework and the use of nationally representative data, this study has several limitations that should be acknowledged. First, the analysis relies on cross-sectional data from the 2019/20 NSCA, which restricts the ability to examine inter-seasonal dynamics or long-term effects of fertilizer use on rice productivity. Second, the productivity thresholds used to categorize yield performance (below 1.5 t ha−1, 1.5–3.0 t ha−1, and above 3.0 t ha−1), while policy-relevant, are not statistically optimized cut-off points and should therefore be interpreted as indicative benchmarks rather than definitive productivity classes. Third, fertilizer use is modeled as a binary treatment, which does not capture variations in application rates, timing, fertilizer quality, or interactions with complementary inputs such as improved seed varieties, irrigation, and water management. Finally, the analysis focuses on yield probabilities and does not explicitly incorporate input costs, profitability measures, or returns on investment, which are critical considerations for both farmer decision-making and policy formulation. Future research should therefore adopt panel data approaches, integrate fertilizer dosage and cost information, and explore joint input optimization to provide a more comprehensive assessment of the economic and agronomic impacts of fertilizer use in Tanzania’s rice farming systems.
Data availability statement
The datasets used in this study are publicly available from the Tanzania National Bureau of Statistics. The correct access link is: https://microdata.nbs.go.tz/index.php/catalog/52.
Ethics statement
This study is based on secondary data obtained from the 2019/2020 National Sample Census of Agriculture (NSCA), which was conducted by the National Bureau of Statistics (NBS) of Tanzania in collaboration with the Ministry of Agriculture and the World Bank. The data are anonymized and publicly available for research and policy analysis purposes. Therefore, ethical approval and individual consent to participate were not required for this study. The use of the data complies with the terms and conditions set by the NBS for secondary data analysis.
Author contributions
IK: Writing – original draft, Data curation, Software, Formal analysis, Visualization, Methodology, Resources, Validation, Investigation, Writing – review & editing, Conceptualization, Supervision.
Funding
The author(s) declared that financial support was not received for this work and/or its publication.
Acknowledgments
The author sincerely appreciates the support and guidance provided by the Simetar team, particularly James Richardson and Jean-Claude Bizimana, for their assistance in applying the Monte Carlo Simulation model using the Simetar Add-In (www.simetar.com). Their contributions have greatly enriched the quality and robustness of this manuscript.
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.
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Supplementary material
The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fsufs.2026.1683883/full#supplementary-material
Footnotes
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Keywords: agroecological zones, inorganic fertilizer, organic fertilizer, rice, stochastic simulation, Tanzania
Citation: Kadigi IL (2026) The role of fertilizers in Tanzania’s rice production: policy insights from the National Sample Census of Agriculture. Front. Sustain. Food Syst. 10:1683883. doi: 10.3389/fsufs.2026.1683883
Edited by:
Amitava Rakshit, Banaras Hindu University, IndiaReviewed by:
Ayush Bahuguna, Banaras Hindu University, IndiaNaveena Saharan, Sher-e-Kashmir University of Agricultural Sciences and Technology of Jammu, India
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*Correspondence: Ibrahim L. Kadigi, a2lkZWFubml0b0BnbWFpbC5jb20=; aWJyYWhpbS5rYWRpZ2lAc3VhLmFjLnR6