The analysis, published in Discover Agriculture by Ibrahim L. Kadigi of Mbeya University of Science and Technology and Lekumok Kironyi of Mwalimu Nyerere University of Agriculture and Technology, draws on the 2019/20 National Sample Census of Agriculture, Tanzania’s most comprehensive agricultural survey. The census, run by the National Bureau of Statistics, covered thousands of villages across the mainland and captured detailed records of household crop production, landholdings, and input use. From an initial pool of 2,830 sorghum-producing households, the researchers assembled an analytical dataset of 2,000 farms with complete harvest information, spanning all seven of the country’s agro-ecological zones.

What the data revealed about fertilizer adoption was stark. Only 91 farms, or 4.6 percent of the sample, reported applying organic fertilizers such as manure to their sorghum, and just 59 farms, about 3 percent, used inorganic mineral fertilizers. The remaining 92 percent of farms grew the crop on whatever nutrients the soil still held. Adoption was heavily concentrated in the Southern Highlands, where 48 of the 59 inorganic fertilizer users were found, a pattern the researchers attribute to better market integration and stronger extension support. In the Central Zone, the heart of Tanzanian sorghum production with more than 255,000 hectares under the crop, not a single sampled farm reported using mineral fertilizer.

To compare productivity across these three farming systems, the team went beyond simple averages. They employed a two-stage Monte Carlo simulation framework, a stochastic technique that converts observed farm-level yields into full probability distributions. In the first stage, yields for each fertilizer category were parameterized as empirical distributions and resampled 500 times using Latin Hypercube Sampling, an efficient method that preserves the statistical properties of the parent data with relatively few iterations. In the second stage, a multivariate empirical procedure introduced correlated randomness across the three systems, ensuring that simulated outcomes respected the real-world covariance structure of the data rather than producing implausible values such as negative yields.

The simulation was validated in two rounds against the observed census data, with goodness-of-fit tests and probability density function overlays confirming that the simulated distributions reproduced the means, spreads, and extremes of actual harvests. The researchers are careful to note what the method cannot do: because fertilizer use was not randomly assigned, the results describe associations between fertilizer categories and yield outcomes, not causal effects. Farms that choose to fertilize may differ in soil quality, management skill, or access to markets in ways the census cannot fully capture. Still, the scale of the differences is striking.

Farms using no fertilizer averaged 1.30 tonnes per hectare, with a minimum observed yield of just 0.49 tonnes. Organic fertilizer users averaged 1.38 tonnes per hectare, a marginal gain accompanied by greater variability, with a coefficient of variation of 56.4 percent compared with 47.2 percent among non-users. The researchers link this inconsistency to the uneven nutrient density and variable quality of manure and compost, which depend on storage, handling, and application rates that few farmers can control precisely. Inorganic fertilizer users, by contrast, averaged 1.94 tonnes per hectare, nearly 50 percent above non-users, with a higher minimum yield of 0.66 tonnes per hectare that suggests mineral nutrients also cushion farmers against the worst outcomes.

The probability analysis sharpened the picture further. Among non-users, 38 percent of farms fell below the national average yield of 1.0 tonne per hectare, and only 10 percent exceeded the 2.0-tonne benchmark widely regarded as the standard for economically viable sorghum production. Organic fertilizer users fared no better in the short term: 41 percent fell below 1.0 tonne, and just 8 percent cleared 2.0 tonnes, a result consistent with evidence from Kenya, Ethiopia, and Burkina Faso showing that organic inputs build soil structure and fertility over multiple seasons but rarely deliver immediate yield gains on their own. Inorganic fertilizer users told a different story. Only 19 percent fell below 1.0 tonne, while 30 percent exceeded 2.0 tonnes, a threefold increase over non-users.

When the bar was raised to the aspirational threshold of 3.0 tonnes per hectare, aligned with global productivity standards and Tanzania’s national goal of doubling cereal output by 2030, the gap widened further. Ninety percent of non-users and 92 percent of organic users remained below 2.0 tonnes, and only 1 percent and 4 percent respectively surpassed 3.0 tonnes. Among inorganic fertilizer users, however, 18 percent, nearly one farm in five, achieved yields above 3.0 tonnes, and a further 12 percent landed between 2.0 and 3.0 tonnes. The authors describe this as a tiered performance gradient: non-users trapped at subsistence levels, organic users marginally and inconsistently better, and mineral fertilizer users shifted decisively toward surplus production with commercial potential.

The findings arrive with important caveats that the authors themselves emphasize. The census does not record fertilizer formulations, nutrient composition, application rates, or timing, so the study cannot say which products or practices drive the gains. It also cannot identify farms applying organic and mineral fertilizers together, leaving integrated soil fertility management, the approach most agronomists consider optimal, empirically untested here. The small number of fertilizer users in the sample limits precision, and simulated iterations, however sophisticated, are not a substitute for additional field observations. Confounding factors such as improved seed varieties, rainfall, and soil type may contribute to the yield differences observed.

Even so, the policy implications are difficult to ignore. Only about one in twelve Tanzanian sorghum farms uses any fertilizer at all, and the yield gap between fertilized and unfertilized fields mirrors patterns documented across Ethiopia, Nigeria, Ghana, and Zimbabwe. The study recommends expanding access to inorganic fertilizers through targeted subsidies and improved distribution, strengthening extension services so that farmers apply nutrients correctly, and integrating mineral inputs with organic amendments to secure both immediate yields and long-term soil health. For a crop that anchors food security in Tanzania’s drought-prone center, where sorghum occupies nearly a quarter of cultivated land in regions like Singida, closing even part of this gap could transform a subsistence staple into a reliable source of food, income, and climate resilience.

Subject of Research: The contribution of organic and inorganic fertilizer use to sorghum productivity in Tanzania's rainfed smallholder farming systems

Article Title: Organic and inorganic fertilizer use and sorghum productivity in Tanzania’s rainfed farming systems using national sample census of agriculture data

Article References: Kadigi, I. L., & Kironyi, L. (2026). Organic and inorganic fertilizer use and sorghum productivity in Tanzania’s rainfed farming systems using national sample census of agriculture data. Discover Agriculture, 4(1), Article 312. https://doi.org/10.1007/s44279-026-00777-z

Image Credits: AI Generated

DOI: 10.1007/s44279-026-00777-z

Keywords: sorghum, Tanzania, fertilizer, inorganic fertilizer, organic fertilizer, crop yield, smallholder farming, rainfed agriculture, Monte Carlo simulation, food security, soil fertility, National Sample Census of Agriculture

The research, conducted by Ibrahim L. Kadigi of Mbeya University of Science and Technology, draws on microdata from the 2019/20 National Sample Census of Agriculture, a comprehensive survey covering more than 5,000 rice-growing households across Mainland Tanzania, with the earlier 2007/08 census used to normalize and stabilize yield comparisons. Rather than relying on simple averages or regression models, the study deployed a nonparametric stochastic simulation framework. Using Latin Hypercube Sampling and a Multivariate Empirical distribution, the analysis generated 500 simulated yield draws for each of 22 unique combinations of seed type and agroecological zone, producing 11,000 simulated yields in total. This Monte Carlo approach captures the full probability distribution of farm yields, including variance, skewness, and extreme outcomes, offering a far richer picture of production risk than conventional mean-based comparisons.

The methodological core of the study is the so-called stoplight analysis, which ranks the probability that farms exceed, fall between, or drop below defined productivity thresholds. Under Scenario A, the researchers set a lower cut-off of 1.5 tonnes per hectare and an upper benchmark of 3.0 tonnes per hectare, roughly the national target range. Under Scenario B, aligned with global standards, the thresholds rise to 2.0 and 4.5 tonnes per hectare. Because average rice yields in Tanzania hover around 2.4 tonnes per hectare, well below the global average of about 4.7 tonnes and far short of the 5 to 7 tonnes achievable with better management, these thresholds effectively frame the gap between ordinary and exceptional performance.

The results are unambiguous. At the national level, 29 percent of farms using improved seeds exceeded the 3.0 tonnes per hectare benchmark, compared with only 20 percent of local-seed farms, a 45 percent increase in the likelihood of high yields. Meanwhile, the share of farms falling below the 1.5 tonnes per hectare floor dropped from 38 percent among local-seed users to 30 percent among improved-seed users. On the mainland, the advantage widened further: 36 percent of improved-seed farms surpassed 3.0 tonnes per hectare versus just 21 percent of local-seed farms, a 71 percent relative improvement, and the proportion of very low-yielding farms fell from 36 percent to 23 percent. In Zanzibar, where overall productivity is lower, improved seeds still nearly doubled the probability of exceeding the benchmark, from 11 percent to 19 percent, and cut the low-yield share from 52 percent to 41 percent.

When the more demanding global thresholds were applied, the absolute probabilities of top-tier performance shrank, but the relative advantage of improved seeds grew even more pronounced. Nationally, the share of farms exceeding 4.5 tonnes per hectare doubled from 6 percent among local-seed users to 12 percent among improved-seed users, while the proportion of farms yielding less than 2.0 tonnes per hectare fell from 61 percent to 50 percent. On the mainland, 15 percent of improved-seed farms cleared the 4.5 tonnes per hectare bar, more than double the 6 percent of local-seed farms, and the low-yield share collapsed from 60 percent to 41 percent. In Zanzibar, improved-seed users were more than three times as likely as local-seed users to reach the global benchmark, and the share of extremely low-yielding farms declined from 79 percent to 64 percent. The pattern, the study emphasizes, is that improved seeds shift the entire yield distribution upward rather than simply nudging averages.

The agroecological breakdown adds crucial nuance. Tanzania’s rice landscapes span nine distinct zones, from the semi-arid Central Zone with erratic rainfall of 400 to 700 millimeters, to the fertile, well-watered Southern Highlands receiving 900 to 1,400 millimeters annually. In the Northern Highlands, improved seeds raised the probability of exceeding 3.0 tonnes per hectare from 50 to 55 percent and essentially eliminated the risk of yields below 1.5 tonnes per hectare, dropping it from 4 percent to zero. In the Southern Highlands, high-yield probabilities climbed from 34 to 52 percent while low-yield risk was nearly halved from 28 to 15 percent. Even in structurally disadvantaged zones, improved seeds delivered measurable gains: in the Southern Zone, the share of farms exceeding the benchmark rose from a dismal 2 percent to 20 percent, and in the Eastern Zone, high-yield probabilities increased from 14 to 27 percent while low-yield risk fell from 32 to 21 percent.

Yet the study is candid about limits. In the Southern and Western zones, more than half of farms remained in the low-productivity band even with improved seeds, and under the higher global thresholds, low-yield probabilities in some zones climbed to 68 to 79 percent despite adoption. The Western Zone even showed a slightly higher probability of very poor yields among improved-seed users under the strictest scenario, at 79 percent versus 73 percent for local seeds. These findings signal that seed quality alone cannot overcome deeper constraints such as soil acidity, poor drainage, limited irrigation, and inadequate extension coverage. The authors argue that seed subsidies and input programs should therefore not be applied uniformly nationwide but calibrated to zone-specific bottlenecks, pairing improved seeds with irrigation expansion, water harvesting, soil fertility restoration, lime subsidies, and drainage improvement where those constraints dominate.

The adoption picture itself reveals a stark institutional divide. While fewer than 5 percent of mainland farmers use improved rice seeds, adoption in Zanzibar reaches nearly 30 percent, a difference the study attributes partly to targeted seed subsidy schemes, strengthened last-mile distribution through shehia-level cooperatives, and public-sector seed multiplication programs on the islands. The low mainland uptake reflects weak seed systems, high transaction costs, market inefficiencies, limited extension services, and affordability barriers, compounded by the fact that many smallholders rely on informal, recycled seeds with declining genetic purity and germination performance. The census data also cannot distinguish certified improved seeds from quality-declared or farmer-saved improved varieties, a limitation the author notes may cause the estimated productivity differences to understate or overstate the true performance of certified material.

Validation of the simulation framework was rigorous. Observed and simulated yield distributions overlapped closely across all four major farmer groups, with means, standard deviations, coefficients of variation, and distribution shapes aligning almost perfectly. For instance, mainland improved-seed farms showed an observed and simulated mean of 2.77 tonnes per hectare with identical standard deviations of 1.39 and coefficients of variation of roughly 50 percent. No outliers were removed, since rare high-yield observations plausibly reflect practices such as the System of Rice Intensification, and the nonparametric framework deliberately retains tail outcomes to represent the full spectrum of production risk. The close correspondence reflects correct model specification rather than overfitting, the study explains, because simulated draws are generated directly from empirical distributions.

The policy implications extend well beyond Tanzania’s borders. Rice consumption in Sub-Saharan Africa has surged from roughly 13 kilograms per capita annually in the 1970s to more than 30 kilograms today, and Africa imported over 16 million metric tonnes of rice valued at more than USD 6 billion in 2022 alone. Tanzania, despite cultivating more than 1.1 million hectares, still imports between 70,000 and 120,000 metric tonnes annually. By quantifying where improved seeds most effectively lift yield distributions and where complementary investments are essential, the study offers actionable evidence for the Agricultural Sector Development Programme II, the National Rice Development Strategy II, and the Tanzania Seed Sector Development Strategy 2030, while supporting progress toward Sustainable Development Goals on poverty, hunger, and climate action. In high-potential zones, the priority is scaling, mechanization, and market integration; in marginal zones, seeds must ride alongside water, soil, and knowledge investments. Improved seeds, the evidence shows, are a necessary lever for Tanzania’s rice transformation, but never a sufficient one on their own.

Subject of Research: The effect of improved rice seed adoption on rice productivity and production risk across agroecological zones in Tanzania, using national census microdata and nonparametric stochastic simulation.

Article Title: Improved seeds and rice productivity in Tanzania: policy insights from national sample census data

Article References: Kadigi, I. L. (2026). Improved seeds and rice productivity in Tanzania: policy insights from national sample census data. BMC Agriculture, 2(1), Article 35. https://doi.org/10.1186/s44399-026-00042-0

Image Credits: AI Generated

DOI: 10.1186/s44399-026-00042-0

Keywords: improved seeds, rice productivity, Tanzania, agroecological zones, stochastic simulation, National Sample Census of Agriculture, food security, smallholder farmers, yield thresholds, seed systems, ASDP-II, NRDS-II