Improved Seeds and Rice Productivity in Tanzania: Policy Insights from National Sample Census Data

doi:10.21203/rs.3.rs-7280225/v1

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Improved Seeds and Rice Productivity in Tanzania: Policy Insights from National Sample Census Data

https://doi.org/10.21203/rs.3.rs-7280225/v1

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Improving rice productivity remains a central pillar of Tanzania’s national strategy to achieve food security and rural development. While improved seed technologies have long been promoted as a pathway to higher yields and poverty reduction, there is limited empirical evidence on their actual performance at scale. This study utilizes nationally representative data from the 2019/20 National Sample Census of Agriculture (NSCA), complemented by the 2007/08 NSCA, to assess the impact of improved seed use on rice productivity in Tanzania, disaggregated by agroecological zones (AEZs) and seed types (improved vs. local). A stochastic simulation model was employed to evaluate the likelihood of rice farms meeting or exceeding productivity thresholds under varying conditions while controlling for other production factors. Two threshold-based scenarios were analyzed: (i) the probability of yield exceeding the standard national benchmark of 3.0 t/ha and (ii) the probability of surpassing the global high-yield benchmark of 4.5 t/ha. The results reveal that rice farmers using improved seeds consistently outperform those using local seeds in both yield level and probability of surpassing key productivity thresholds. Nationally, improved seed users had a 12% probability of exceeding 4.5 t/ha, compared to 6% among local seed users. Farms in Mainland Tanzania had a 15% rate, versus 6% for local seed users. Farms in Zanzibar had a 7% higher yield compared to only 2% for non-users. These benefits were more pronounced in high-potential AEZs such as the Southern Highlands and Eastern Zones, highlighting the role of agroecological targeting in input efficacy. Moreover, improved seed adoption reduced the risk of sub-optimal yields (< 1.5 t/ha), contributing to production stability and resilience. This study provides new insights for policymakers and development actors on the actual yield gap between improved seed users and non-users in rice production systems. The findings provide a critical baseline for informing seed sector reform, targeted subsidy programs, and planning for agroecological inputs. The research further contributes to advancing several Sustainable Development Goals (SDGs), particularly SDG 2 (Zero Hunger), SDG 1 (No Poverty), and SDG 12 (Responsible Consumption and Production). To optimize the benefits of improved seeds, the study recommends integrated approaches that combine seed adoption with access to complementary inputs, extension services, and market linkages.

Stochastic Simulation

Rice

Local Seeds

Improved Seeds

Agroecological Zones (AEZs)

Tanzania

Rice (Oryza sativa L.) is among the most critical staple foods globally, serving as a dietary foundation for over 3.5 billion people, more than half the world’s population (FAO et al., 2023a). It provides up to 50% of daily caloric intake in some countries and is a central pillar in achieving food security, reducing poverty, and promoting rural livelihoods (GRiSP, 2013). Global rice consumption continues to grow, driven by population growth, rapid urbanization, and income increases, particularly in developing economies. By 2050, global demand is projected to increase by 28%, with the most tremendous growth expected in Africa and South Asia (Seck et al., 2012; OECD/FAO, 2023). Rice occupies nearly 160 million hectares worldwide, with an average productivity of 4.7 tonnes per hectare; however, yields vary significantly across countries (FAOSTAT, 2023).

Africa has witnessed the fastest growth in rice consumption over the last three decades. Sub-Saharan Africa (SSA) alone has seen per capita consumption increase from 13 kg/year in the 1970s to over 30 kg/year today, with urban centers accounting for the majority of this demand (Africa Rice Center, 2021; Wopereis et al., 2013; Arouna et al., 2021; Adesina, 2019; Center, 2018). Rice is now the second most consumed cereal in SSA after maize. However, African rice production has not kept pace with demand, resulting in a significant import gap. In 2022, the continent imported over 16 million metric tonnes of rice, valued at more than USD 6 billion (World Bank, 2023; FAO, 2023b). This heavy dependence on imports exposes African nations to global price shocks, trade disruptions, and currency volatility, thereby threatening national food sovereignty and the resilience of local food systems (Jiang et al., 2021; Balistreri et al., 2022; Workie et al., 2020).

Tanzania represents a microcosm of these continental trends. With a population of over 65 million and rising urbanization, rice has become a key staple in the national diet. It is the second most important food and cash crop, after maize, with consumption at approximately 36 kg per capita annually (URT, 2019). Tanzania is one of the top 10 rice-producing countries in Africa, cultivating over 1.1 million hectares annually, primarily by smallholder farmers (FAOSTAT, 2023). The crop is not only vital for household food security but also for employment and income generation, particularly in the Southern Highlands, Eastern, and Lake Zones (URT, 2017; 2021a). Nevertheless, despite its importance, domestic production remains insufficient to meet national demand. Annual rice imports range between 70,000 and 120,000 metric tonnes, mainly sourced from Pakistan, India, and Vietnam (Mauki et al., 2023; Nkuba et al., 2016; Wilson & Lewis, 2015).

A significant factor limiting domestic rice production is low productivity. Tanzania’s average yield is estimated at just 2.4 t/ha, which is well below the global average and significantly lower than the achievable yields under optimal conditions, which range from 5 to 7 t/ha (IRRI, 2022; URT, 2019; Senthilkumar et al., 2020; Dossou-Yovo et al., 2020; Nhamo et al., 2020; Alou, 2020). This yield gap is primarily due to inadequate adoption of improved agricultural technologies, particularly certified improved seed varieties. Most farmers continue to rely on recycled or locally sourced seeds, which are often susceptible to pests, diseases, and climatic stress, resulting in suboptimal outcomes (Waithaka et al., 2021; Rutsaert et al., 2021; Willy et al., 2020; Walker & Alwang, 2015; Muzari et al., 2012; Maredia et al., 2000; Thompson & Gyatso, 2020). While improved seeds have demonstrated potential to increase yields by 30–50%, their adoption remains constrained by seed system inefficiencies, limited access to input markets, and weak extension services (World Bank, 2019; Langyintuo et al., 2010; Erenstein et al., 2011; Louwaars et al., 2013; Tripp, 2006; Rubyogo & Sperling, 2009; Monyo et al., 2004). This backdrop raises a crucial policy question: What is the actual yield gap between improved seed users and non-users in rice farming, controlling for other production factors? Addressing this question is essential for informing investment decisions, guiding input subsidy programs, and shaping seed sector reforms under Tanzania’s Agricultural Sector Development Programme II (ASDP-II) and the National Rice Development Strategy II (NRDS-II) (URT, 2017; 2021b; 2021c).

Despite the recognized importance of improved seeds, empirical studies on their impact in Tanzania remain limited and fragmented, often relying on small samples or case-specific assessments. Recent research highlights the importance of collecting large-scale, nationally representative data to capture heterogeneity in agroecological conditions and farming practices (Sheahan et al., 2014; Sheahan & Barrett, 2017; Ariga et al., 2019; Nin-Pratt & McBride, 2014; Mottaleb et al., 2016; Ayal et al., 2019; Jena & Tanti, 2023; Beaman et al., 2014; Steel & Webster, 1992). This study contributes to this evidence base by utilizing data from the 2019/20 National Sample Census of Agriculture (NSCA), which covers over 5,000 rice-growing households across all major regions. Using robust analytical approaches, we isolate the effect of improved seeds on rice productivity while accounting for other confounding variables.

This research has broader relevance beyond academic inquiry. It contributes directly to the realization of several United Nations Sustainable Development Goals (SDGs). By quantifying the role of improved seeds in enhancing rice productivity, this study informs interventions aligned with SDG 1 (No Poverty), SDG 2 (Zero Hunger), SDG 3 (Good Health and Well-being), and SDG 8 (Decent Work and Economic Growth). Higher productivity can lead to increased incomes, better nutrition, and greater resilience among rural households. Moreover, by improving land-use efficiency and reducing the need to expand cultivation into forested areas, improved seeds contribute to SDG 12 (Responsible Consumption and Production), SDG 13 (Climate Action), and SDG 15 (Life on Land) (Pretty, 2018; Pretty et al., 2012; 2018; Thomson et al., 2019; van Ittersum et al., 2013).

Furthermore, this study serves as a baseline for future evaluations of rice sector transformation in Tanzania and similar contexts. It provides timely evidence for policymakers, development partners, and seed companies seeking to promote climate-resilient, high-yielding varieties that can sustain agricultural growth in the face of climate uncertainty. As the government and its partners implement the Agricultural Sector Transformation Agenda and the CAADP Malabo Declaration, reliable data on input effectiveness will be vital for targeting interventions and maximizing impact (African Union, 2018; Ubah & Nagabhatla, 2025; Mdleleni et al., 2018). The study provides new insights into the yield-enhancing potential of improved rice seeds in Tanzania. By quantifying yield differences between users and non-users across agroecological zones and farming systems, it contributes to both policy and academic debates on sustainable intensification. Ultimately, it offers a data-driven foundation for designing inclusive and climate-smart seed systems that enhance productivity, equity, and environmental sustainability.

2.1. Study Area

This study examines rice-producing farms across Mainland Tanzania and Zanzibar, focusing on the impact of seed type, specifically, improved seeds versus local seeds, on rice productivity, while controlling for other factors. To account for geographical and ecological variability, rice farms in Mainland Tanzania were further categorized into Agroecological Zones (AEZs). These zones exhibit distinct climatic conditions, soil characteristics, and farming practices, all of which significantly influence agricultural outcomes. By analyzing rice productivity within these AEZs, the study offers a detailed understanding of how improved seeds perform under different ecological contexts. This zonal classification enhances the robustness of the findings by controlling for environmental differences, allowing for a more accurate assessment of the benefits of improved seeds. It also increases the applicability of the results, ensuring they are relevant across Tanzania’s varied agricultural regions. Segmenting the analysis by AEZ enables a comparative evaluation of improved seed effectiveness under different environmental settings, which is essential for developing targeted recommendations that optimize seed selection and boost productivity.

Furthermore, insights into how rice yields respond to improved seeds compared to local varieties within each AEZ can guide the design of region-specific, climate-resilient agricultural strategies. These findings are vital for informing policies and interventions that aim to enhance rice production, improve food security, and support sustainable agricultural development in Tanzania. Figure 1 presents a map of Tanzania, indicating regional rice production volumes (in metric tons) for the 2019/20 growing season, as reported by URT (2021a).

The first grouping involved categorizing the farms into two main groups (Mainland Tanzania and Zanzibar). Farms in Mainland Tanzania were further grouped into eight (8) zones to include:

Central AEZ (CAZ): Encompassing the regions of Dodoma and Singida.

Eastern AEZ (EAZ): Comprising the Morogoro region

Coastal AEZ (CSZ): Comprising Dar es Salaam, Pwani (Coastal Region), Tanga,

Southern Zone (SAZ): Includes Lindi and Mtwara regions.

Northern Highlands Zone (NHZ): Covering the regions of Arusha, Kilimanjaro, and Manyara.

Southern Highlands Zone (SHZ): Including Iringa, Mbeya, Songwe, Njombe, and Ruvuma regions.

Lake Zone (LAZ): Covering Geita, Shinyanga, Simiyu, Mara, Mwanza, and Kagera regions.

Western Zone (WAZ): Encompassing Katavi, Kigoma, Tabora, and Rukwa regions.

2.2. Data Sources

The data utilized in this study come from the 2019/20 National Sample Census of Agriculture (NSCA), supplemented by the 2007/08 NSCA data to normalize and adjust the recent figures. The NSCA, administered by Tanzania’s National Bureau of Statistics (NBS), is a vital information repository that captures essential details of agricultural households, including farm size, crop production, livestock holdings, and the use of inputs like fertilizers and seeds. Furthermore, the census assesses rural infrastructure and the living standards of farming households, providing crucial benchmarks for measuring agricultural productivity and the effectiveness of various interventions. Additionally, the census examines rural infrastructure improvements and the living standards of farming households, offering valuable benchmarks for evaluating agricultural productivity and the impact of agricultural interventions implemented by various stakeholders, including Agricultural Sector Lead Ministries (ASLMs).

To ensure nationwide representativeness, the NSCA employed a two-stage sampling methodology. In the first stage, Census Enumeration Areas (CEAs) from the 2012 Population and Housing Census were systematically selected as Primary Sampling Units (PSUs). These PSUs were proportionately distributed across regions and districts to guarantee geographical balance. In the second stage, agricultural households within the selected PSUs were randomly sampled, with a focus on households actively engaged in crop cultivation and livestock production. The sampling process covered a total of 2,820 PSUs, comprising 2,670 PSUs from Mainland Tanzania and 150 PSUs from Tanzania Zanzibar. However, this study exclusively analyzes data from Mainland Tanzania, aligning with its research objectives. The sampling strategy employed a Probability Proportional to Size (PPS) technique, which prioritizes areas with higher agricultural activity, thereby enhancing the reliability and accuracy of the data. Details of the sampling procedure, including design and implementation, can be found in the official NSCA report (URT, 2021a).

2.3. Data Processing and Simulation

Rice production data were sorted from the NSCA dataset for simulation. The sorted data were categorized into three fertilizer-use categories: non-fertilizer users (F0), organic fertilizer users (F1), and inorganic fertilizer users (F2). Table 1 summarizes the observed data, sorted by fertilizer-use groups, regions (mainland Tanzania and/or Zanzibar), and across agroecological zones as described by Kadigi et al. (2025a, 2025b).

Table 1
Number of observations for rice farming practices under two seed type regimes per agroecological zone (AEZ)
Agroecological Zone (AEZ)	Rice Seed Category Uses Tanzania
Agroecological Zone (AEZ)	Local Seed Users (S1)	Improved Seed Users (S2)	Proportion (%) of improved seed users
Central AEZ (CAZ)	121	5	4.0
Eastern AEZ (EZ)	775	41	5.0
Coastal AEZ (CSZ)	129	7	5.1
Northern AEZ (NAZ)	29	15	34.1
Lake AEZ (LAZ)	1,413	43	3.0
Southern Highlands AEZ (SHZ)	902	93	9.3
Southern AEZ (SAZ)	287	17	5.6
Western AEZ (WAZ)	1,089	20	1.8
Mainland Tanzania (MTZ)	4,745	241	4.8
Zanzibar (ZNZ)	390	164	29.6
All Farms in Tanzania (ALL.TZ)	5,135	405	7.3

A Probabilistic Stochastic Simulation Approach (PSSA) that follows non-parametric Monte Carlo simulation protocols, as outlined by Kadigi et al. (2020) and Richardson et al. (2007, 2008). PSSA is a computational technique that uses random sampling to model and analyze systems with inherent randomness or uncertainty. It involves generating multiple scenarios based on probability distributions and then analyzing the outcomes to understand the system's behavior and potential risks (Madsen et al., 2000; Bishwal, 2008). Monte Carlo simulation is widely used for modeling stochastic processes, enabling the estimation of yield distributions for different farming systems under varying agroecological conditions. This method allows for the robust quantification of risks and variability associated with crop productivity in Tanzania.

The first stage of the PSSA simulation involved defining and parameterizing the stochastic (risky) yield for each farming system based on the observed data. To convert the rice yields into a stochastic format to simulate and validate the yield distributions for each farming system. Given the study's scope, the data were further disaggregated by agroecological zones to account for regional variations, resulting in a total of 30 random models, as shown in Table 1. Since we have 30 random yields from different zones and three fertilizer-use farming practices, the study adopted a Multivariate Empirical (MVE) distribution, as described by Richardson et al. (2000), to incorporate multiple variables simultaneously. The MVE approach is particularly effective for this analysis because it accommodates multiple variables while ensuring that simulated values remain within logical boundaries, such as eliminating unrealistic negative yields. Residuals, defined as percentage deviations of observed yields from their mean, were used to estimate the parameters for the MVE yield distribution. This technique captures the variability in historical yield data and reflects the probabilistic nature of rice productivity across different farming setups.

The following equation was used to develop the simulation model:

$$\:{\stackrel{\sim}{y}}_{i,\omega\:}={\stackrel{̄}{y}}_{c,i,\omega\:}*\left(1+EMP\left({S}_{y,i,\omega\:},P\left({S}_{y,i,\omega\:}\right),CUS{D}_{y,i,\omega\:}\right)\right)*{\beta\:}_{{\stackrel{\sim}{y}}_{0}}$$

Where:

~	=	A tilde represents a stochastic variable.
i	=	Type of farming practices used (no-fertilizer (F0), organic (F1), and inorganic fertilizer (F2)
$\:\omega\:$	=	Represents farms in Mainland Tanzania, Zanzibar, and other main producing AEZs
a_i	=	Hectares (ha) allocated for farming practice i
	=	Stochastic mean yield per ha for farming practice i
$\:{\stackrel{-}{y}}_{i}$	=	Deterministic (mean) yield per ha for farming practice i
$\:{\beta\:}_{{\stackrel{\sim}{y}}_{0}}$	=	The normalization factor which is given by $\:\frac{{\stackrel{\sim}{y}}_{c,i}}{{\stackrel{\sim}{y}}_{h,i}}\:$and it is used to scale/adjust the 2019/20 NCSA mean yield.
$\:{\stackrel{\sim}{y}}_{c}$	=	Stochastic yield for the current survey (2019/20 NCSA)
$\:{\stackrel{\sim}{y}}_{h}$	=	Stochastic yield for the historical or previous survey (2007/08 NCSA)
S_y	=	Fraction deviations from the mean or sorted array of random yields for farming practice i
P(S_y)	=	Cumulative probability function for the S_y values
CUSD_y	=	Simetar function to simulate the correlated uniform standard deviation of random variables.
EMP()	=	Simetar function used to simulate a stochastic variable (yield)

The next step in the analysis involved simulating the stochastic model presented in Eq. 1 for each farming setup using the Latin Hypercube Sampling technique, with at least 500 iterations, as described by Richardson et al. (2007) and Kadigi et al. (2025a, 2025b). The Latin Hypercube Sampling method was chosen for its efficiency, as it ensures that a relatively small sample size (500 iterations) is sufficient to reproduce the characteristics of the parent yield distributions accurately. Given that the study considers 30 random variables, each with unique observations (Table 1), a simulation of 500 iterations was conducted for each random variable. This approach generated a total of 15,000 simulated yields (500 iterations × 30 variables), providing a reasonable sample for comparative analysis. The larger simulated sample enhances the robustness of the analysis and facilitates a more precise evaluation of the impact of various farming practices on rice yields.

In the final step, the simulated yield distributions were compared against the historical yield distributions to ensure validity and accuracy. This validation process confirms that the stochastic model reliably reflects the observed data while capturing the variability introduced through the Monte Carlo simulation. By aligning the simulated results with the actual historical distributions, the analysis ensures that the findings are both representative and suitable for assessing the performance of various farming systems across Tanzania's agroecological zones. The validation results are presented in Fig. 2 using probability distribution functions (PDF).

2.4 Ranking of Target Probabilities Using the Stoplight Function

The Stoplight Chart function was used to rank the probabilities of rice farms achieving the maximum yield thresholds and the probabilities of falling below the minimum values per unit area (which is 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 National Sample Census of Agriculture (NSCA) URT (2021a) revealed that the average yield in Tanzania ranges between 1–3.3 t/ha, hence, we set our minimum threshold to be 1.5 t/ha and the maximum being 3.0 t/ha. However, since the aim of most national initiatives, such as the ASDP-II (URT, 2016) and the Tanzania Seed Sector Development Strategy – 2030 (TSSDS 2030) (Minde et al., 2024; Rweyemamu 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 and a maximum value of 4.5 t/ha, aligning with the global standard of 3.5–4.66 t/ha (Bin Rahman & Zhang, 2023).

3.1. Model Validation

Figure 3 presents a comparison between the observed and simulated yield distributions for rice farms using local seeds (_S1) and improved seeds (_S2) across Zanzibar and Mainland Tanzania. This serves as a model validation step to assess how accurately the stochastic simulation replicates real-world yield outcomes, ensuring the reliability of the simulation results used in subsequent analyses. In Panel (a), the observed and simulated distributions for farmers in Zanzibar using local seeds show a strong agreement. Both distributions have a mean yield of 1.88 tons per hectare, and their standard deviations are nearly identical, at 0.97 for the observed and 0.99 for the simulated. The coefficient of variation (CV) is also very close (51.83% for observed vs. 52.55% for simulated), indicating that the model captures not only the central tendency but also the variability of the yields. Furthermore, the minimum, median, and maximum values are aligned, suggesting that the model reliably simulates the yield outcomes for this subgroup.

In Panel (b), the comparison for farmers in Mainland Tanzania using local seeds shows similarly high consistency. Both observed and simulated data report a mean yield of 2.22 tons per hectare with a standard deviation of 1.16. The CVs are nearly the same (52.28% for observed vs. 52.31% for simulated), and the shapes of the distributions are visually overlapping. This close agreement affirms the model's ability to accurately simulate rice yield outcomes for local seed users on the Mainland. In Panel (c), for Zanzibar farmers using improved seeds, the model continues to perform well. The mean yield for both observed and simulated data is 2.27 tons per hectare, with a standard deviation of 1.38 for both. The CVs (60.85% observed vs. 60.72% simulated) further indicate minimal deviation between the two distributions. This suggests that the model is appropriately calibrated to reflect the performance of improved seed technologies in Zanzibar’s rice farming systems.

The comparison for Mainland (Panel d) farmers using improved seeds shows an even stronger alignment. Both observed and simulated distributions have a mean yield of 2.77 tons per hectare, with identical standard deviations of 1.39. The CVs are also nearly the same (50.14% vs. 50.12%), and the shapes of the distributions reinforce that the model accurately replicates the actual variability and range of yields. Thus, the observed and simulated yield distributions across all four farmer groups show close agreement in terms of mean, standard deviation, coefficient of variation, and distribution shape. This consistency confirms that the stochastic simulation model is valid and robust. It effectively captures the behavior of rice yields under different seed types and regional contexts, lending strong credibility to the simulation results and the policy implications drawn from them.

3.2. Overview of Improved Seeds Use in Tanzania

The figure presents a comparative overview of the use of improved seed versus local seed among rice farmers in Tanzania, highlighting differences between farmers on the mainland and those on Zanzibar. Nationally, the adoption of improved rice seeds remains very low. Only 7.3% of all rice farmers across the country use improved seed varieties, while the significant majority, at 92.7%, continue to rely on local seed varieties. This suggests a considerable gap in the adoption of modern technologies despite national efforts to promote agricultural transformation. In mainland Tanzania, the situation is even more concerning. Merely 4.8% of farmers use improved rice seeds, while 95.2% rely on traditional local seeds. This suggests that most mainland rice farmers continue to be excluded from the potential benefits associated with improved varieties, such as higher yields and greater resilience to climate-related stresses. The low adoption could be attributed to a range of factors, including limited access to certified seed, weak extension services, cost barriers, or limited awareness of the benefits of improved seeds under rainfed conditions.

In contrast, Zanzibar exhibits relatively higher adoption levels. Approximately 29.6% of rice farmers in Zanzibar have adopted improved seed varieties, while 70.4% continue to use local ones. Although the uptake is still below desirable levels, Zanzibar shows better progress compared to the mainland, possibly due to more localized seed distribution systems, targeted public investments, or stronger institutional support. In summary, the figure highlights a critical policy and institutional gap in promoting the improved use of seeds across Tanzania. The stark disparity between Zanzibar and the mainland highlights the need for targeted interventions that enhance both access and farmer confidence in improved seed technologies. For meaningful productivity growth and improved food security outcomes, urgent efforts are needed to strengthen seed systems, enhance farmer awareness, and address affordability and availability challenges across all regions.

3.1. Impact of Fertilizer Application on Rice Productivity Across All Farms in Tanzania

3.1.1 Scenario A: (lower thresholds: Max 3.0 t/ha and Min 1.5 t/ha)

Figure 5 presents the probabilistic distribution of rice productivity outcomes for farms using local seeds (_S1) and improved seeds (_S2) in Tanzania, disaggregated by national-level aggregates (ALL.TZFarms), Mainland Tanzania (MTZFarms), and Zanzibar (ZNZFarms). The analysis is based on two productivity thresholds (Scenario A): 1.5 t/ha (lower bound) and 3.0 t/ha (upper bound). The probabilities are categorized into three bands: yields above 3.0 t/ha (exceeding the national average yield, green), below 1.5 t/ha (indicating low productivity, red or risk threshold), and between 1.5 and 3.0 t/ha (representing moderate productivity, yellow).

At the national level, farms using improved seeds (ALL.TZFarms_S2) show a clear advantage over those using local seeds (ALL.TZFarms_S1). Specifically, 29% of improved seed users exceeded the 3.0 t/ha threshold, compared to only 20% among local seed users, representing a 45% increase in the likelihood of achieving high yields. Additionally, the share of farms falling below the 1.5 t/ha threshold is lower among improved seed users (30%) than local seed users (38%), indicating a reduction in downside productivity risk. The majority of farms, in both seed categories, fall within the moderate productivity range (1.5–3.0 t/ha), but the upward shift in the distribution for improved seed users reflects their relative advantage.

In Mainland Tanzania, the productivity gains associated with improved seeds are even more pronounced. About 36% of MTZFarms_S2 users achieved yields above 3.0 t/ha, compared to only 21% of MTZFarms_S1 users. This represents a 71% increase in the probability of achieving high yields among adopters of improved seeds. Moreover, only 23% of improved seed users fell below the 1.5 t/ha threshold, compared to 36% of local seed users, again indicating a significant reduction in low-yield risk. These findings underscore the effectiveness of improved seeds in boosting rice productivity and stabilizing output performance across diverse farming systems in the Mainland.

In Zanzibar, although rice productivity levels are generally lower compared to the Mainland, improved seed users still demonstrate superior outcomes relative to local seed users. Only 19% of ZNZFarms_S2 users exceeded 3.0 t/ha, compared to just 11% of ZNZFarms_S1 users, an improvement of 73%. Furthermore, the proportion of farms producing below 1.5 t/ha declined from 52% among local seed users to 41% among those using improved seed. While the probabilities of moderate yields (1.5–3.0 t/ha) remained relatively similar between the two groups, the gains observed in both the upper and lower yield thresholds suggest that improved seeds play a critical role in enhancing rice productivity in Zanzibar’s constrained production environment.

Overall, the results from Fig. 4 provide compelling evidence that the adoption of improved rice seeds substantially shifts the productivity distribution towards higher yield categories while reducing the risk of extremely low yields. These effects are evident across both the Mainland and Zanzibar regions, though the magnitude of improvement is more significant in the Mainland. This pattern confirms the yield-enhancing potential of improved seeds in Tanzania’s rice sector and supports broader policy efforts aimed at accelerating their dissemination and adoption nationwide.

3.1.2 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). Figure 6 highlights how productivity patterns shift across seed types and geographic zones when moving from standard yield thresholds (1.5 and 3.0 t/ha) to higher thresholds (2.0 and 4.5 t/ha). 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 seed type regimes. An adjusted minimum cut-off value of less than 2.0 t/ha indicates poor productivity. The figure shows a more stringent assessment of rice productivity performance by applying higher cut-off values. The results clearly demonstrate that the probability of achieving high yields (≥ 4.5 t/ha) significantly declines for all categories. However, improved seed users continue to outperform local seed users across all zones.

A comparative analysis of Figs. 5 and 6 reveals important insights into how rice productivity distributions shift under different threshold scenarios and seed types across Tanzania. At the standard yield thresholds (1.5 t/ha and 3.0 t/ha) presented in Fig. 5, improved seed users already demonstrated a clear productivity advantage over local seed users. This advantage was evident in both reducing the proportion of low-yield farms (below 1.5 t/ha) and increasing the share of farms surpassing the 3.0 t/ha benchmark or lying between the thresholds. For example, at the national level, the probability of achieving more than 3.0 t/ha was 29% for improved seed users compared to only 20% for local seed users, while the share of farms producing below 1.5 t/ha dropped from 38% (local) to 30% (improved).

However, when higher thresholds were applied in Fig. 6, specifically 2.0 t/ha as the lower bound and 4.5 t/ha as the upper cut-off, the differences between the two seed types became even more pronounced. Under this stricter assessment, the proportion of farms exceeding the 4.5 t/ha benchmark was considerably lower overall, but improved seed users still consistently outperformed local seed users across all regions. At the national level, the share of farms surpassing 4.5 t/ha doubled from 6% for local seed users to 12% for improved seed users. Meanwhile, the proportion of farms falling below 2.0 t/ha declined from 61–50%, indicating a marked reduction in downside risk due to improved seed use.

Again, the advantage of improved seeds was particularly prominent in Mainland Tanzania. In Fig. 4, improved seed users in the Mainland had a 36% chance of exceeding 3.0 t/ha compared to 21% for local seed users. In Fig. 6, despite the elevated threshold, 15% of improved seed users still managed to exceed 4.5 t/ha, more than double the 6% observed among local seed users. Additionally, the share of low-yielding farms (< 2.0 t/ha) declined sharply from 60% (local) to 41% (improved), reaffirming the effectiveness of improved seeds in shifting productivity outcomes upward, even under more demanding performance benchmarks.

In Zanzibar, productivity levels were generally lower under both threshold scenarios, but the relative benefit of improved seeds remained evident. Under standard thresholds in Fig. 5, only 11% of local seed users exceeded 3.0 t/ha compared to 19% of improved seed users. This pattern persisted in Fig. 6, where only 2% of local seed users surpassed 4.5 t/ha, while improved seed users achieved a 7% probability, more than three times higher. Furthermore, the proportion of extremely low-yielding farms (below 2.0 t/ha) declined from 79% among local seed users to 64% among improved seed users, highlighting the resilience advantages of improved varieties even in more constrained agroecological zones.

Overall, the comparison between Figs. 5 and 6 confirms that while higher thresholds reduce the absolute probabilities of achieving top-end yields, improved seed users maintain a consistent advantage in both productivity and risk reduction across all regions. These findings reinforce the strategic importance of improved seed adoption not only for raising average yields but also for enabling a greater proportion of farms to achieve productivity levels that approach or exceed commercial viability and national food security goals.

3.2 Impacts of Improved Seeds Over Local Seeds on Rice Productivity Across Agroecological Zones (AEZ) under Scenario A

Figures 7 and 8 illustrate the distribution of rice productivity for farms using local and improved seeds across Tanzania’s AEZs, respectively, under scenario A. The results indicate significant spatial variability in yield performance under the two scenarios. For example, in Fig. 7, the Northern Highlands Zone (NHZ) stands out as the best-performing zone among local seed users, with 50% of farms producing more than 3.0 t/ha and only 4% falling below the 1.5 t/ha threshold. This suggests that favorable biophysical conditions or better agronomic practices in the NHZ may partly offset the limitations of local seed varieties. The Central AEZ (CAZ), the Southern Highlands Zone (SHZ), and the Lake AEZ (LAZ) also show relatively good performance, with 32% and 34%, 22% of farms, respectively, surpassing 3.0 t/ha. However, both zones still have moderate shares of farms in the low-productivity category, 25% in CAZ, 28% in SHZ, and 27% in LAZ, reflecting some level of production risk among local seed users.

On the other hand, the Western (WAZ) and Southern AEZs (SAZ) exhibit the weakest productivity outcomes for local seed users. In WAZ, more than half (56%) of the rice farms fall below the 1.5 t/ha mark, and only 18% manage to exceed 3.0 t/ha, signaling widespread productivity constraints. A similar pattern is observed in SAZ, where 54% of farms fall in the lowest yield category and only 2% exceed the 3.0 t/ha benchmark, effectively rendering the zone highly vulnerable to food insecurity and economic inefficiency in rice farming. The Coastal (CSZ) and Eastern AEZs (EAZ) demonstrate mixed outcomes. While 11–14% of farms in these regions exceed 3.0 t/ha, the red bars show that 32–44% of farms still fall below 1.5 t/ha. These results collectively highlight the inadequacy of local seeds in delivering consistent yields across most AEZs, and particularly the heightened risks faced by farmers in low-performing zones.

Figure 8 presents the distribution of rice productivity for farms using improved seeds across the same AEZs. A clear upward shift in productivity is observed when compared to local seed users. In the NAZ, the probability of achieving more than 3.0 t/ha increases slightly from 50–55%, while the proportion of farms below 1.5 t/ha drops to 0%, a remarkable outcome indicating that improved seeds nearly eliminate the risk of very low productivity in this region. Similarly, in the SHZ, improved seed use raises the share of high-performing farms from 34–52% and reduces the risk of low productivity from 28–15%. These zones demonstrate the full potential of improved seeds in achieving both yield enhancement and risk mitigation, likely due to complementary factors such as improved access to inputs, extension support, or agroecological advantages.

In low-performing zones, the introduction of improved seeds also leads to measurable gains, although to a lesser extent. In SAZ, the share of farms producing more than 3.0 t/ha increases from a mere 2–20%, while the proportion of farms below 1.5 t/ha decreases slightly from 54–35%. In the WAZ, although there is a noticeable decrease in high-yielding farms from 18–12%, the proportion of low-yield farms decreases significantly from 56–39%. These improvements, though still limited in magnitude, demonstrate the value of improved seed adoption in challenging environments, where other complementary interventions, such as irrigation or soil fertility management, may be required to maximize impact. In the CAZ and CSZ zones, improved seed users report 35–39% probabilities of achieving yields greater than 3.0 t/ha, with modest changes in the proportion of farms yielding less than 1.5 t/ha (28–32%). The improved seeds application in the EAZ exhibits a decline in low-yield risk from 32–21%, accompanied by an increase in high-yield probability from 14–27%.

Comparatively, the results in Figs. 7 and 8 reveal that improved seeds significantly enhance rice productivity across all AEZs in Tanzania. The use of improved seeds consistently reduces the probability of yields falling below 1.5 t/ha while simultaneously increasing the probability of exceeding 3.0 t/ha. These effects are particularly pronounced in the Northern AEZ, where the proportion of high-yielding farms increased from 50% (using local seeds) to 55% (using improved seeds), and the risk of low yields decreased from 4–0%. Similarly, in the Southern Highlands, high-yield probabilities rose from 34–52%, while low-yield risks halved. Even in marginal zones such as SAZ and WAZ, improved seeds led to measurable gains, demonstrating their potential to elevate productivity even under suboptimal conditions. Overall, the spatial distribution of these gains highlights the importance of zone-specific seed targeting strategies while affirming the general effectiveness of improved seeds as a crucial input for sustainable rice intensification.

3.3 Impacts of Improved Seeds Over Local Seeds on Rice Productivity Across Agroecological Zones (AEZ) under Scenario B

Figures 9 and 10 explore the productivity probabilities of rice farms using local and improved seeds across Tanzania’s agroecological zones (AEZs) under scenario threshold criteria. 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 92% of rice farms produce less than 2.0 t/ha for local seed users, while only 1% manage to exceed the 4.5 t/ha mark. 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 WAZ and CSZ reports indicate that 73% and 75% of farms fall into the low-yield bracket (2.0 t/ha), with only 6% and 5% achieving yields exceeding 4.5 t/ha, respectively, a clear indication of climatic and soil limitations echoing the trends observed in other constrained AEZs.

Of all zones, the NZ is the first zone with the highest probability (22%) of exceeding 4.5 t/ha, followed by the SHZ (14%). NZ has 55% of farms in the 2.0–4.5 t/ha middle range, with the smallest probability (22%) of underperforming, confirming room for improvement but relative stability in yields. Despite having the second highest probability of surpassing the global maximum threshold, SHZ still has a greater than 50% probability of yield falling below the minimum cut-off for local seed users. Notably, LZ shows promising results, with 6% of farms reaching the higher productivity threshold. However, more than half of its farms (50%) still yield below 2.0 t/ha. This suggests that even in zones with stronger biophysical characteristics, local seed users are at risk of underperformance without external support for improved agronomic practices. The CZ also demonstrates moderate outcomes, with 10% of farms achieving above 4.5 t/ha and 45% falling short of 2.0 t/ha. While not as critical as CSZ or SZ, the CZ still showcases vulnerability in the absence of improved seeds. These results show that when higher yield benchmarks are applied, the incidence of low-productivity farms sharply increases across all AEZs for local seed users, with limited capacity to attain high yields (≥ 4.5 t/ha).

Figure 10 shifts the focus to evaluate the performance of rice farms using improved seeds (_S2) across Tanzania's AEZs under higher productivity thresholds. The results highlight stark differences in how well improved seeds enable farms to reach global productivity standards in different zones. Notably, improved seed users in the CSZ and CAZ have higher chances of surpassing the global high-yield benchmark of 4.5 t/ha, with probabilities of 26% and 16%, respectively. The SHZ also performs well, with 25% of farmers expected to exceed this threshold. In contrast, farmers in the EAZ and WAZ exhibit the lowest probabilities of achieving high productivity, at less than 10% each. Furthermore, the risk of low yields (< 2.0 t/ha) is significantly reduced among improved seed users in regions such as the NAZ and SHZ, where only 10% and 25% of farmers, respectively, fall below this critical threshold. This suggests that improved seed technology performs strongly under favorable agronomic and climatic conditions. However, high vulnerability persists in the WAZ, SAZ, and LAZ, where 79%, 68% and 55% of improved seed users, respectively, are still likely to fall below the 2.0 t/ha productivity cut-off, pointing to systemic constraints beyond seed quality, such as soil degradation, limited irrigation, or inadequate extension services.

Across most AEZs, the majority of improved seed users fall within the moderate productivity range (2.0–4.5 t/ha), especially in the Eastern AEZ (52%), SHZ (49%), and NAZ (80%). This suggests a considerable potential for these farmers to transition into the high-productivity category with modest complementary interventions (e.g., better water access or fertilizer application). The Central and Coastal zones also exhibit a healthy distribution in this range, indicating that improved seeds alone can significantly mitigate yield failure while providing a stepping stone toward high-yield production. The results further suggest that agroecological targeting and tailored support systems are crucial for realizing the full potential of improved seed technologies. Even in areas with low high-yield probabilities, the reduced risk of falling into the low-yield band underscores the risk-mitigating effect of improved seeds. Such outcomes justify policies that promote improved seed adoption not only as a means to boost average productivity but also to stabilize rice production and reduce vulnerability, especially in the context of climate variability.

When compared with Fig. 9, which shows the yield probabilities for local seed users, the superiority of improved seeds becomes clear. For example, in the SHZ, 25% of improved seed users exceed 4.5 t/ha compared to only 11% of local seed users. In CAZ and CSZ, improved seeds result in higher top-tier yield probabilities and significantly lower chances of falling below the 2.0 t/ha threshold. In SAZ, the risk of poor yields reduces from 92% (local) to 68% (improved). Similarly, in CSZ, the risk of poor yield reduced from 75–50%. Except for one AEZ (WAZ), which reported a relatively lower probability (73%) of poor yield risk for local seed compared to improved seed (79%), the rest of the zones demonstrated improvements with improved seed. While the improvement is modest in some low-potential zones, the consistent trend of performance uplift and reduced risk of crop failure among improved seed users is evident across all zones. This comparison strongly reinforces the argument for scaling up improved seed use while addressing the systemic constraints that limit their effectiveness in certain AEZs.

This study provides robust empirical evidence on the impact of improved seed varieties on rice productivity in Tanzania, utilizing nationally representative data from the 2019/20 National Sample Census of Agriculture (NSCA) and complemented by data from the 2007/08 NSCA. The findings confirm that improved seeds significantly outperform local seeds in terms of yield outcomes, reducing downside risk, and increasing the likelihood of achieving both standard and high productivity benchmarks. These results are particularly relevant to ongoing policy efforts aimed at transforming Tanzania’s agricultural sector and addressing national goals related to food security and poverty reduction.

One of the most consistent patterns observed is the positive productivity differential in favor of users who have improved seeds. At standard yield thresholds (1.5–3.0 t/ha), improved seeds are associated with a 45% higher probability of exceeding 3.0 t/ha at the national level, and a reduction in the proportion of farms falling below 1.5 t/ha by up to 21 percentage points in some zones. These results align with previous findings in Sub-Saharan Africa, and other developing countries, where the adoption of improved seeds, especially when combined with good agronomic practices, has led to yield increases of 20–50% (Sheahan et al., 2014; Sheahan & Barrett, 2017; Ariga et al., 2019; Nin-Pratt & McBride, 2014; Garbero et al., 2018; Walker & Alwang, 2015; Maredia et al., 2000; Mottaleb et al., 2016; Aryal et al., 2019; Jena & Tanti, 2023). The risk-reducing effect of improved varieties is also supported by studies emphasizing their tolerance to biotic and abiotic stresses, including drought and pest resistance (Tripp & Rohrbach, 2001; Tripp, 2006; Matlon & Minot, 2007; GAFSP, 2021; Khan et al., 2022; Feed et al., 2020; Fan et al., 2021; Komen & Wafula, 2021).

The application of higher yield thresholds (2.0–4.5 t/ha) further sharpens the distinction between seed types. While fewer farms attain yields above 4.5 t/ha, those using improved seeds consistently show higher probabilities of doing so. For example, in the Southern Highlands, 25% of improved seed users surpassed this benchmark, compared to just 14% among local seed users. The improved seed effect is even more visible in the Northern AEZ, where the high-yield probability increases from 22% (local) to 25% (improved), and the proportion of low-yielding farms (below 2.0 t/ha) declines from 22–10%. These patterns underscore the role of improved seeds in facilitating productivity breakthroughs, particularly in agroecological zones with higher potential (van Ittersum et al., 2013; van Ittersum & Cassman, 2013; Anderson et al., 2016; Gobbett et al., 2017; Pretty, 2018; Pretty et al., 2012; 2018; Thomson et al., 2019).

Nonetheless, the study also reveals substantial inter-zone disparities in the effectiveness of improved seeds. While zones such as the Northern AEZ (NAZ) and the Southern Highlands (SHZ) demonstrate substantial gains, marginal zones like the Southern AEZ (SAZ) and Western AEZ (WAZ) exhibit only modest improvements, even with improved seeds. In SAZ, the share of farms producing below 2.0 t/ha remains high at 68%, with only 11% exceeding 4.5 t/ha. This finding suggests that while genetic potential is a necessary condition for yield gains, it is not sufficient in isolation; improved seeds require complementary inputs, such as fertilizer, irrigation, and extension services, to reach their full potential (Chakoma & Chummun, 2019; Mercer, 2011; Kifle & Atilaw, 2018; Shimeles et al., 2018; Post et al., 2021; Dias et al., 2021; Jararweh et al., 2023).

Moreover, infrastructural and institutional factors may explain the uneven uptake and performance of improved seed technologies. Studies have shown that seed system inefficiencies, including limited seed multiplication, weak distribution networks, and high transaction costs, constrain access to and adoption of seeds, particularly in remote and resource-poor areas (Mabaya et al., 2018, 2021a, 2021b, 2021c; Spielman, 2006; Spielman et al., 2009; World Bank, 2007). Furthermore, knowledge gaps about seed characteristics and a lack of training in optimal agronomic practices often reduce the yield gains realized by farmers (Wossen et al., 2017; Farauta et al., 2011a; 2011b; Ojo & Baiyegunhi, 2020; Haider, 2019; Abaje et al., 2016). The variability observed in this study suggests that targeted support systems, region-specific extension services, access to finance, and localized demonstrations are crucial to translating improved seed potential into consistent productivity gains.

The implications of these findings are far-reaching. First, the results support Tanzania’s strategic policy initiatives such as the National Rice Development Strategy II (NRDS-II) and the Agricultural Sector Development Programme II (ASDP-II), both of which emphasize increased productivity through technology adoption. Improved seeds should be central to input subsidy programs, seed certification reforms, and public-private partnerships that aim to scale access and improve seed quality. Second, the observed heterogeneity in seed performance suggests that policies must be context-sensitive. Blanket solutions may not yield optimal results; interventions should be tailored to agroecological and socioeconomic realities (URT, 2021b; 2023; URT, 2019).

Lastly, from a global development perspective, this study contributes evidence to the literature on agricultural intensification as a pathway to achieve multiple Sustainable Development Goals. Improved seed adoption can simultaneously promote SDG 1 (No Poverty), SDG 2 (Zero Hunger), and SDG 8 (Decent Work and Economic Growth) by raising productivity and household incomes. It also contributes to SDG 13 (Climate Action) by increasing resilience to climate-induced stressors and enabling farmers to utilize natural resources more effectively (FAO et al., 2023; Jiang et al., 2021; Balistreri et al., 2022; Workie et al., 2020).

This study provides compelling evidence on the effectiveness of improved rice seed varieties in enhancing farm-level productivity across diverse agroecological zones in Tanzania. Utilizing stochastic analysis on nationally representative data, the results confirm that improved seed users are more likely to surpass both standard and high-yield thresholds while facing significantly lower downside risks than users of local seeds. The productivity gains are particularly notable in high-potential zones such as the Northern AEZ and Southern Highlands, but are also observable in marginal zones, albeit at reduced magnitudes.

From a policy standpoint, these findings offer a timely contribution to Tanzania’s agricultural transformation agenda. The government’s goal to double productivity for major food security crops such as rice is contingent upon widespread access to and adoption of productivity-enhancing technologies, foremost among them, improved seeds. These results justify renewed investments in strengthening the national seed system, including seed breeding, multiplication, and last-mile delivery. Moreover, the results underscore the importance of integrating improved seeds into comprehensive support packages that encompass fertilizer subsidies, mechanization services, and climate-smart extension delivery.

To maximize the impact of improved seeds, differentiated strategies are needed. In high-potential zones, policies should focus on scaling improved seeds with complementary inputs to accelerate commercialization and surplus production. In marginal or low-performing zones, efforts should prioritize adaptive trials, farmer training, and risk-mitigating innovations, such as stress-tolerant varieties, irrigation, and soil health interventions. This zonal targeting approach aligns with the principles of precision agriculture and ensures the efficient allocation of public resources.

For future research, it is necessary to investigate the long-term yield stability and economic returns of improved seeds under climate variability, particularly in regions vulnerable to drought or flooding. Additionally, investigating farmer behavior, including willingness to pay, varietal preferences, and learning dynamics, can offer insights into how to enhance adoption rates. Gender-disaggregated analysis would also be valuable, considering evidence that female farmers often face additional barriers to accessing certified inputs.

The significance of this study lies not only in its methodological robustness and national scope but also in its direct alignment with Tanzania’s development aspirations. By demonstrating the potential of improved seeds to raise yields, mitigate risks, and reduce poverty, this study provides the empirical foundation for implementing the National Agricultural Policy (2013), NRDS-II, and Vision 2025 targets. It also supports Tanzania’s commitments to continental and global frameworks such as CAADP and the SDGs. In particular, the study advances SDG 1 (No Poverty) by improving the incomes of smallholders, SDG 2 (Zero Hunger) by enhancing food availability, and SDG 13 (Climate Action) by promoting sustainable intensification practices. In conclusion, accelerating the adoption of improved rice seeds presents a viable, high-impact strategy for transforming Tanzania’s rice sector. However, for this transformation to be sustained and inclusive, it must be embedded in a holistic approach that addresses systemic constraints, empowers farmers with knowledge and tools, and fosters innovation throughout the entire seed value chain.

Author’s contribution

Conceptualization, writing the whole manuscript, Formal analysis/simulation, and investigator.

Funding

Not applicable

Availability of Data

Data available on request

Ethics approval and consent to participate

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.

Consent for publication

Not applicable

Competing Interest

The author declares no conflict of interest.

Clinical Trial Number

Not applicable

Author details

Department of Business Management, College of Humanities and Business Studies, Mbeya University of Science and Technology (MUST), P. O. Box 131, Mbeya, Tanzania.

Acknowledgments

The author sincerely appreciates the support and guidance provided by the Simetar team, particularly Prof. James Richardson and Dr. Jean-Claude Bizimana, for their support and guidance on 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.

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You are reading this latest preprint version

Improved Seeds and Rice Productivity in Tanzania: Policy Insights from National Sample Census Data

Status:

Version 1

Abstract

Figures

1.0 Introduction

2.0 Methodology

2.1. Study Area

2.2. Data Sources

2.3. Data Processing and Simulation

2.4 Ranking of Target Probabilities Using the Stoplight Function

3.0 Results and Discussion

3.1. Model Validation

3.2. Overview of Improved Seeds Use in Tanzania

3.1. Impact of Fertilizer Application on Rice Productivity Across All Farms in Tanzania

3.1.1 Scenario A: (lower thresholds: Max 3.0 t/ha and Min 1.5 t/ha)

3.1.2 Scenario B (Higher thresholds: Max 4.5 t/ha and Min 2.0 t/ha)

4. Discussion

5. Conclusions

Declarations

References

Additional Declarations

Supplementary Files

Status:

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~	=	A tilde represents a stochastic variable.
i	=	Type of farming practices used (no-fertilizer (F0), organic (F1), and inorganic fertilizer (F2)
\(\:\omega\:\)	=	Represents farms in Mainland Tanzania, Zanzibar, and other main producing AEZs
a_i	=	Hectares (ha) allocated for farming practice i
	=	Stochastic mean yield per ha for farming practice i
\(\:{\stackrel{-}{y}}_{i}\)	=	Deterministic (mean) yield per ha for farming practice i
\(\:{\beta\:}_{{\stackrel{\sim}{y}}_{0}}\)	=	The normalization factor which is given by \(\:\frac{{\stackrel{\sim}{y}}_{c,i}}{{\stackrel{\sim}{y}}_{h,i}}\:\)and it is used to scale/adjust the 2019/20 NCSA mean yield.
\(\:{\stackrel{\sim}{y}}_{c}\)	=	Stochastic yield for the current survey (2019/20 NCSA)
\(\:{\stackrel{\sim}{y}}_{h}\)	=	Stochastic yield for the historical or previous survey (2007/08 NCSA)
S_y	=	Fraction deviations from the mean or sorted array of random yields for farming practice i
P(S_y)	=	Cumulative probability function for the S_y values
CUSD_y	=	Simetar function to simulate the correlated uniform standard deviation of random variables.
EMP()	=	Simetar function used to simulate a stochastic variable (yield)