Match Each Practice of the Agricultural Revolution With Its Description

Match Each Practice of the Agricultural Revolution With Its Description.

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  • Published: January 15, 2021


In this newspaper, the authors utilize survey data from over 800 households to examine the touch of demonstration plots and associated activities (distribution of small packs of agricultural inputs) on smallholder farmers’ decisions to buy agricultural inputs in Tanzania. Using propensity score matching and inverse probability-weighted adjustment models, the authors estimated the result of access to demonstration plots solitary and demonstration plots combined with small packs of agronomical inputs on a household’s decision to purchase improved inputs. The results bespeak that access to demonstration plots and sit-in plots with small packs increased the probability of purchasing improved inputs by thirteen–17 percentage points. This paper suggests that demonstration plots and sit-in plots with pocket-sized packs are an constructive model for enhancing improved technology adoption and are further increased when those inputs are bachelor inside a 5km radius. The results point to the importance of strengthening farmers’ organizations and terminal-mile agricultural input suppliers in order to enhance and facilitate access to data, appropriate production techniques, and improved inputs. The results as well point the importance of investing in infrastructure to reduce transportation costs that limit market efficiency for advisable technologies.

1. Introduction

1.1 Background

Despite some progress over the past years, agricultural productivity in sub-Saharan Africa is notwithstanding depression and far below potential [1]. In Tanzania, smallholder agriculture is the main source of livelihoods for most of the population, employing over 70% of the population and contributing 25% to the Gdp [2]. The fact that nearly of the population contribute only 25% of Gross domestic product is indicative of the low productivity and therefore high vulnerability to food and income insecurity. The master factors that limit the productivity of smallholder agriculture in the land include land degradation and poor soil fertility; climate variability; crop pests and diseases; low adoption of improved agronomic practices equally a result of inadequate access to data and unreliable agro-input supply systems and institutional barriers such as poor markets for inputs and farm products; and poor farmer arrangement [3].

The Government of Tanzania and its development partners accept developed and implemented several policies and programs aimed at improving agronomical productivity and nutrition as summarized by [iv]. These include the Tanzania Development Vision (TDV 2025), CAADP state strategy, which translates into the Food Security Development Plan (TAFSIP– 2016/17–2020/21); Agricultural Sector Development Plan II (2016/17–2020/21); the National Multi-Sectoral Nutrition Action Plan (NMNAP– 2016/17–2020/21), District Agricultural Development Plans (DADPs), and the Southern Agricultural Growth Corridor of Tanzania (SAGCOT). Efforts of these plans have realized some results but more still needs to be done. At the global level, in 2010, the United States government launched a hunger and nutrient security initiative, Feed the Future (FtF), which is designed to harmonize regional hunger- and poverty-fighting efforts in countries with chronic food insecurity and insufficient product of staple crops. Tanzania was one of the beneficiary countries as function of this initiative. The FtF initiative was designed to thrive on leveraging of partnerships, innovation and host regime leadership [five].

One of the FtF investments in Tanzania was the NAFAKA staple value chain project led past ACDI/VOCA. The beginning phase of the action (project) was deputed in 2011 and ran through 2016 with a goal of sustainably reducing poverty and hunger by improving the productivity and competitiveness of maize and rice value chains that offer task and income opportunities for rural households in Tanzania [half dozen]. A 2nd phase of the NAFAKA project focusing on market systems development (NAFAKA Two) was launched in September 2016 and will close activities in October 2021. The NAFAKA project partnered with another FtF initiative, the Africa Enquiry in Sustainable Intensification for the Next Generation (Africa RISING) led by the International Establish of Tropical Agriculture (IITA). This project also had a focus on creating opportunities for smallholder farm households to move out of hunger and poverty through sustainably intensified farming systems that improve food, nutrition, and income security, particularly for women and children, and conserve or enhance the natural resource base.

Given the axis of agricultural extension and advisory services for addressing rural poverty and food insecurity [7], the 2 interventions made investments in this component. Particularly, demonstrations plots were used in conjunction with other extension methods and techniques given their office in enabling farmers to learn start-manus well-nigh improved technologies [8] and and so complemented with small packs of agro-inputs and extension training activities to stimulate farmers’ trial and experimentation before making adoption decisions as suggested by [ix]. The objective of this study is therefore to appraise whether these influence farmers’ decisions to adopt agro-inputs when compared to farmers in non-project locations.

1.2 Study context and related literature

Our study focuses on activities related to maize production in Tanzania, the largest producer in East Africa. Maize is also the master staple crop in Tanzania, in addition to rice which NAFAKA and Africa Ascension projects also work with in the country. Both crops are grown by over ninety% of farmers in the country. The implementation approach for the NAFAKA and Africa RISING projects involved developing a network of rural-based extension service providers (volunteer and government staff), group and association capacity building and enhancing access to agro-inputs through agro-input supply networks. NAFAKA has additional unique approaches to expanding market and merchandise and engaging with public and private sectors to play active roles in enhancing smallholder livelihoods across the value chain.

The intervention farther focuses on the establishment of demonstration plots for farmer learning and experimentation, thereby providing an opportunity for them to observe the benefits of crop varieties, skillful agronomic practices (GAPs) and natural resource management. The plots are managed by the village-based extension staff and lead farmers who apply them to provide direct training to farmers with technical support from NAFAKA and Africa Ascension scientists. Some other utility of the demonstration plot model is that it is anticipated to stimulate farmers’ purchase of agro-inputs after observing articulate benefits of the technologies at the plots.

Demonstration plots, and afterward farmer field schools have been a cornerstone of agricultural extension services in Tanzania [8, 10, 11]. Demonstration plots, when well planned, designed and implemented, provide an opportunity for beneficiaries to, among others, encounter the technologies together with their benefits besides as collaborate with the scientists, extension staff and other actors in development and enquiry. The beneficiaries are also able to have key questions answered and doubts cleared thereby providing further reinforcement on their decisions to adopt the sit-in technologies. Several studies related to sit-in plots and cereals product have been conducted in East Africa. For instance, [12–14], analyzed the bear upon of demonstration plots and other factors on farming practices, while [15, xvi], and specifically focused on the impact of demonstration plots on cereals farming in Eastward Africa.

Results of these studies prove different benefits of demonstration plots on household income and investment. Notably [12], concludes that an extension program featuring demonstration plots contributed to statistically significant increases in household income and investment. Likewise [xiii], found a highly statistically meaning increase in farm income for farmers attending Farmer Training Centers and demonstration plots. In contrast [fourteen], showed that although grooming programs featuring sit-in plots were linked to adoption decisions, the affect was limited by majuscule constraints. However, to our cognition, very few studies explicitly focused on the extent to which demonstration plots, either in isolation or in combination with other activities influenced farmers’ decisions to buy and use inputs associated with the sit-in technologies. We aim to contribute to the growing literature on agronomical extension by assessing the issue of demonstration plots and sit-in plots combined with modest packs of inputs on the purchase of improved agronomical inputs using a unique and recent household-level information. Precisely, we use the propensity score matching (PSM) and the doubly robust inverse probability weighted regression adjustment (IPWRA) models to judge the boilerplate treatment effects. The IPWRA provides efficient estimates by allowing the modelling of both the outcome and the handling equations and requires that simply i of the two models are correctly specified to consistently approximate the affect [17].

The rest of the paper is organized as follows. In the side by side department, nosotros present the sampling strategy and data collection procedure. Section three lays out the empirical framework whereas section iv presents the results and discussion. The terminal section draws conclusions and recommendations.

two. Materials and methods Sampling and information collection

The study was conducted in: (i) districts where NAFAKA/Africa Rising was operational from the inception of the projects in 2012 (i.due east. Kongwa and Mvomero districts); (ii) districts where NAFAKA/Africa RISING started operating in 2016 (i.due east. Iringa Rural and Kilolo districts). These districts are shown in Fig 1. As the beginning phase of the sampling procedure, these districts were selected purposively. Specifically, Kongwa and Iringa districts were selected to participate in the Africa Ascent/NAFAKA projects because they had some of the about nutrient-insecure villages in Tanzania. There are parts of these districts that are semi-arid with unreliable and unevenly distributed rainfall associated with frequent cycles of drought and flooding pushing agro-pastoral and smallholder farming households over the edge. Without the benefit of modern farming technologies, farmers typically rely on low-yielding practices and ingather varieties. Opposite to Kongwa and Iringa, Mvomero and Kilolo districts have stable and reliable rains but the farming communities typically own minor land sizes and most are remote and thus far from markets. Also, they face threats of country deposition and diminishing subcontract outputs although they are using improved seeds and fertilizers.

In the second phase, sets of villages were selected as “treatment” villages and “control” villages from each district. Handling villages are those where NAFAKA/Africa Ascent had interventions and command villages are those that did not receive any project intervention. The control villages were identified in the same agroecological zone as treatment villages. Farmers in these villages rely exclusively on public extension services provided past village agricultural extension officers (VAEOs). The VAEOs operate in a challenging piece of work environment with limited travel and logistical support, limited preparation on new technologies and minimum supervision. There were no established demonstration plots in the control villages at the time of conducting this report.

VAEOs were engaged past the projects through boosted GAP trainings. Each VAEO was also tasked to establish a demonstration plot in collaboration with existing farmer groups in his or her village of performance. Also working with customs members, the treatment villages also benefited from the ACDI/VOCA Village-Based Agricultural Agents (VBAAs). These agents were selected by customs members to complement the VAEOs’ work and provide immediate GAP knowledge in the villages after completing a series of training sessions conducted by the projects. In addition to co-managing the demonstration plots, the VBAAs besides provided small packs of agro-inputs to farmers and, where supported, established agronomical input shops thereby increasing farmers access to these inputs.

In each of the four NAFAKA/Africa RISING districts, five treatment villages and v control villages were randomly selected for the survey using probability proportional to size sampling (PPS). It is also noteworthy that the study focused on only maize production locations.

Finally based on a sample size calculation because the total number of NAFAKA/Africa RISING farmers, 400 farmers each were selected from the treatment and control villages to create a total sample size of 800 respondents. Nonetheless, to account for the non-response rate, more than than the minimum target of 20 farmers per village were interviewed in some villages. In total, 866 respondents were interviewed including 444 respondents from treatment villages and 422 respondents from command villages. Nevertheless, due to incomplete data from some of the questionnaires, but 852 households were considered in the analysis.

Data were collected in February 2018 using interviews with respondents from the treatment and command villages. Specifically, a team of well-trained enumerators used an electronic questionnaire on the
Kobo Toolbox
smartphone awarding to interview the selected survey respondents. The interviews were conducted in the local language (Swahili) to ensure that the questions could be easily understood by all respondents. The use of an electronic questionnaire was very cost-effective and allowed for highly efficient survey enumeration.

2.2 Ethics statement

“The information was collected through household surveys and data were analyzed anonymously. The participants in the survey were selected from the beneficiaries and not-beneficiaries of the Africa RISING and NAFAKA project. A clear explanation of the objectives of the survey was given to the participants and all of them were asked for their exact informed consent to willingly participate in the study. If the respondents declined to exist interviewed, the reasons for their refusal were also recorded and no one was forced to participate in the survey.”

2.3 Conceptual framework and empirical procedure

In this report, we view the decisions of the farmer to visit a demonstration plot in a given flow to be derived from the maximization of expected utility subject to cash, credit, and other constraints [18]. In the spirit of other studies in the vein (e.m. [19–21]), permit (U

) represent the utility to the farmer from accessing a sit-in plot and let (U

) correspond the utility from non visiting a demonstration plot. A farmer will choose to visit a sit-in plot if


is a latent variable adamant past observed characteristics (Z

) which include group membership, ownership of household assets, livestock, household head socioeconomic characteristics and average annual rainfall and; the mistake term (e

) such that:




is a binary indicator variable that equals 1 if a farmer visits a sit-in plot and/or demonstration plot with small packs (hereafter referred to as treated) and zilch otherwise (hereafter referred to as not treated) and
is a vector of parameters to be estimated.

2.3.1 Propensity score matching.

As explained above, we envisage that accessing sit-in plots and demonstration plots with small packs volition encourage farmers to invest in improved inputs. To estimate the affect of the demonstration plots and/or demonstration plots with small packs on the agro-input purchase, nosotros used the propensity score matching approach [21–23]. Specifically, we used the Boilerplate Treatment Outcome on the Treated (ATT) to measure the touch which is the average departure between expected outcome values with and without handling for those who had access to sit-in plots and/or sit-in plots with small packs. Post-obit [24, 25], the ATT tin be defined equally:


(.) is the expectation operator,

is the outcome for the treated households,

is the counterfactual result for the same household and


is equally defined as in a higher place. One problem that arises in estimating Eq (2) is that nosotros can but detect either


but not both of them for each household. Using the mean outcome of untreated individuals may lead to selection bias because it is most likely that components which decide the treatment decision likewise determine the outcome variable of involvement particularly in non-experimental studies [24]. To address this problem, we use PSM. The PSM uses propensity scores to match every individual observation of treated households with an observation with similar characteristics from the non-treated or command group. The propensity score is the conditional probability of assignment to the treatment given a vector of observed covariates [26]. In an ideal state of affairs, random consignment to handling is the all-time way of correcting for selection bias because all beneficiaries would have an equal chance of being assigned to each treatment [27]. Nevertheless, implementing a randomized experiment is quite expensive and was non feasible in our written report. Other methods of correcting for choice bias due to both observed and unobserved characteristics such equally Instrumental Variable (Iv) techniques impose distributional and functional form assumptions, such equally linearity on the event equation and extrapolating over regions of no common support, where no similar treated and non-treated observations exist [21]. Although PSM does not correct for pick bias due to unobservables, it does not impose distributional assumptions. Incorporating propensity scores in Eq (2) leads to:



) are the propensity scores estimated from Eq (1) and defined as:


is a vector of covariates based on observed characteristics (i.e. the aforementioned as


in Eq (ane)) and
F{.} is a normal cumulative distribution function. In the estimation of the ATT, nosotros used the nearest neighbour and kernel-based matching algorithms.

PSM estimation relies on two of import assumptions; the provisional independence and overlap assumptions. The conditional independence assumption (CIA) states that the handling consignment is essentially randomized when we status on a rich fix of covariates. It suggests that that systematic differences in outcomes between treated and comparison households with the same values for covariates are owing to handling [25]. The CIA supposition cannot be tested and merely relies on conditioning on a rich set of observed covariates. The overlap assumption on the other manus states that conditioning on a prepare of covariates, anybody has a positive probability of receiving treatment (also known as the overlap assumption). We exam this assumption in the subsequent sections.

2.3.two Inverse probability weighted regression aligning.

As a robustness bank check, we also estimated the ATT using the changed probability weighted regression adjustment (IPWRA) which is sometimes referred to as a doubly robust estimator [17, 28]. Like propensity score matching (PSM), the IPWRA only accounts for observed and does not control for unobserved heterogeneity. One of the drawbacks of the PSM method is that biased estimates may be obtained if the propensity score model is misspecified [28]. Unlike PSM, the IPWRA method provides efficient estimates by allowing the modelling of both the event and the treatment equations and requires that only one of the two models are correctly specified to consistently estimate the touch on. Information technology combines the inverse probability weighting (treatment model) with regression adjustment (outcome model) to achieve this. The ATT for the IPWRA can be specified equally:



are attained from the inverse probability-weighted to the lowest degree squares problem for the treated group



are attained from the inverse probability-weighted least squares problem for non-treated


The * on the estimated parameters
β, and
describes the double robustness result;

are the estimated propensity scores. Note that the
X’s are defined as above.

3. Results and word Descriptive results

Tabular array 1 shows the result and explanatory variables considered in the written report, drawn from the extensive literature on agricultural extension (e.g. [vii, 29–31]). On boilerplate, 33% of the households purchased improved agricultural inputs. The improved inputs include fertilizers, crop protectants, and improved seeds. Accordingly, ‘improved inputs’ as used in this study is a purchase of the combination of improved seeds, fertilizers and crop protectants. A household was considered to have purchased improved inputs if they bought any one or a combination of the inputs. Results in Tabular array i also show that nigh 37% of the households had access to sit-in plots while 33% had admission to demonstration plots and received a minor pack of improved inputs. Demonstration plots are farmer-owned and farmer-managed plots of state used by hamlet-based extension agents (VBAA), village agricultural extension officers (VAEOS) or Pb Farmers as a platform for grooming farmers on GAPs. They are designed to facilitate positive changes in farmer practices through the integration of core behaviours in their farm activities such as proper country preparation, proper spacing, utilize of fertilizer and improved seeds, soil and water direction, pest and disease control, and pre-harvest/harvest/post-harvest practices. Such practical training in the demonstration plots is the initial step towards developing noesis and skills for farmers to build their capacity to adopt improved practices and, in plough, increase marginal sales and yields. Farmers were asked whether they have ever accessed the project demonstrations at least in one case for purposes of accessing knowledge and skills which they transfer to their farm operations.

In dissimilarity, small packs represent agro-inputs marketing arroyo designed to remove barriers to smallholder farmers’ adoption of improved seeds and fertilizers in rural and remote areas suffering from the prevalence of expired and counterfeit inputs, particularly seed, leading to low conviction among farmers that improved seeds and fertilizers justify their investment costs. Information technology involves packs of seed or fertilizer ranging from l to 250 grams beingness distributed for gratis to farmers past VBAAs for them to effort out for purposes of eliminating doubt, increasing awareness, and generating interest in purchasing these inputs.

To capture household majuscule endowments, we include household size, educational activity and wealth. The size of the household is usually a proxy of household labour availability and previous studies take shown that larger households are more than likely to prefer improved agricultural technologies [32]. We expect access to demonstration plots to increment with education because more often than not, education broadens interest in admission to information and services, supporting innovation. We proxy for wealth using a wealth index constructed using principal component analysis (PCA). The wealth index includes variables measuring various dwelling characteristics: access to electricity, toilet quality, roof quality, floor quality, and the number of rooms. Also, mobile phone ownership and livestock ownership are included in our models merely are not office of the constructed wealth index. It is expected that wealthier households are more likely to access demonstration plots and utilize improved agricultural inputs because, in virtually cases, improved agricultural inputs are expensive.

Social upper-case letter is of import in not only facilitating access to improved agriculture technologies but also in mitigating against production and net returns risks. We measure out social capital in terms of farmer and lender group membership. Group membership indicates the intensity of contacts with other farmers, hence farmers who do not have contacts with extension agents may notwithstanding be informed virtually new technologies by their colleagues [33]. Results betoken that about 58% of the sample households were members of a farmer group.

Finally, most countries in sub-Saharan Africa, including Tanzania are subject area to environmental problems such as droughts and uneven distribution of rainfall and may also touch on the determination to buy improved agricultural inputs. We capture the variability in rainfall by including a rainfall variable which measures the corporeality of rainfall that was received in the 2016–17 farming season.

Tabular array ii shows the descriptive statistics disaggregated by access to demonstration plots. There is a statistically pregnant difference between the two groups for several variables notably, households which accessed demonstrations had significantly higher means for several variables related to improved inputs, wealth and access to resources.

3.two Empirical results

A logit model was used to estimate the probability of access to sit-in plots and sit-in plots with small packs. Table 3 shows the marginal effects, with standard errors amassed at the village level for the results in columns 2 and 4. Even though the main objective of the study was to examine the touch of extension (i.e. access to demonstration plots and demonstration plots with pocket-sized packs) on the purchase of improved inputs, we briefly discuss the results in Table 3. The results bespeak that female person-headed households were eight% and 7% less likely to access demonstration plots and demonstration plots with small packs and these results are in line with the findings of [34]. Consequent with previous studies on extension [e.chiliad. 35], we constitute that households with larger farms were less likely to access demonstration plots with pocket-size packs. This is plausible because most extension agents are more probable to target smallholder farmers. The results also show that admission to sit-in plots and demonstration plots with small packs increased with livestock ownership and wealth index. Wealthier households are commonly in a meliorate position to bear the possible risks and costs associated with accessing sit-in plots and may take the power to finance the buy of inputs. The results also bespeak that mobile phones increased the likelihood of accessing demonstration plots past 8%, which is likely because mobile phones are an important information admission tool allowing farmers to exchange information regarding the location of the demonstration plots for instance.

Similar to the results plant by [36, 37], results in Table iii bespeak that admission to demonstration plots and demonstration plots with pocket-sized packs increased with membership in farmer and lending groups by between 12%–25%. Bicycle ownership and access to a tarred road are proxies for transport equipment and transaction costs associated with accessing data through sit-in plots. Specifically, the results evidence that the probability of accessing demonstration plots increased by 39% and that of demonstration plots with small packs past 34%. Accessing a tarred road also increased the propensity to access sit-in plots and demonstration plots with small packs by 34%, suggesting that farmers who are located virtually a tarred route were more are probable to access extension services [37].

Finally, district dummies reverberate the agro-ecological and resource differences in the four districts. Relative to Kongwa district, farmers in Kilolo and Mvomero districts were less probable to have access to demonstration plots and demonstration plots with small packs.

three.iii PSM estimates of the touch on of access to demonstration plots and demonstration plots with small packs on the buy of improved inputs

The logit model results presented above (with standard errors clustered at village level) were used to generate propensity scores upon which the observed characteristics were balanced across the treated and non-treated households. Before estimating the causal effects of demonstration plots and sit-in plots with small packs on the purchase of improved inputs, we first tested whether the overlap assumption was satisfied and accessed the quality of matching on propensity scores. Fig two shows the propensity score distribution and common support for propensity score estimation. The results bear witness that the common support condition is satisfied equally at that place is substantial overlap in the distribution of the propensity scores of the treated and non-treated groups.


Fig 2.

Propensity score distribution and common support for propensity score interpretation.

Notation: ‘‘Treated: on support” indicates the observations in the treated grouping (demonstration plots and demonstration plots with small packs) have a suitable comparison. ‘‘Treated: off support” indicates the observations in the treated grouping that do not have a suitable comparing.

Since PSM relies on conditioning on propensity scores and not on all the covariates, it must be checked if the matching procedure can rest the distribution of the relevant variables in the control and treatment groups [25]. Tabular array iv presents the results from covariate balancing tests earlier and later on matching. The reduction in the mean absolute standardized bias betwixt the matched and unmatched models was used to assess the balancing of covariates. The balancing tests in Table four showed a substantial reduction in the mean absolute bias between the matched and unmatched models, with no pregnant differences after matching. The total bias reduction ranged from 71–76% and this indicates that PSM was successful in reducing pick bias due to observed characteristics.

The effects of demonstration plots and demonstration plots with pocket-sized packs on the purchase of improved inputs estimated with the nearest neighbour (NNM) and kernel-based matching (KBM) models are presented in Tabular array 5. The results from the two models are similar (albeit with dissimilar treatment effects magnitudes) and they indicate that the probability of purchasing improved inputs increased with access to demonstration plots and demonstration plots with small packs. In the NNM model, visiting a sit-in plot increased the probability of acquiring inputs by xiii percentage points. The households that received small packs in combination with access to demonstration plots were also likely to procure improved inputs by 15 percent points as compared to the not-treated households (Table 5). The results for the KBM matching algorithm can be interpreted similarly.

3.iv Sensitivity analysis and robustness checks

3.4.i Sensitivity analysis with Rosenbaum bounds.

Since the estimation of treatment effects with PSM is based on observed characteristics, a hidden bias may arise if treated and not-treated individuals differ on unobserved variables which simultaneously affect assignment into treatment and the outcome variable. Using the bounding approach suggested past [38], we assess how strongly an unobserved factor may influence the selection process to invalidate the results of PSM assay [25]. Because that our outcome variable is binary, we use the Mantel-Haenszel (MH) bound proposed past [39]. The results in Tabular array 6 indicate that the handling effects were quite robust to the presence of hidden bias at different critical levels of hidden bias (Γ). Beyond the unlike treatment variables, the level at which we start to question our determination of a positive effect of demonstration plots and demonstration plots with minor packs on improved inputs purchase ranges from Γ = 1.four–1.6. This implies individuals differ in their odds of handling by a factor of 40–60%, in terms of unobserved covariates. These values or bounds reflect “worst-case scenarios” and hence do not bespeak the presence of choice bias merely merely tell us how strong the option bias should be to invalidate our conclusions [25].

iii.4.2 IPWRA estimates of the bear on of access to demonstration plots and demonstration plots with small packs on the purchase of improved inputs.

As a key robustness check for the PSM results, we too estimated the IPWRA model and the results are presented in Table 7. The first and second stage results from the IPWRA are presented in Table A1 in S1 Appendix. The first phase results (treatment equation) shows the determinants of access to demonstration plots and demonstration plots with small packs and are like those presented in Table 3. Since our interest was mainly to compare the bear upon results with those of the PSM, nosotros are not going to interpret the results in Tabular array A1 in S1 Appendix. When estimating the IPWRA model, we also conducted an overidentification exam for covariate residuum to check whether the covariates were balanced later propensity score reweighting. The results in Table A1 in S1 Appendix bespeak that nosotros cannot pass up the nothing hypothesis that the covariates are balanced, implying that there is no evidence that the covariates used remain imbalanced after propensity score reweighting.

The results evidence that participating in sit-in increases the probability of households to purchase improved inputs past 16 percent points. Similarly, the probability of buying improved inputs increased by 17 percentage points for the households who accessed demonstration plots with pocket-sized packs. The IPWRA results are very similar to the PSM results which gives credence to our PSM results. The results also suggest that our propensity score model was not misspecified.

four. Conclusions and recommendations

This article examines the affect of sit-in plots on the use of improved agricultural inputs in Tanzania. Specifically, we use survey information from more than 800 households and a combination of propensity score matching and the doubly robust inverse probability weighted regression models to achieve our objective.

The results bespeak that livestock ownership, membership in farmer’s and lending groups, and access to a tarred road were some of the important determinants of access to demonstration plots and demonstration plots with small packs. Overall, the empirical results beyond our estimation methods used in this study were largely consistent and show increases in input purchase by between xiii per centum points (for demonstration plots) and 17 percentage points (for the combination of demonstration plots with small packs).

The result suggests that strengthening farmers’ organizations and associations are disquisitional for potentially enhancing, not only admission to and employ of agro-inputs, only besides facilitating admission to output markets through improved quality, access to information and knowledge as well equally facilitating engagement with policymakers [40, 41].

Though both the control and treatment villages had village agriculture extension officers, the results from this study revealed that farmers in treatment villages were more likely to purchase improved agricultural inputs, which is the objective of well-nigh of the agricultural extension models. The results point to the demand for policies to aggrandize by demonstration plots and encourage financial investment to adopt the VBAAs, and farmer organizations models to act every bit agents for multiple seeds, fertilizers and crop protection companies. Policies that encourage individual entrepreneurs and farmer organizations that can “certify” themselves through VAEOs or the Tanzanian Ministry building of Agronomics to act as village agents providing credible GAP noesis every bit they identify marketing opportunities volition further increment revenues at the village level. These certifications should also be provided with an incubation period that allows new agro-input businesses to increase their cash flow, assuasive for an expansion of growth and to constitute a customer base.

Furthermore, it is apparent from the results of this study that to enhance smallholder access to demonstration plots, investing in the rural road infrastructure is important. This is considering roads not only facilitate access to demonstration plots merely also reduce the cost of transportation to the input and output markets.

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