Next Article in Journal
Conflicting Values in Rural Planning: A Multifunctionality Approach through Social Multi-Criteria Evaluation
Previous Article in Journal
Sustainability in the Minerals Industry: Seeking a Consensus on Its Meaning
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 10
Issue 5
10.3390/su10051430
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Conservation Agriculture for Environmental Sustainability in A Semiarid Agroecological Zone under Climate Change Scenarios

by
Zhang Hong
1,
Msafiri Y. Mkonda
1,2,* and
Xinhua He
1,3,*
1
Centre of Excellence for Soil Biology, College of Resources and Environment, Southwest University, Chongqing 400715, China
2
Department of Geography and Environmental Studies, Solomon Mahlangu College of Science and Education, Sokoine University of Agriculture, Morogoro 3038, Tanzania
3
School of Biological Sciences, University of Western Australia, Crawley 6009, Australia
*
Authors to whom correspondence should be addressed.
Sustainability 2018, 10(5), 1430; https://doi.org/10.3390/su10051430
Submission received: 11 February 2018 / Revised: 22 April 2018 / Accepted: 26 April 2018 / Published: 4 May 2018
(This article belongs to the Section Environmental Sustainability and Applications)
Browse Figures
Versions Notes

Abstract

:
Using the Mann–Kendall Test to analyze data from a survey of 400 farmers, this study compared the rate of adoption of conservation agriculture (CA) in two contrasting villages of Mnyakongo and Ugogoni locating in the Kongwa District, a semi-arid zone in central Tanzania. Results exhibited that the level of CA adoption was <10% of the total households. The trend of CA adoption was determined at the coefficient of R2 = 0.95, 0.90, 0.68 and 0.57 for mulching, crop rotation, agroforestry and little tillage, respectively. Despite little tillage and crop rotation having high acreage under CA, the rate of mulching adoption was significantly higher than that of others. Furthermore, there were significant correlations between the CA adoption and crop yields or environmental sustainability (p < 0.05). Maize, sorghum and millet yields were significantly greater under CA (1.7 t ha−1) than no-CA (0.7 t ha−1). Particularly, maize yields were increased from 1.3 to 2.3 t ha−1 from 2000 to 2015 under CA when it was intercropped with legumes. The majority farmers (>70%) asserted that CA had optimized their yields for both food and economic incentives. Thus, this study recommends the adoption of CA in the semi-arid agro-ecological zones.

Graphical Abstract

1. Introduction

The significance of soil or environmental conservation to limit soil degradation has been advocated since 1903 [1]. The current situation of a rapid population increase and global climate change has necessitated the practicability of such soil conservation [2]. For example, the USA government has invested millions of dollars yearly to support conservation related projects [3,4]. To increase fruit production and environmental conservation in organic Australian vineyards, both mulching and composite are used as conservation agricultural practices [5]. This practice increases crop yields and environmental conservation in various areas of the country.
The increasing extreme of weather changes especially for temperature and precipitation has significantly impacted the nutrient cycling and soil moisture in most of Sub-Saharan Africa [6,7]. These weather changes could intensify in the future as various climate models have been predicting further climate alterations. As a result, a crop production system and its productivity are to worsen with possibly high outbreak of diseases, pests and pathogens.
To intervene with these authentic and potential consequences, we need to develop resilient agricultural systems through rational and affordable strategies that maintain the ecosystem functions and protect livelihoods [8,9,10]. Studies have recommended different conservation agriculture (CA) practices to mitigate, and adapt to, climate impacts [11]. CA features in various forms such as organic soil management, agroforestry, crop rotation, etc. Bennett et al. [12] and Malviya et al. [13] in their models found that N2-fixing legumes hve potential for both crop yields optimization and environmental services. Moreover, Zhu et al. [14] realized that organic matter from dead debris and litter decomposition are important for improving soil fertility in various ecosystems. Organic soil management is an important aspect of CA.
Since smallholder farmers are vulnerable to extreme change of rainfall pattern, CA can be their best preposition to elevate their resilience [15,16]. This is particularly important because it is expected that, by 2030, Africa will have 120–150 million smallholder farmers and most of them will rely on rain-fed agriculture for their subsistence [17]. While the dependence on rainfed-fed agriculture is expected to increase, the projections from an assessment of 12 CMIP3 (CMIP3 - Coupled Model Intercomparison Project Three) (AR4-4th Assessment Report) GCMs (Global Climate Models) over Africa suggest an extreme variation in rainfall by the end of the 21st Century [18]. In that respect, the susceptibility of the farmers will further increase due to such increased challenging condition [19,20].
Specifically, the farmers’ vulnerability with less rainfall is expected to be worst in arid and semi-arid areas due to increased drought effects in these already stressed areas [18,19,20,21]. Such effects in these areas cannot be underestimated because the global dry land areas cover about 41% of Earth’s surface and sustain the livelihoods of about two billion people [21,22]. In these areas, drought is greatly exacerbated by the global increase in temperature and decrease in rainfall [23]. For instance, studies have revealed the average increase of global surface temperature as 0.8 °C during the past century or the last three decades [24], leading to the deterioration of agricultural systems. Subsequently, this deterioration has exerted more pressure on already stressed environmental resources through overutilization, thus adding more complexity to sustainable agriculture management [22,23,24,25].
The frequently excessive droughts in most dryland areas have led to significant degradation of soil fertility [26]. Such soil fertility degradation is further accrued by poor agronomic practices and excessive chemical fertilization [27,28,29], and studies have shown that different forms and principles of CA, such as terraces or ridges, minimum tillage cropping, cover cropping, large pits and intercropping especially legume intercropping of sweet beans and lablab, have the potential to improve soil fertility and mitigate climate change impacts.
Similarly, soil fertility, crop production and carbon sequestration have increased in most areas where CA has been effectively applied [20]. Under CA, soil microbial communities become more functional than without CA and can influence a wide range of soil functions and ecological services such as organic matter turnover and nutrient cycling (nitrogen, phosphorus and carbon), among others [30,31]. Thus, CA has been recently proposed as Climate-Smart Agriculture (CSA) in most African countries because it is economically viable and environmentally friendly [20,30,31,32]. According to FAO [33], the CA practice could elevate food security, resilience and/or adaptive capacity to climate variability impacts by sequestrating carbon in both plant biomass and soils.
Similarly, Lal [34] promoted that reduced tillage is a powerful tool for retaining soil carbon and improving soil fertility for healthy crop growth and biochemical transformation of biomass carbon into soil organic matter or humus. It was evidenced that conventional tillage loses soil nutrients and water through infiltration [35]. Even minor soil disturbance has significant ecological effects on soil fertility in eastern and northern Tanzania [36,37].
However, despite this significance, there has been high inconsistence of CA adoption in most of Sub-Saharan Africa where about 70% of agriculture industry is under smallholder farmers who are most vulnerable to global climate changes. Their vulnerability is accrued by the high dependence on rain fed agriculture [38]. Thus, Sub-Saharan African countries have recently adopted the CA practice, although the adoption rate has not been sufficient in most of Sub-Sahara Africa (Figure 1).
Most Sub-Saharan countries recently started the adoption of CA. For example, Malawi, Zambia and Zimbabwe intensified their CA adoption in the 1990s [6,7,37]. Having been informed on the significance of CA, there is an immediate need to emphasize the optimal adoption and utilization of CA in the region (Sub-Sahara Africa) where most farmers are destitute and marginalized.

An Overview of Conservation Agriculture in Tanzania

Most Tanzanian communities adopted indigenous agricultural conservation practices, e.g., the Matengo pits (terraces) in Ruvuma, Chagga garden (agroforestry) in Kilimanjaro and Ngitiri (enclosed pasture) in Shinyanga regions to intervene the challenges associated with environmental stress. These practices have shown promise for optimizing crop yields, increasing fodder, controlling soil erosion and conserving moisture and fertility [27,39]. However, CA has only been operated in few regions including Dodoma, Manyara, Arusha and Southern highlands of Tanzania [40]. Few CAs have been practiced in the aforementioned regions that involve agroforestry, crop cover and crop rotation and are mostly influenced by private or government organizations [41,42,43,44,45,46,47,48,49,50,51,52,53]. Thus, reliable policy could greatly affect the CA adoption in the country. At present, CA receives little attention from the Tanzania Agricultural Policy [54] as the existing policy advocates the green revolution that emphasizes more on conventional tillage and chemical fertilization.
At the household level, the adoption of CA or any other agricultural technology is reached after the adopter is satisfied with the decision. In most cases, the household adopts new agricultural technology (i.e., CA) whose net benefits are significantly greater than those of an existing technology. In this approach, prospective new technology adopters observe the utility gained by the early adopters before adopting that technology. This can be described in various models as follows.
For example, the Heckman model [43] specified the decision and extent of technology adoption as follows:
Ci = Ziφ + εi (CA adoption)
Yi = Xiβ + µi (extent of CA adoption)
where Ci is a dummy variable for CA adoption; Zi is a vector of determinants of CA adoption; Yi is the extent of CA adoption (proportion of land area under CA); Xi is a vector of determinants of CA extent of adoption; θ and β are vectors of parameters to be estimated: and εi and µi are error terms.
Based on the Heckman model [43], for the estimated parameters of Equation (b) to be efficient, there should be no correlations between the two error terms (εi and µi). Nevertheless, the sample selection bias has resulted in a non-zero correlation between the two errors. To correct for this selection bias, the Heckman model firstly estimates Equation (a) to obtain a sample selection indicator, i.e., the Inverse Mills Ratio (IMR). This is suitable for measuring the covariance between the two errors.
The study by Rogers [9] had agreement with Heckman model, as it informed that “the innovation-decision process can lead to either adoption, a decision to make full use of an innovation as the best course of action available, or rejection, a decision not to adopt an innovation”. A few CA studies have been conducted to evaluate the extent of CA in Tanzania including on maize yields in the Uluguru Mountains (Eastern Arc Mountains) [26].
The present study focused on the semi-arid agroecological zone in Tanzania, where maize, sorghum and millet are the major crops, because this semi-arid area is the most vulnerable to climate change impacts and environmental degradation. In these areas, CA is the most reliable way of limiting these two major challenges. The dominant farming systems in the most semi-arid regions include: cropping, pastoralism and agro-pastoralism (integration of crops with livestock). Such integrations increase the biomass inputs of perennial plants and the optimization of nutrient amendments in the soil [30,39]. These systems can also increase the mutual interactions between plants roots and mycorrhizae fungi, and eventually nutrients uptake and resistant to pathogens by plants.
Although the science of CA and its significance at the global level is progressing rapidly, various knowledge gaps still exist, particularly in developing countries. This present study was geared to assess the rate of CA adoption and its ecological significance in the semi-arid areas of central Tanzania. Although several FAO-CA projects have been conducted in several Tanzanian regions, their impacts have remained trivial [41,42]. This study was hypothesized as follows:
H1. 
The rate of CA adoption is influenced by the desire of farmers to have optimal yields, preservation of soil moisture and fertility, control of soil erosion, and reduction of labor work.
H2. 
The rate of CA adoption is not influenced by the desire of farmers to have optimal yields, preservation of soil moisture and fertility, control of soils erosion, and reduction of labor work.
The above-mentioned items in the hypotheses were widely known to the respondents, thus it was possible to differentiate and/or synthesize one another during data collection and interpretation.
Here, we investigated the level of CA adoption in the semi-arid agroecological zone of central Tanzania. It was quite important to conduct such a study because most semi-arid areas experience frequent food-shortage associated with environmental degradation and extreme climate change impacts. Thus, the present study explored the rate of CA adoption and is socio-ecological significance to the community and environment. To achieve this objective, the rate of CA adoption was hypothesized against the factors for its adoption. The findings of such a study are expected to have significant contribution to the establishment of CA promotion policies in Tanzania with an earmark to the vulnerable communities and ecosystems. The policy advocacy on CA is a significant move toward sustainable adoption of CA in all countries’ agroecological zones.
At present, the adoption of CA is determined by personal characteristics (i.e., knowledge and experience), physical factor (land availability), and social and financial factors. Despite the insignificant willingness in the CA adoption, the practice has numerous fruitful results, as it improves biological functions of the soil through mycorrhizae fungi, ants and worms that can enhance nutrients uptake by plants. The basis of the study’s findings was fieldwork with a constructed conceptual framework (Figure 2) enlisting the significant aspects of the CA.
The framework portrays the major roles of CA as a tool firstly for increasing crop yields and secondly for environmental conservation to improve soil fertility through no- or reduced-tillage, mulching, agroforestry and crop rotation [50,51,52,53,54,55,56,57,58,59,60,61]. It also increases crop yields in terms of quality and quantity. By so doing, it curbs food insecurity and abject poverty. It further preserves biodiversity and mitigates the emission of greenhouse gases (GHGs), e.g. CO2, CH4 and N2O.

2. Materials and Methods

2.1. Profile of the Study Site

This study was carried out in the Kongwa District, a semiarid zone of Central Tanzania between June and September 2016 (Figure 3). This district is located on the leeward side of Ukaguru Mountains with an area of ~4041 km2 and a varying elevation between 900 and 1000 m a.s.l. (6°19′660” Latitude (S) and 36°15′36”Longitude (E)). The typical vegetation in the Central Tanzania is bush or thicket. The mean annual precipitation is 400–600 mm (most between December and April) and the mean annual temperature is 26 °C. The soil is classified as Chromic Luvisols (the FAO Soil Taxonomic System) with a sandy loam texture. The silt contents of the soils at different farms were not significantly different (p > 0.05) and ranged 170–255 g kg−1 soil with a bulk density of 1.25–1.65 Mg m−3 [37,44,45,46,47,48].

2.2. Agricultural Systems in the Area

With 3637 km2 of arable land, the study area is dominated by cropping systems, pastoral systems and mixed farming. About 80% of the cropping systems are under smallholder farmers (~2.5 hectares per household) who use a hand hoe as the main farming tool. Medium scale farmer (about 17%) use power tillers while large scale farmers (about 3%) use tractors. The dominant food and cash crops include maize, sorghum, millet, common beans, cassava, sweet potatoes, chick peas, sesame, cashews, sunflower and groundnuts. The dominant animals are cattle, sheep, pigs, donkey and goats. In addition, one ranch (~250 km2) and one pasture (~150 km2) are owned by the National Ranching Company and Livestock Research Center, respectively.

2.3. Methodology

2.3.1. Data Collection and Sampling Design

A simple random sampling was employed in selecting the study area. We picked one the Kongwa district among numerous districts of the semi-arid zone of Tanzania that are severely impacted by climate change and frequent food shortage. Purposive sampling was employed in selecting two representative villages, i.e., Mnyakongo and Ugogoni. Priority was given to villages that have been practicing CA. A reconnaissance survey was done in April 2016, two months before the actual data collection. During this phase, data collection tools were tested to determine their effectiveness. We also used this phase to process the required research permits and determined some key informants. All discrepancies raised during this phase were fixed before the actual process of data collection.
Data collection including household surveys, group discussions, informative interviews and physical observation was conducted from June to September 2016. These activities were simple and suitable because they optimally involved relevant stockholders. A simple random sampling was applied when selecting households while a systematic sampling was used to form groups for discussions. In addition, purposive sampling was employed in selecting the interviewees.
We also conducted intensive interviews with agricultural experts, extension officers and few elders. Data on CA (i.e., acreage under CA) and crops yields were gathered from the Kongwa District Agricultural and Livestock Development Officer (DALDO) and the Ministry of Agriculture, Livestock and Fishery, Tanzania.
The acreage data about areas under irrigation were gathered from the Dodoma Region Zonal Irrigation Office (in which Kongwa is affiliated). A total of 400 questionnaires were collected from household heads of smallholders (farmer/livestock households), as shown in Table 1. The questionnaires involved both closed and open questions. The selection of households was done by dividing the total number of households in each village by the required sample size (about 10%). The household lists were obtained from the village’s government leaders in the study area.
Interviews and household surveys were used to collect socio-ecological data at a society level. We collected both quantitative and qualitative data at the field and farm household level. About 258,219 ha (71%) of the total arable land (363,690 ha) in the district was cultivated by 45,271 households. The two representative villages had 4500 farming households with about 16,000 ha. Since we aimed to explore the rate of CA adoption, we selected 400 households (farmers) from the two villages on a random basis. In total, these 400 households had 1600 ha (an average of 2.0–4.0 ha per household) under crop production. We determined the overall farmers’ perception on CA, and its types and benefits. In the process, we also determined the availability of extension services. Finally, information on soil characteristics was mainly obtained from the Kongwa District Land Use and Planning office and literature review.
The Participatory Rural Appraisal method (PRA) was also employed to collect socio-economic data at the field level. These PRAs include informative interviews, group discussions, physical observation, etc. The application of the PRA method has been used to explore perceptions of rural communities on environmental issues that affect their lives [49,50,51]. One group discussion with 15 people was convened in each village, and interviews were conducted with 20 agricultural experts, farmers, livestock keepers and village government leaders.

2.3.2. Data and Statistical Analyses

We analyzed the quantitative data using the Mann–Kendall Test (at 95% level of confidence), and Microsoft excel (window 13) software. In this regard, the p-values less than 0.05 were supposed to be statistically significant (p < 0.05). The qualitative data from the household surveys were analyzed using theme content methods, whereas qualitative information was summarized and inserted in the text during discussions.

3. Results

3.1. Recent Adoption of Conservation Agriculture

Results showed that, despite the recently increased rate of CA adoption (Figure 4, Figure 5 and Figure 6), <10% of households had adopted it. For instance, in the two representative villages, 400 households cultivated an area of about 1600 ha for crop production, while 200 ha of CA were practiced by 10% of these households. At the district level, there were 45,271 farming households who had cultivated 258,219 ha (Table 2), but only 4300 households had adopted the CA for an area of 20,000 ha.
CA practices had been more adopted in Ugogoni than in Mnyakongo. The former had a higher averaged land size and total cultivated lands (Table 2). This brought significant differences in terms of socio-ecological benefits to both the community livelihoods and the environment.
In addition, the major types of the CA in the study area were agroforestry, mulching, crop rotation and minimum tillage. The lands allocated to CA were corrected by the total lands under farming (p < 0.05). Hence, predictions and extrapolations could be done based on such dimension.
Figure 4 indicates the temporal trend of CA adoption with special focus on little tillage, crop rotation, agroforestry and mulching. There was a slight increase in almost all CA practices, while reduced tillage and crop rotation were more adopted compared to others.
Figure 5 shows the adoption disparities between crop rotation and reduced tillage. More lands with CA practices were under reduced tillage (5000 ha) than under crop rotation (4500 ha). Meanwhile, crop rotation was significantly more adopted (R2 = 0.90) than reduced tillage (R2 = 0.57). Under such premises, it was evident that reduced tillage had less new adopters than crop rotation did, probably because it had already been adopted by the laggards (late adopters). These results agree with reduced tillage being the leading CA in Tanzania, although it is integrated with mulch, crop cover and legumes [26].
In addition, mulching and agroforestry (Figure 6) had smaller lands (1000 ha) with a high rate of CA adoption. Of these two, mulching appeared to have higher adoption rate (R2 = 0.95) than agroforestry did (R2 = 0.68) while the former received more new adopters. This was because most of the adoption was done within the past 20 years (1995–2015).
In fact, there is an immediate need to establish compelling efforts to make CA understandable and sustainable to farmers (Table 3). An intensive adoption of CA would therefore optimize sustainable livelihoods, especially for vulnerable and deprived smallholder farmers.
Results in Table 3 indicate that most farmers (50%–70%) asserted that the effectiveness of CA had been either very high or moderate. Most farmers (71%) asserted that crop rotation (e.g., maize, sorghum, millet, groundnuts, etc.) has been very effective, while 7% of them were not sure if the practice was effective. Likewise, most farmers (76%) asserted that the effectiveness of agroforestry has been moderate, while 6% did not think it was effective.
Figure 7 and Table 4 indicate that most farmers essentially adopted a CA practice to optimize yields. This notion was also observed in various studies and models [9,10,43]. The improvement of agro-ecosystems such as soil moisture and fertility retention, and control of soil erosion were other substantial reasons for adopting the CA in the area.
Figure 7 presents the farmers assertion based on questionnaires survey while Table 4 shows the findings from PRAs (i.e., mostly from discussion and interviews), although the results from these two sources correlate with each other.

3.2. Crop Yields

CA has proven to significantly contribute to crop yields. In the present study, there were significant differences (p < 0.05) between the yields from farms with and without CA (Figure 8). We calculated the yields in tons per hectare from different farmers with and without CA. These results agree with those of the FAO [33].
The findings of the present study revealed that maize, sorghum and millet yields were significantly greater (1.7 t ha−1) under CA than (0.7 t ha−1) without CA (Figure 8). The yields of maize, a preferred food crop in Tanzania, increased from 1.3 t in 2000 to 2.3 t ha−1 in 2015. Further, its yields were even more when intercropped with leguminous crops. In farms without CA, maize yields trailed by 0.8 t ha−1 in 2000 and more than 1 t ha−1 in 2015. Thus, there are significant differences (p < 0.05) between the two scenarios in terms of yields.

4. Discussion

4.1. Conservation Agriculture

The results from analyses revealed that, despite the significance of CA to both crop yields and environmental conservation, its adoption at both local and national level was very low (Figure 4, Figure 5, Figure 6 and Table 2). This reflects the African trend where CA is less adopted and predominantly under small scale (Figure 1). Crop rotation and reduced tillage were more optimally adopted than other forms in terms of land hectares (Figure 5).
However, the rate of adoption was significantly greater for mulching (Figure 6), with R2 = 0.95, followed by crop rotation, agroforestry and little tillage at 0.90, 0.68 and 0.57, respectively (Figure 5 and Figure 6). The CA with high land hectares (Figure 5) had been in practice for a couple of years while mulching and agroforestry (Figure 6) appeared to be new to the farmers, although the latter received high attention. Thus, time factor had significant influence in the CA adoption.
The findings indicate that households always adopt new agricultural technology when the benefit of a new technique is significantly greater than that of the existing technology. This utility-based adoption approach was also observed by Rogers [9], Mwaseba et al. [10] and Heckman [43]. In the study area, 150 respondents specifically adopted CA because they wanted higher yields, while 65 and 60 did so to conserve soil fertility and soil moisture and control erosion, respectively. In addition, 45 and 20 farmers adopted CA to reduce frequent labor work and related activities, respectively (Figure 7 and Table 4).
Most households adopted CA after a careful decision based on the trade-off. These results agree with Thierfelder et al. [6], Ngwira et al. [7] and Kimaro et al. [26] who had similar observations in other Sub-Sahara countries. Since the rate of CA was mainly hypothesized against the desire for higher yields, preservation of soil moisture and fertility, control of soils erosion, and reduction of labor work, this study has confirmed that the majority farmers adopted CA for higher yields.
Despite not being an extremely labor demanding practice, it was noted that labor shortages hampered CA adoption to some extent. This was critically caused by the rural–urban migration amongst the working class in search of employment and wage labor. As a result, agricultural practices in most rural areas remain under the dependent class (children and old people) who are less energetic. To some extent, agroforestry was less affected by this migration compared to other CA forms as it mostly involved perennial crops that would not demand frequent labor.
Through interviews and discussions, most agricultural officers acknowledged proposing several CA practices, however, the financial constraint has been a major limiting factor to regularly visit farms, especially in remote areas. Despite the financial constraints, this study realized that the adoption of CA practices may solely depend on skills, interest, awareness and priorities of the adopters (i.e., early, moderate and laggards). However, to enhance CA adoption in the study area and the country at large, agricultural and extension officers should pay adequate visit to advise farmers on different agronomic practices that can optimize yields and elevate environmental services.
This is important because, at a farm level, some farmers blamed agricultural experts for not giving substantial extension services. This means, despite the farmers’ willingness to adopt the CA, if there will be no extension support, obviously, the adoption rate will slow down. This is because their indigenous knowledge may not be enough to cope with climate change. During the series of discussions, an anonymous farmer claimed that agriculture is meant for the poor. He further justified that most of the officers/experts had not engaged in it, instead they preferred other clerical jobs. His claims were associated with numerous government reports that indicated that 70% of agricultural industry in the country was under smallholder farmers who were mostly economically deprived.
Based on such premise, this study suggests that agricultural and extension officers should instill a sense of awareness and confidence among farmers thatagriculture is a respectable industry and, thus, anyone can do it, regardless his/her economic status. This can contempt the long standing local joke “mkulima”, a Swahili word that means “poor” as associated to agriculture. Here, they mean that agriculture is for the lowest class and jobless people. The government is always asking and requesting jobless people, especially in towns, to join agriculture industry for their survival and development.

4.2. Crop Yields

The influence of CA on crop production has been substantially revealed. From 2000 to 2015, the yields of sorghum and millet were significant greater under CA (1.5–1.8 ha−1 and 1.3–1.6 t ha−1, respectively) than under no-CA (0.2–0.5 n ha−1 and 0.5–0.7 t ha−1, respectively) (Figure 8). Of the two crops, sorghum yields had significant variation between the farms with (1.8 t ha−1) and without CA (0.2 t ha−1) compared to millet, i.e. 1.6 t ha−1 with CA and 0.5 t ha−1 without CA (Figure 8).
Thus, for optimal yields, the CA practices need to be more integrated with sorghum than with millet production. These results agree with and Kimaro et al. [26], Glaser et al. [37] and Dixon et al. [52]. The yields increase has been significant in limiting the level of hunger in the area. Moreover, CA gave optimal yields when integrated with irrigation and organic fertilization. Thus, the incorporation of these aspects in CA is worthwhile to increase crop yields in the study area.
On the other hand, the increasing demand of organic food at the global market may increase the adoption of CA (organic farming) in various countries [33]. While other parts of the globe have considerably adopted CA, Africa has not yet done well in that aspect (see Figure 1). Thus, compelling measures and emphasis are required in the continent to increase this adaption [44,45]. This will enhance the adaptive capacities among the smallholder farmers and limit the level of vulnerability from the global and local environmental change [15,16,45]. Thus, while proposing the increased CA adoption at a local level and in various agroecological zones, we also advocate its adoption at national, regional, continental and global levels because mitigation measures can have both global and local impacts [52,53,54,55,56,57,58,59,60,61].

4.3. Irrigation

With respect to the semi-arid areas, CA observed to work properly through irrigation. It mainly safeguards soil fertility and moisture, thus improving agroecosystems tenable for crop production and environmental conservation. Even though irrigation was limited to small area (about 5814 ha, i.e., <5% of the total area) located near the Mseta, Mzeru, Mlanga, Ikoka and Chelwe Rivers (Figure 3), it had significant contribution to yields and conservation.
However, Water User Association (UWA) and Irrigators Organization (IO), which control irrigation operation in the area, have encountered multiple challenges: water use conflicts, destruction of irrigation infrastructures and financial constraints. All these challenges posed some consequences.
The small area under irrigation denies the exploitation of a wide range potentials associated with irrigation. This problem is also acute at a national level where less than 4% of the total irrigable land potential has been harnessed [53,54]. According to the zonal irrigation engineers, the area has abundant ground water that could be the best option, but this potential has not yet made into use.
It was further clarified that various geophysical surveys have indicated that most ground water is located at less than 60 m depth. This is contrary to countries such as China where this depth can exceed 200 m [52]. As a way forward, the substantial investment in technology is very important for exploitation of both ground and rain water.
During rains, some farmers collect running water from seasonal rivers for spate irrigation. This water is intended to be reserved and used during water stress. However, due to weak infrastructure, the loss of this water is critically high. Thus, suitable mechanisms are required to boost their local innovations.

4.4. Fertilization

Fertilization has significant contribution to CA as in increases crop yields. CA gives optimal advantages when integrated with fertilizations [26,61]. The present study found that the majority (62%) of farmers did not use any fertilization on their farms; 80% of those (38%) applied organic fertilizers while few (20%) used chemical fertilizers.
It was realized that most organic fertilizers come from straw and animal manure. Most animal manure comes from goats, sheep, cattle, pig and donkeys. In terms of amounts, most farmers were assertive that, for optimal crops yields, they required at least 5000–10,000 kg ha−1 of organic fertilizers whose fertility could remain in the farm for five years. However, organic fertilization was insignificantly applied in the study area.
On the other hand, chemical fertilization such as DAP (Diammonium Phosphate), NPK (Nitrogen Phosphorus Potassium), SA (Ammonium Sulphate), TSP (Triple Super Phosphate) and UREA were applied in few areas especially under irrigation schemes. This is because the Ministry of Agriculture, Livestock and Fishery (MALF) recommends chemical fertilizers under irrigation schemes, as it does better under constant soil moisture than in mere drought areas (i.e., where evaporation is high).
The MALF has been providing inadequate share of chemical fertilizers to Dodoma Region, i.e., 1 × 104. This meant that only 4000 ha could be fertilized in the whole region. In that share, Kongwa District received <2000 vouchers that fertilized very few hectares. However, it increased crop yields to 7.2 t ha−1 (from less than 3 t ha−1 under no-CA).
Overall, this study realized that, for a successful CA, there is an immediate need to attach irrigation and fertilization for sustainable conservation of soil moisture and fertility. It was also realizable that CA offered economic and socio-ecological advantages to the farmers depending on the biophysical environment. Areas under irrigation schemes that received chemical fertilization provided favorable conditions for crops production.

4.5. Environmental Sustainability

The study by FAO [46] has showed that chromic luvisols with a sandy loam texture is the dominant soil type in the area. Soil types are among the most important factors that control several biological processes in a particular locality. It was observed that silt contents in different farms were not significantly different (p > 0.05) and ranged between 170 and 255 g kg−1 soil while bulk density was 1.11 and 1.35 Mg m−3 with and without CA, respectively [47]. Soil carbon ranged from 1 to 1.22 Mg C ha−1 (0–20 cm deep) under CA and declined in farms under no-CA, while calcium, magnesium and sodium ranged from 0.5 to 4 Mg ha−1 under CA [37].
In most areas, the soils had neutral pH values ranging 5.40–6.10 on the top soils [46,47,48]. In addition, it had moderate high cation exchange capacity and high base saturation [48,49,50,51,52,53,54]. The CA practices appeared to optimize important soil nutrients, i.e., soil quality in the area. Accordingly, soil nutrients were significantly greater (p < 0.05) under farms with CA than under no-CA due to such soil amendments. The former was better in the optimization of environmental sustainability than the latter.
Many studies conducted in similar agroecosystems, i.e., climate and soil types, have also endorsed the positive roles of soil fertility and moisture (under CA) in elevating the biological functions of microorganisms in balancing the ecosystems [29,46,47,48]. Mycorrhiza fungi also operate well under proper soil organic managements where they then increase the capacity of nutrients uptake and resistance to pathogens by plants [48]. This situation improves the interaction between roots and microorganisms
Moreover, a study by Haoa et al. [29] further indicated that no-tillage, crops rotation, mulching and agroforestry were the sinks of the top three greenhouse gases, i.e., carbon dioxide (CO2), methane (NH4) and nitrous oxide (N2O), and, thus, confer adaptation and mitigation. It is thus obvious that the CA practices have multiple benefits to environmental sustainability [30,55,56,57,58,59]. Hence, CA practices have significant contributions to sustainable environmental conservation focusing on lithosphere, hydrosphere, biosphere and atmosphere.
As a result, it is advisable to build the capacity of the smallholder farmers (who form 70% of Tanzanian agriculture) so that they can effectively integrate AC in their farming. If this integration reached at least 20% of the total farm size of every household, it would definitely increase crop yields and environmental services [33,55,56,57,58,59,60,61]. This would have a long-term positive impact in serving the present needs of the people and environment without compromising the needs of future generations [55,56,57,58,59,60,61].

5. Conclusions

This study sought to explore the adoption rate of CA in the Kongwa District, a semi-arid agroecological zone in the central Tanzania. It accepted alternative hypothesis H1 because CA adoption was greatly influenced by the famers’ desire to achieve higher yields. Besides, the retention of soil moisture, conservation of soil fertility, and control of soil erosion were among the contributing factors that attracted farmers to adopt CA. In both villages, it was realized that there was correlation (p < 0.05) between the size of land under CA and the total land under farming (Table 2).
Time factor was another determinant factor for the adoption trend as there has been an increase in hectares under CA over time. However, the present study showed that CA is insignificantly practiced in study area, as <10% of households were involved. Among the CA practices, mulching appeared to receive high attention to adopters as its adoption was determined at the coefficient of R2 = 0.95. Little tillage appeared to dominate others in total hectares (about 6000 ha). In addition, CA practices appeared to be more beneficial when supported with organic fertilizations and irrigation.
Animal manure and straw were the main source of organic fertilization. Further, this study found the significance difference between the areas with and without CA. Crops yields and environmental sustainability were better with CA than without it (Figure 8). Therefore, despite of being understandable that CA could improve the agricultural systems, it is recommendable to quantify such environmental potentials. The present study proposes the adoption of CA practices in various agroecological zones in Tanzania to manage agricultural soils, and attain socio-economic and ecological advantages. This is because CA confers adaptation and mitigation advantages. Likewise, effective livestock keeping should be integrated into various CA practices for mutual benefits.
Subsequently, planners, policy makers, agricultural experts and other agricultural stakeholders and practitioners should consider these findings as a baseline for their future endeavors. Lastly, we call for more proactive interventions and efforts from different stakeholders to join in this agenda. These efforts should mostly target areas with extreme weather stresses.
There are several research priorities for further investigations to tackle: (1) characterization of people who are involved in CA (small scale or large scale) and the policy implications; (2) the drivers that can influence CA adoption in small-scale farming; (3) how climate variability influences positively or negatively the adoption of CA (especially during the extreme wet period as compared to the extreme dry period); and (4) how crops yield harvested from CA contribute to food security and economic welfare.

Author Contributions

All authors designed the study. Z.H. and M.Y.M. collected the data in the study area, analyzed the data and wrote a first draft. X.H. revised the manuscript to get a final draft for submission. All authors read and approved the final version for submission.

Acknowledgments

This study was supported by the College of Resources and Environment, Southwest University. Apart from the funder, the authors convey thanks to the research assistants for their good job in the field. Other appreciations are routed to the local government authorities of Dodoma Region and Kongwa District in Tanzania for permitting the undertakings of this study in their administrative areas. The authors are also thankful to the three anonymous reviewers for their constructive comments and insights during review process.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Jenrich, M. Potential of precision conservation agriculture as a means of increasing productivity and incomes for smallholder farmers. J. Soil Water Conserv. 2011, 66, 171A–174A. [Google Scholar] [CrossRef]
  2. Vanlauwe, B.; Wendt, J.; Giller, K.E.; Corbeels, M.; Gerard, B.; Nolte, C. A fourth principle is required to define conservation agriculture in sub-Saharan Africa: The appropriate use of fertilizer to enhance crop productivity. Field Crop Res. 2014, 155, 10–13. [Google Scholar] [CrossRef]
  3. Baker, J.M.; Ochsner, T.E.; Venterea, R.T.; Griffis, T.J. Tillage and soil carbon sequestration—What do we really know? Agric. Ecosyst. Environ. 2007, 118, 1–5. [Google Scholar] [CrossRef]
  4. Lal, R. World Soils and the Carbon Cycle in Relation to Climate Change and Food Security; Carbon Management and Sequestration Center, The Ohio State University: Columbus, OH, USA, 2012. [Google Scholar]
  5. Penfold, C. Grape and Wine Research and Development Corporation: “Herbicide Reduction Strategies for Winegrape Production”; University of Adelaide: Adelaide, Australia, 2003. [Google Scholar]
  6. Thierfelder, C.; Cheesman, S.; Rusinamhodzi, L. A comparative analysis of conservation agriculture systems: Benefits and challenges of rotations and intercropping in Zimbabwe. Field Crops Res. 2012, 137, 237–250. [Google Scholar] [CrossRef]
  7. Ngwira, A.; Johnsen, F.H.; Aune, J.B.; Mekuria, M.; Thierfelder, C. Adoption and extent of conservation agriculture practices among smallholder farmers in Malawi. J. Soil Water Conserv. 2014, 69, 107–119. [Google Scholar] [CrossRef] [Green Version]
  8. Mattee, A.Z. The adoption of agricultural innovations by small farmers in Tanzania: An analysis of the research needs. Afr. Study Monogr. 1994, 15, 167–176. [Google Scholar]
  9. Rogers, E.M. Diffusion of Innovations; Free Press: New York, NY, USA, 1995. [Google Scholar]
  10. Mwaseba, D.L.; Kaarhus, R.; Johnsen, F.H.; Mvena, Z.S.K.; Matte, A.Z. Beyond adoption/rejection of agricultural innovations. Outlook Agric. 2006, 35, 263–272. [Google Scholar] [CrossRef]
  11. Delgado, J.; Groffman, P.; Nearing, M.; Goddard, T.; Reicosky, D.; Lal, R.; Kitchen, N.R.; Rice, C.W.; Salon, P.; Towery, D. Conservation practices to mitigate and adapt to climate change. J. Soil Water Conserv. 2011, 66, 118A–129A. [Google Scholar] [CrossRef]
  12. Bennett, R.G.; Ryan, M.H.; Colmer, T.D.; Real, D. Prioritization of novel pasture species for 490 use in water-limited agriculture: A case study of Cullen in the Western Australian wheatbelt. Genet. Resour. Crop. Evol. 2011, 58, 83–100. [Google Scholar] [CrossRef]
  13. Malviya, S.; Priyanka, N.; Irfan-Ullah, M.; Davande, S.; Joshi, P.K. Distribution potential of 563 simarouba glauca under climate change—Strategizing rural livelihood adaptation. Int. J. Geoinform. 2013, 9, 31–37. [Google Scholar]
  14. Zhu, J.; Hu, H.; Tao, S.; Chi, X.; Li, P.; Jiang, L.; Ji, C.; Zhu, J.; Tang, Z.; Pan, Y.; et al. Carbon stocks and changes of dead organic matter in China’s forests. Nat. Commun. 2017, 8. [Google Scholar] [CrossRef] [PubMed]
  15. Paavola, J. Livelihoods, vulnerability and adaptation to climate change in Morogoro, Tanzania. Environ. Sci. Policy 2008, 11, 642–654. [Google Scholar] [CrossRef]
  16. Challinor, A.J.; Watson, J.; Lobell, D.B.; Howden, S.M.; Smith, D.R.; Chhetri, N. A meta-analysis of crop yield under climate change and adaptation. Nat. Clim. Chang. 2014, 4, 287–291. [Google Scholar] [CrossRef]
  17. FAO. Investing in Sustainable Agricultural Intensification: The Role of Conservation Agriculture. A Framework for Action; FAO: Rome, Italy, 2008. [Google Scholar]
  18. Field, C.B.; Barros, V.R.; Estrada, R.C.; Genova, B.; Girma, E.S.; Kissel, A.N.; Levy, S.; MacCracken, P.R.; Mastrandrea, L.L. (Eds.) Intergovernmental Panel on Climate Change (2014a) Climate Change Impacts. Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. In Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2014. [Google Scholar]
  19. Nyong, A.; Francis, A.; Osman-Elasha, B. The value of indigenous knowledge in climate change mitigation and adaptation strategies in the African Sahel. Mitig. Adapt. Strat. Glob. Chang. 2007, 12, 787–797. [Google Scholar] [CrossRef]
  20. Neufeldt, H.; Jahn, M.; Campbell, B.M.; Beddington, J.R.; DeClerck, F.; de Pinto, A.; Gulledge, J.; Hellin, J.; Herrero, M.; Jarvis, A.; et al. Beyond climate smart agriculture: Toward safe operating spaces for global food systems. Agric. Food Secur. 2013, 2, 12. [Google Scholar] [CrossRef] [Green Version]
  21. Duru, M. How to implement biodiversity-based agriculture to enhance ecosystem services. Agron. Sustain. Dev. 2015, 35, 1259–1281. [Google Scholar] [CrossRef]
  22. Plaza-Bonilla, D.; Luis-Arrúe, J.; Cantero-Martínez, C.; Fanlo, R.; Álvaro-Fuentes, A. Carbon management in dryland agricultural systems. Agron. Sustain. Dev. 2015, 35, 1319–1334. [Google Scholar] [CrossRef]
  23. Ye, L.; Xiong, W.; Li, Z.; Yang, P.; Wu, W.; Yang, G.; Fu, Y.; Zou, J.; Chen, Z.; Van Ranst, E.; et al. Climate change impact on China food security in 2050. Agron. Sustain. Dev. 2013, 33, 363–374. [Google Scholar] [CrossRef] [Green Version]
  24. Rowhani, P.; Lobell, D.B.; Linderman, M.; Ramankutty, N. Climate variability and crop production in Tanzania. Agric. For. Meteorol. 2011, 15, 449–460. [Google Scholar] [CrossRef]
  25. Ahmed, S.; Deffenbaugh, N.; Hertel, T.; Lobell, D.; Ramankutty, N.; Rios, A.; Rowhani, P. Climate volatility and poverty vulnerability in Tanzania. Glob. Environ. Chang. 2011, 21, 46–55. [Google Scholar] [CrossRef]
  26. Kimaro, A.; Mpanda, M.; Rioux, J.; Aynekulu, E.; Shaba, S.; Thiong’o, M.; Mutuo, P.; Abwanda, S. Is conservation agriculture ‘climate-smart’ for maize farmers in the highlands of Tanzania? Nutr. Cycl. Agroecosyst. 2015, 3, 217–228. [Google Scholar] [CrossRef]
  27. Vanlauwe, B. Integrated soil fertility management research at TSBF: The framework, the principles, and their application. In Managing Nutrient Cycles to Sustain Soil Fertility in Sub-Saharan Africa; Bationo, A., Ed.; Academy Science Publishers: Nairobi, Kenya, 2004. [Google Scholar]
  28. Hartemink, A.E.; Veldkamp, T.; Bai, Z. Land cover change and soil fertility decline in tropical regions. Turk. J. Agric. For. 2008, 32, 195–213. [Google Scholar]
  29. Haoa, Q.; Jianga, C.; Chaia, X.; Huanga, Z.; Fana, Z.; Xiea, D.; He, X.H. Drainage, no-tillage and crop rotation decreases annual cumulative emissions of methane and nitrous oxide from a rice field in Southwest China. Agric. Ecosyst. Environ. 2016, 233, 270–281. [Google Scholar] [CrossRef]
  30. Lienhard, P.; Tivet, F.; Chabanne, A.; Dequiedt, S.; Lelièvre, M.; Sayphoummie, S.; Leudphanane, B.; Prévost-Bouré, N.C.; Séguy, L.; Maron, P.-A.; et al. No-till and cover crops shift soil microbial abundance and diversity in Laos tropical grasslands. Agron. Sustain. Dev. 2013, 33, 375–384. [Google Scholar] [CrossRef] [Green Version]
  31. Duru, M.; Therond, O.; Fares, M. Designing agroecological transitions. Agron. Sustain. Dev. 2015, 35, 1237–1257. [Google Scholar] [CrossRef]
  32. Harvey, C.A.; Chacón, M.; Donatti, C.I.; Garen, E.; Hannah, L.; Andrade, A.; Bede, L.; Brown, D.; Calle, A.; Chará, J.; et al. Climate-smart landscapes: Opportunities and challenges for integrating adaptation and mitigation in tropical agriculture. Conserv. Lett. 2014, 7, 77–90. [Google Scholar] [CrossRef]
  33. FAO. Climate-Smart Agriculture Sourcebook; FAO: Rome, Italy, 2013. [Google Scholar]
  34. Lal, R. Sequestering carbon and increasing productivity by conservation agriculture. J. Soil Water Conserv. 2015, 70, 55A–62A. [Google Scholar] [CrossRef]
  35. Mohammadshirazi, F.; Brown, V.K.; Heitman, J.L.; McLaughlin, R.A. Effects of tillage and compost amendment on infiltration in compacted soils. J. Soil Water Conserv. 2016, 71, 443–449. [Google Scholar] [CrossRef]
  36. Hartemink, A.E. Soils Fertility Decline in Some Major Soil Groupings under Permanent Cropping in Tanga Region, Tanzania. Geoderma 1997, 75, 215–229. [Google Scholar] [CrossRef]
  37. Glaser, B.; Lehmann, J.; Führböter, M.; Solomon, D.; Zech, W. Carbon and nitrogen mineralization in cultivated and natural savanna soils of Northern Tanzania. Biol. Fertil. Soils 2001, 33, 301–309. [Google Scholar] [CrossRef]
  38. Sen, A.K. Development as Freedom; Knopf, A., Ed.; Oxford University Press: New York, NY, USA, 1999. [Google Scholar]
  39. Sosovele, H.; Schechambo, F.; Kisanga, D. Rethinking Natural Resource Degradation in Semi-Arid Sub-Saharan Africa: The Case of Semi-Arid Tanzania; Overseas Development Institute (ODI): London, UK, 1999. [Google Scholar]
  40. Kahimba, F.C.; Mutabazi, K.D.; Tumbo, S.D.; Masuki, K.F.; Mbungu, W.B. Adoption and Scaling-Up of Conservation Agriculture in Tanzania: Case of Arusha and Dodoma Regions. Nat. Resour. 2014, 5, 161–176. [Google Scholar] [CrossRef]
  41. FAO. Conservation Agriculture for SARD and Food Security in Southern and Eastern Africa. Terminal Report, Kenya and Tanzania; FAO: Rome, Italy, 2006. [Google Scholar]
  42. FAO. Conservation Agriculture and Sustainable crop Intensification. In Karatu District, Tanzania. Integrated Crop Management; FAO: Rome, Italy, 2012; Volume 15-2012. [Google Scholar]
  43. Heckman, J.J. Sample selection bias as a specification error. Econometrica 1979, 47, 153–161. [Google Scholar] [CrossRef]
  44. Mkonda, M.Y.; He, X.H. Conservation Agriculture in Tanzania. Sustain. Agric. Rev. 2017, 22, 309–324. [Google Scholar]
  45. Mkonda, M.Y.; He, X.H. Yields of the Major Food Crops: Implications to Food Security and Policy in Tanzania’s Semi-arid Agro-ecological Zone. Sustainability 2017, 9, 1490. [Google Scholar] [CrossRef]
  46. FAO. FAO/Unesco Soil Map of the World, Revised Legend, with Corrections and Updates. (World Soil Resources Report 60); FAO: Rome, Italy, 1988; reprinted with updates as Technical Paper 20; ISRIC: Wageningen, The Netherlands, 1997. [Google Scholar]
  47. Bationo, A.; Kihara, J.; Vanlauwe, B.; Waswa, B.; Kimetu, J. Soil organic carbon dynamics, functions and management in West African agro-ecosystems. Agric. Syst. 2006, 94, 13–25. [Google Scholar] [CrossRef]
  48. Solomon, D.; Lehmann, J.; Zech, W. Land use effects on soil organic matter properties of chromic Luvisols in the semiarid tropics: Carbon, nitrogen, lignin and carbohydrates. Agric. Ecosyst Environ. 2000, 78, 203–213. [Google Scholar] [CrossRef]
  49. Cramb, R.A.; Purcell, T.; Ho, T.C.S. Participatory assessment of rural livelihoods in the Central Highlands of Vietnam. Agric. Syst. 2004, 81, 255–272. [Google Scholar] [CrossRef]
  50. Brown, M.E. Assessing natural resource management challenges in Senegal using data from participatory rural appraisals and remote sensing. World Dev. 2006, 34, 751–767. [Google Scholar] [CrossRef]
  51. Humphrey, W.K.; Kimberly, E.M. Participatory resource mapping for adaptive collaborative management at Mt. Kasigau. Landsc. Urban Plan. 2007, 82, 145–158. [Google Scholar]
  52. Dixon, J.; Gulliver, A.; Gibbon, D. Farming Systems and Poverty; FAO and World Bank: Rome, Italy, 2001. [Google Scholar]
  53. URT. Tanzania National Sample Census of Agriculture 2007/2008 Small Holder Agriculture: Regional Report—Singida Region (Volume Vm); Ministry of Agriculture, Food Security and Cooperatives, Ministry of Livestock Development and Fisheries, Ministry of Water and Irrigation, Ministry of Agriculture, Livestock and Environment: Zanzibar, Tanzania, 2012.
  54. United Republic of Tanzania (URT). National Agriculture Policy; Government Publishing Press: Dar es Salaam, Tanzania, 2013.
  55. Mkonda, M.Y.; He, X.H. Are Rainfall and Temperature Really Changing? Farmer’s Perceptions, Meteorological Data, and Policy Implications in the Tanzanian Semi-Arid Zone. Sustainability 2017, 9, 1412. [Google Scholar] [CrossRef]
  56. Mkonda, M.Y.; He, X.H.; Sandell, F.E. Comparing smallholder farmers’ perception of climate change with meteorological data: Experiences from seven agro-ecological zones of Tanzania. Weather Clim. Soc. J. 2018. [Google Scholar] [CrossRef]
  57. Mkonda, M.Y.; He, X.H. Climate Variability and Crop Yields Synergies under Rain-fed Agriculture in Tanzania’s Semi-arid Agro-ecological Zone. Ecosyst. Health Sustain. 2018, 4, 59–72. [Google Scholar] [CrossRef]
  58. Mkonda, M.Y.; He, X.H. Vulnerability Assessment of the Livelihoods in Tanzania’s Semi-Arid Agro-Ecological Zone under Climate Change Scenarios. Climate 2018, 6, 27. [Google Scholar] [CrossRef]
  59. Mkonda, M.Y.; He, X.H. Accumulation of SOC under organic and no-fertilizations and its influence to crop yields in Tanzania’s semi-arid zone. Ecosyst. Health Sustain. 2018, 4, 34–47. [Google Scholar]
  60. Servadio, P.; Bergonzoli, S.; Beni, C. Soil Tillage Systems and Wheat Yield under Climate Change Scenarios. Agronomy 2016, 6, 43. [Google Scholar] [CrossRef]
  61. Servadio, P.; Bergonzoli, S.; Toderi, M. Soil mapping to assess workability in central Italy as climate change adaptation techniques. Glob. NEST J. 2014, 16, 229–239. [Google Scholar]
Sustainability 10 01430 g001 550
Figure 1. Development of conservation agriculture over the last 20 years by world region in total area (ha) and as an average percentage across the adopting countries of the respective region. African countries seem to adopt conservation agriculture slowly. It needs more emphasis for a good take off. Afterwards, the continent will increase crop yields in terms of quantity and quality as well as conserving the ecosystems (Source: Adopted from FAO [17]).
Figure 1. Development of conservation agriculture over the last 20 years by world region in total area (ha) and as an average percentage across the adopting countries of the respective region. African countries seem to adopt conservation agriculture slowly. It needs more emphasis for a good take off. Afterwards, the continent will increase crop yields in terms of quantity and quality as well as conserving the ecosystems (Source: Adopted from FAO [17]).
Sustainability 10 01430 g001
Sustainability 10 01430 g002 550
Figure 2. Conceptual framework for conservation agriculture (Source: Adopted from Mkonda and He [44]).
Figure 2. Conceptual framework for conservation agriculture (Source: Adopted from Mkonda and He [44]).
Sustainability 10 01430 g002
Sustainability 10 01430 g003 550
Figure 3. Location and sampling positions of the selected villages in the Kongwa District. The study sites are also denoted with latitude and longitudes degrees (Source: IRA GIS Lab, UDSM: 2016).
Figure 3. Location and sampling positions of the selected villages in the Kongwa District. The study sites are also denoted with latitude and longitudes degrees (Source: IRA GIS Lab, UDSM: 2016).
Sustainability 10 01430 g003
Sustainability 10 01430 g004 550
Figure 4. Variation in land use or farming systems from 1995 to 2015 (five-year averaged data) in Mnyakongo and Ugogoni villages of the Kongwa District, a semi-arid zone in central Tanzania. Data (means ± SD, n = 5) with different letters denote significant differences between averaged years for the same CA practice (a, b, c, d, e) and between different CA practices for the same averaged year (w, x, y, z) at p < 0.05. Note: Little tillage involves shallow cultivation (minimum tillage) of the farm (i.e., non-conventional). Source: Field Survey Data, 2016.
Figure 4. Variation in land use or farming systems from 1995 to 2015 (five-year averaged data) in Mnyakongo and Ugogoni villages of the Kongwa District, a semi-arid zone in central Tanzania. Data (means ± SD, n = 5) with different letters denote significant differences between averaged years for the same CA practice (a, b, c, d, e) and between different CA practices for the same averaged year (w, x, y, z) at p < 0.05. Note: Little tillage involves shallow cultivation (minimum tillage) of the farm (i.e., non-conventional). Source: Field Survey Data, 2016.
Sustainability 10 01430 g004
Sustainability 10 01430 g005 550
Figure 5. Adoption rate of crop rotation (A) and little tillage (B) as CA practices in the Mnyakongo and Ugogoni villages of the Kongwa District, a semi-arid zone in central Tanzania. Source: Field Survey Data, 2016.
Figure 5. Adoption rate of crop rotation (A) and little tillage (B) as CA practices in the Mnyakongo and Ugogoni villages of the Kongwa District, a semi-arid zone in central Tanzania. Source: Field Survey Data, 2016.
Sustainability 10 01430 g005
Sustainability 10 01430 g006 550
Figure 6. Adoption rate of mulching (A) and agroforestry (B) as CA practices in the study area. Source: Field Survey Data, 2016.
Figure 6. Adoption rate of mulching (A) and agroforestry (B) as CA practices in the study area. Source: Field Survey Data, 2016.
Sustainability 10 01430 g006
Sustainability 10 01430 g007 550
Figure 7. Reasons for adopting CA in the Mnyakongo and Ugogoni villages of the Kongwa District, a semi-arid zone in central Tanzania. Different letters (a, b, c, d) denote significant differences between different reasons for the CA adoption. Source: Field Survey Data, 2016.
Figure 7. Reasons for adopting CA in the Mnyakongo and Ugogoni villages of the Kongwa District, a semi-arid zone in central Tanzania. Different letters (a, b, c, d) denote significant differences between different reasons for the CA adoption. Source: Field Survey Data, 2016.
Sustainability 10 01430 g007
Sustainability 10 01430 g008 550
Figure 8. Variation in crop yields under conservation and no-conservation from 2000 to 2015 (five-year averaged data) in the Mnyakongo and Ugogoni villages of the Kongwa District, a semi-arid zone in central Tanzania. Vertical bars indicate the standard error of the means (n = 4). Source: Field Survey Data, 2016.
Figure 8. Variation in crop yields under conservation and no-conservation from 2000 to 2015 (five-year averaged data) in the Mnyakongo and Ugogoni villages of the Kongwa District, a semi-arid zone in central Tanzania. Vertical bars indicate the standard error of the means (n = 4). Source: Field Survey Data, 2016.
Sustainability 10 01430 g008
Table
Table 1. Summary of questionnaires administration and PRAs tools in the sampled villages.
Table 1. Summary of questionnaires administration and PRAs tools in the sampled villages.
MnyakongoUgogoni
Questionnaires (n = 400)Total HH (2050)Total HH (2080)
 Number of households interviewed200200
 Crop farmers (%)7080
 Livestock farmers (%)1010
 Both crop and livestock farmers (%)2010
Focus group discussion (n = 30)
 Crop farmers1210
 Livestock farmers02
 Both crop and livestock farmers33
Interview (n = 20)
 Crop farmers89
 Livestock farmers00
 Both crop and livestock farmers21
* PRAs = Participatory Rural Appraisal; * HH = Household heads; * n = Number of observations. Source: Field Survey Data, 2016.
Table
Table 2. Current and estimated land under conservation agriculture in the sampled villages.
Table 2. Current and estimated land under conservation agriculture in the sampled villages.
VillageTotal Cultivated Land (ha)Average-Landholding Size (ha)Average Land under CA (ha)Adoption Rate (%)Estimated Land under CA (ha)
Mnyakongo86003.50.348.790
Ugogoni92004.10.3811.4105
* The adoption rate was from the agricultural officers and others from local farmers in a 2016 field survey. Source: Field Survey Data, 2016.
Table
Table 3. Responses (%) of effectiveness to the conservation agriculture practices by local farmers in the sampled villages (n = 200 in each village) during a 2016 field survey.
Table 3. Responses (%) of effectiveness to the conservation agriculture practices by local farmers in the sampled villages (n = 200 in each village) during a 2016 field survey.
Conservation MethodsVery EffectiveModerate EffectiveNot SureNot Effective
MnUgMnUgMnUgMnUg
Agroforestry181976770064
Crop rotation717322257200
Little tillage15166771131053
Mulching272962649532
Abbreviations: Mn, Mnyakongo, and Ug, Ugogoni; Source: Field Survey Data, 2016.
Table
Table 4. Established hypotheses as expressed in percent (from participatory research appraisals).
Table 4. Established hypotheses as expressed in percent (from participatory research appraisals).
VillageOptional YieldsSoil MoistureSoil FertilityErosion ControlReduced LaborOthersTotal
Mnyakongo217885249
Ugogoni198875251
40151615104100
Source: Data are from a 2016 field survey in the Mnyakongo and Ugogoni villages of the Kongwa District, a semi-arid zone in central Tanzania.

Share and Cite

MDPI and ACS Style

Hong, Z.; Mkonda, M.Y.; He, X. Conservation Agriculture for Environmental Sustainability in A Semiarid Agroecological Zone under Climate Change Scenarios. Sustainability 2018, 10, 1430. https://doi.org/10.3390/su10051430

AMA Style

Hong Z, Mkonda MY, He X. Conservation Agriculture for Environmental Sustainability in A Semiarid Agroecological Zone under Climate Change Scenarios. Sustainability. 2018; 10(5):1430. https://doi.org/10.3390/su10051430

Chicago/Turabian Style

Hong, Zhang, Msafiri Y. Mkonda, and Xinhua He. 2018. "Conservation Agriculture for Environmental Sustainability in A Semiarid Agroecological Zone under Climate Change Scenarios" Sustainability 10, no. 5: 1430. https://doi.org/10.3390/su10051430

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop