Drivers Of Land Use Change And The Carbon
Carbon dioxide emissions associated with land-use change are estimated to be 1.6 [0.5 to 2.7] GtC (5.9 [1.8 to 9.9] GtCO 2) per year over the 1990s, although these estimates have a large uncertainty.
The indirect land use change impacts of, also known as ILUC, relates to the unintended consequence of releasing more due to around the world induced by the expansion of croplands for or production in response to the increased global demand for biofuels. As farmers worldwide respond to higher crop prices in order to maintain the global food supply-and-demand balance, pristine lands are cleared to replace the food crops that were diverted elsewhere to.
Because natural lands, such as and, store carbon in their soil and as plants grow each year, clearance of wilderness for new farms translates to a net increase in. Due to this change in the of the soil and the biomass, indirect land use change has consequences in the GHG balance of a biofuel. Other authors have also argued that indirect land use changes produce other significant social and environmental impacts, affecting biodiversity, water quality, worker migration, and community and cultural stability. Contents. History The estimates of for a given biofuel depend on the assumptions regarding several variables.
As of 2008, multiple studies had found that, and produce lower greenhouse gas emissions than. None of these studies, however, considered the effects of indirect land-use changes, and though land use impacts were acknowledged, estimation was considered too complex and difficult to model.
A controversial paper published in February 2008 in by a team led by Searchinger from concluded that such effects offset the (positive) direct effects of both corn and cellulosic ethanol and that Brazilian sugarcane performed better, but still resulted in a small carbon debt. After the Searchinger team paper, estimation of from ILUC, together with the debate, became one of the most contentious, debated in the, and public letters from the, and the ethanol industry, both American and Brazilian. This controversy intensified in April 2009 when the (CARB) set rules that included ILUC impacts to establish the that entered into force in 2011.
In May 2009 (EPA) released a for implementation of the 2007 modification of the (RFS). EPA's proposed regulations also included ILUC, causing additional controversy among ethanol producers. EPA's February 3, 2010 final rule incorporated ILUC based on modelling that was significantly improved over the initial estimates. The program requires the (RFA) to report potential indirect impacts of biofuel production, including indirect land use change or changes to food and other commodity prices.
A July 2008 RFA study, known as the Gallager Review, found several risks and uncertainties, and that the 'quantification of GHG emissions from indirect land-use change requires subjective assumptions and contains considerable uncertainty', and required further examination to properly incorporate indirect effects into calculation methodologies. A similarly cautious approach was followed by the. In December 2008 the adopted more stringent sustainability criteria for biofuels and directed the to develop a methodology to factor in GHG emissions from indirect land use change. Studies and controversy –. UK figures for the of bioethanol and. Graph assumes all bioethanols are burnt in country of origin and prior cropland was used to grow feedstock. No ILUC effects were included.
Before 2008, several ('Well to Wheels' or WTW) studies had found that reduced transport-related greenhouse gas emissions. In 2007 a team led by Farrel evaluated six previous studies, concluding that corn ethanol reduced GHG emissions by only 13 percent. However, 20 to 30 percent reduction for corn ethanol, and 85 to 85 percent for, both figures estimated by Wang from, are more commonly cited. Wang reviewed 22 studies conducted between 1979 and 2005, and ran simulations with Argonne's. These studies accounted for direct land use changes. Several studies of Brazilian sugarcane ethanol showed that sugarcane as feedstock reduces GHG by 86 to 90 percent given no significant land use change.
Estimates of depend on, agricultural practices, power sources for ethanol distilleries and the energy efficiency of the distillery. None of these studies considered ILUC, due to estimation difficulties. Preliminary estimates by Delucchi from the, suggested that carbon released by new lands converted to agricultural use was a large percentage of life-cycle emissions. Searchinger and Fargione studies In 2008 Timothy Searchinger, a from, concluded that ILUC affects the and that instead of saving, both corn and cellulosic ethanol increased carbon emissions as compared to gasoline by 93 and 50 percent respectively. Ethanol from Brazilian sugarcane performed better, recovering initial carbon emissions in 4 years, while U.S. Corn ethanol required 167 years and cellulosic ethanol required a 52 years payback period. The study limited the analysis a 30-year period, assuming that emits 25 percent of the carbon stored in soils and all carbon in plants cleared for cultivation.
Brazil, and were considered among the overseas locations where land use change would occur as a result of diverting U.S. Corn cropland, and it was assumed that new cropland in each of these regions correspond to different types of, or based on the historical proportion of each converted to cultivation in these countries during the 1990s. Summary of Searchinger et al. Comparison of corn ethanol and gasoline emissions with and without land use change ( of released per of energy in fuel) Fuel type (U.S.) Carbon intensity Reduction GHG Carbon intensity + ILUC Reduction GHG Gasoline 92 - 92 - Corn ethanol 74 -20% 177 +93% Cellulosic ethanol 28 -70% 138 +50% Notes: Calculated using default assumptions for 2015 scenario for ethanol in.
Gasoline is a combination of conventional and. Fargione and his team published a separate paper in the same issue of Sciencexpress claiming that clearing lands to produce biofuel feedstock created a carbon deficit. This deficit applies to both direct and indirect land use changes. The study examined six conversion scenarios: to, Brazilian to soybean biodiesel, Brazilian Cerrado to sugarcane ethanol, or lowland to biodiesel, Indonesian or Malaysian tropical rainforest to palm biodiesel, and U.S. Central to corn ethanol. The carbon debt was defined as the amount of CO 2 released during the first 50 years of this process of land conversion. For the two most common ethanol feedstocks, the study found that sugarcane ethanol produced on natural cerrado lands would take about 17 years to repay its carbon debt, while corn ethanol produced on U.S.
Central grasslands would result in a repayment time of about 93 years. The worst-case scenario is converting Indonesian or Malaysian tropical peatland rainforest to palm biodiesel production, which would require about 420 years to repay. Criticism and controversy The Searchinger and Fargione studies created controversy in both the and in. Observed that Searchinger's 'indirect analysis' approach is and can be used to 'prove anything'. Wang and Haq from Argonne National Laboratory claiming: the assumptions were outdated; they ignored the potential of increased efficiency; and no evidence showed that 'U.S.
Corn ethanol production has so far caused indirect land use in other countries.' They concluded that Searchinger demonstrated that ILUC 'is much more difficult to model than direct land use changes'. In his response Searchinger rebutted each technical objection and asserted that '. Any calculation that ignores these emissions, however challenging it is to predict them with certainty, is too incomplete to provide a basis for policy decisions.' Another criticism, by Kline and Dale from, held that Searchinger et al. And Fargione et al.
Do not provide adequate support for their claim that bioufuels cause high emissions due to land-use change', as their conclusions depends on a misleading assumption because more comprehensive field research found that these land use changes '. Are driven by interactions among cultural, technological, biophysical, economic, and demographic forces within a spatial and temporal context rather than by a single crop market'. Fargione et al. Responded in part that although many factors contributed to land clearing, this 'observation does not diminish the fact that biofuels also contribute to land clearing if they are produced on existing cropland or on newly cleared lands'.
Searching disagreed with all of Kline and Dale arguments. Biofuel industry also reacted, claiming that the 'Searchinger study is clearly a 'worst case scenario' analysis.' And that this study 'relies on a long series of highly subjective assumptions.' Searchinger rebutted each claim, concluding that NFA's criticisms were invalid. He noted that even if some of his assumptions are high estimates, the study also made many conservative assumptions. Brazil In February 2010, Lapola estimated that planned expansion of Brazilian sugarcane and soybean biofuel plantations through 2020 would replace, with small direct land-use impact on carbon emissions.
However, the expansion of the rangeland frontier into Amazonian forests, driven by cattle ranching, would indirectly offset the savings. 'Sugarcane ethanol and soybean biodiesel each contribute to nearly half of the projected indirect deforestation of 121,970 km 2 by 2020, creating a carbon debt that would take about 250 years to be repaid.' The research also found that would cause the least land-use changes and associated carbon debt. The analysis also modeled livestock density increases and found that 'a higher increase of 0.13 head per hectare in the average livestock density throughout the country could avoid the indirect land-use changes caused by biofuels (even with soybean as the biodiesel feedstock), while still fulfilling all food and bioenergy demands.'
The authors conclude that intensification of cattle ranching and concentration on oil palm are required to achieve effective carbon savings, recommending closer collaboration between the biofuel and cattle-ranching sectors. The main (UNICA) commented that such studies missed the continuing intensification of cattle production already underway.
A study by Arima et al. Published in May 2011 used spatial regression modeling to provide the first statistical assessment of ILUC for the Brazilian Amazon due to production. Previously, the indirect impacts of soy crops were only anecdotal or analyzed through demand models at a global scale, while the study took a regional approach. The analysis showed a strong signal linking the expansion of soybean fields in settled agricultural areas at the southern and eastern rims of the Amazon basin to pasture encroachments for cattle production on the forest frontier. The results demonstrate the need to include ILUC in measuring the of soy crops, whether produced for biofuels or other end-uses. The Arima study is based on 761 municipalities located in the, and found that between 2003 and 2008, soybean areas expanded by 39,100 km² in the basin's agricultural areas, mainly in. The model showed that a 10% (3,910 km²) reduction of soy in old pasture areas would have led to a reduction in deforestation of up to 40% (26,039 km²) in heavily forested municipalities of the Brazilian Amazon.
The analysis showed that the displacement of cattle production due to agricultural expansion drives land use change in municipalities located hundreds of kilometers away, and that the Amazonian ILUC is not only measurable but its impact is significant. Implementation. Is the main feedstock for the production of. The inclusion of ILUC in the proposed ruling provoked complaints from ethanol and biodiesel producers. Several environmental organizations welcomed the inclusion of ILUC but criticized the consideration of a 100-year payback scenario, arguing that it underestimated land conversion effects.
American corn growers, biodiesel producers, ethanol producers and Brazilian sugarcane ethanol producers complained about EPA's methodology, while the oil industry requested an implementation delay. On June 26, 2009, the House of Representatives approved the 219 to 212, mandating EPA to exclude ILUC for a 5-year period, vis a vis RFS2. During this period, more research is to be conducted to develop more reliable models and methodologies for estimating ILUC, and will review this issue before allowing EPA to rule on this matter. The bill failed in the.
On February 3, 2010, EPA issued its final RFS2 rule for 2010 and beyond. The rule incorporated direct and significant indirect emissions including ILUC. EPA incorporated comments and data from new studies. Using a 30-year time horizon and a 0% discount rate, EPA concluded that multiple biofuels would meet this standard. EPA's analysis accepted both and from corn starch as 'renewable fuels'.
Became an 'advanced fuel'. Both and from soy oil and diesel from waste oils, fats, and greases fell in the 'biomass-based diesel' category. And cellulosic diesel met the 'cellulosic biofuel' standard.
The table summarizes the mean GHG emissions estimated by EPA modelling and the range of variations considering that the main source of uncertainty in the life cycle analysis is the GHG emissions related to international land use change. Year 2022 reduction results for final rule (includes and a 30-year payback at a 0% discount rate) Renewable fuel Pathway (for U.S. Consumption) Mean GHG emission reduction (1) GHG emission reduction 95% confidence interval (2) Assumptions/comments 21% 7–32% New or expanded fired dry mill plant, 37% wet and 63% dry it produces, and employing corn oil fractionation technology. Corn 31% 20–40% Natural gas fired dry mill plant, 37% wet and 63% dry DGS it produces, and employing corn oil fractionation technology.
(3) 61% 52–71% Ethanol is produced and dehydrated in Brazil prior to being imported into the U.S. And the residue is not collected. GHG emissions from ocean tankers hauling ethanol from Brazil to the U.S. Are included. From 110% 102–117% Ethanol produced using the biochemical process.
Cellulosic ethanol from 129% No ILUC Ethanol produced using the biochemical process. Ethanol produced from agricultural residues does not have any international land use emissions.
From 57% 22–85% Plant using natural gas. Waste grease biodiesel 86% No ILUC Waste grease feedstock does not have any agricultural or land use emissions. Notes: (1) Percent reduction in lifecycle GHG emissions compared to the average lifecycle GHG for gasoline or diesel sold or distributed as transportation fuel in 2005.
(2) Confidence range accounts for uncertainty in the types of land use change assumptions and the magnitude of resulting GHG emissions. (3) A new Brazil module was develop to model the impact of increased production of Brazilian sugarcane ethanol for use in the U.S. Market and the international impacts of Brazilian sugarcane ethanol production. The Brazil module also accounts for the domestic competition between crop and pasture land uses, and allows for livestock intensification (heads of cattle per unit area of land). Reactions UNICA welcomed the ruling, in particular, for the more precise lifecycle emissions estimate and hoped that classification the advanced biofuel designation would help eliminate the tariff. (RFA) also welcomed the ruling, as ethanol producers 'require stable federal policy that provides them the market assurances they need to commercialize new technologies', restating their ILUC objection.
RFA also complained that corn-based ethanol scored only a 21% reduction, noting that without ILUC, corn ethanol achieves a 52% GHG reduction. RFA also objected that Brazilian sugarcane ethanol 'benefited disproportionally' because EPA's revisions lowered the initially equal ILUC estimates by half for corn and 93% for sugarcane. Several lawmakers commented that they continued to oppose EPA's consideration of the 'dicey science' of indirect land use that 'punishes domestic fuels'. House Agriculture Chairman said, '. To think that we can credibly measure the impact of international indirect land use is completely unrealistic, and I will continue to push for legislation that prevents unreliable methods and unfair standards from burdening the biofuels industry.' EPA Administrator commented that the agency 'did not back down from considering land use in its final rules, but the agency took new information into account that led to a more favorable calculation for ethanol'.
She cited new science and better data on crop yield and productivity, more information on co-products that could be produced from advanced biofuels and expanded land-use data for 160 countries, instead of the 40 considered in the proposed rule. Europe As of 2010, European Union and United Kingdom regulators had recognized the need to take ILUC into account, but had not determined the most appropriate methodology. UK Renewable Transport Fuel Obligation.
See also: The (RTFO) program requires fuel suppliers to report direct impacts, and asked the (RFA) to report potential indirect impacts, including ILUC and commodity price changes. The RFA's July 2008 'Gallager Review', mentioned several risks regarding biofuels and required feedstock production to avoid agricultural land that would otherwise be used for food production, despite concluding that 'quantification of GHG emissions from indirect land-use change requires subjective assumptions and contains considerable uncertainty'. Some environmental groups argued that emissions from ILUC were not being taken into account and could be creating more emissions. European Union.
See also: On December 17, 2008, the approved the Renewable Energy Sources Directive (COM(2008)19) and amendments to the Fuel Quality Directive (Directive 2009/30), which included sustainability criteria for biofuels and mandated consideration of ILUC. The Directive established a 10% biofuel target. A separate Fuel Quality Directive set the, requiring a 6% reduction in GHG intensity of EU transport fuels by 2020. The legislation ordered the to develop a methodology to factor in GHG emissions from ILUC by December 31, 2010, based on the best available scientific evidence. In the meantime, the European Parliament defined lands that were ineligible for producing biofuel feedstocks for the purpose of the Directives. This category included and continuously areas with cover of more than 30 percent or cover between 10 and 30 percent given evidence that its existing carbon stock was low enough to justify conversion. The Commission subsequently published terms of reference for three ILUC modeling exercises: one using a General Equilibrium model; one using a Partial Equilibrium model and one comparing other global modeling exercises.
It also consulted on a limited range of high-level options for addressing ILUC to which 17 countries and 59 organizations responded. The on the Right to Food and several environmental organizations complained that the 2008 safeguards were inadequate.
UNICA called for regulators to establish an empirical and 'globally accepted methodology' to consider ILUC, with the participation of researchers and scientists from biofuel crop-producing countries. In 2010 some accused the European Commission of lacking transparency given its reluctance to release documents relating to the ILUC work. In March 2010 the Partial and General Equilibrium Modelling results were made available, with the disclaimer that the EC had not adopted the views contained in the materials. These indicate that a 1.25% increase in EU biofuel consumption would require around 5,000,000 hectares (12,000,000 acres) of land globally.
The scenarios for varied from 5.6-8.6% of road transport fuels. The study found that ILUC effects offset part of the emission benefits, and that above the 5.6% threshold, ILUC emissions increase rapidly increase. For the expected scenario of 5.6% by 2020, the study estimated that biodiesel production increases would be mostly domestic, while bioethanol production would take place mainly in Brazil, regardless of EU duties. The analysis concluded that eliminating trade barriers would further reduce emissions, because the EU would import more from Brazil. Under this scenario, ' direct emission savings from biofuels are estimated at 18 Mt CO 2, additional emissions from ILUC at 5.3 Mt CO 2 (mostly in Brazil), resulting in a global net balance of nearly 13 Mt CO 2 savings in a 20 years horizon.
The study also found that ILUC emissions were much greater for biodiesel from and estimated that in 2020 even at the 5.6% level were over half the greenhouse gas emissions from diesel. As part of the announcement, the Commission stated that it would publish a report on ILUC by the end of 2010. Certification system On June 10, 2010, the EC announced its decision to set up certification schemes for biofuels, including imports as part of the Renewable Energy Directive.
The Commission encouraged E.U. Nations, industry and to set up voluntary certification schemes. EC figures for 2007 showed that 26% of and 31% of used in the E.U. Was imported, mainly from and the.
Reactions UNICA welcomed the EU efforts to 'engage independent experts in its assessments' but requested that improvements because '. The report currently contains a certain number of inaccuracies, so once these are corrected, we anticipate even higher benefits resulting from the use of Brazilian sugarcane ethanol.' UNICA highlighted the fact that the report assumed land expansion that 'does not take into consideration the agro-ecological zoning for sugarcane in Brazil, which prevents cane from expanding into any type of native vegetation.' Critics said the 10% figure was reduced to 5.6% of transport fuels partly by exaggerating the contribution of (EV) in 2020, as the study assumed EVs would represent 20% of new car sales, two and six times the car industry's own estimate. They also claimed the study 'exaggerates to around 45 percent the contribution of bioethanol—the greenest of all biofuels—and consequently downplays the worst impacts of biodiesel.' Environmental groups found that the measures 'are too weak to halt a dramatic increase in deforestation'. According to, 'indirect land-use change impacts of biofuel production still are not properly addressed', which for them was the most dangerous problem of biofuels Industry representatives welcomed the certification system and some dismissed concerns regarding the lack of land use criteria.
UNICA and other industry groups wanted the gaps in the rules filled to provide a clear operating framework. The negotiations between the European Parliament and the Council of European Ministers continue. A deal is not foreseen before 2014 See also.
Abstract Miombo woodlands support agriculture, biodiversity, and multiple ecosystem services across an extensive part of sub-Saharan Africa. Miombo is frequently overutilised with deforestation and degradation resulting in significant land use and land cover change (LULCC). Understanding the drivers of LULCC is essential to achieving sustainable land management in miombo woodland regions.
Within a remote miombo area of south-west Tanzania in the Kipembawe Division, Mbeya Region, social survey and ecological data were used to identify the direct and indirect drivers of LULCC. Our findings show that tobacco ( Nicotiana tabacum) production results in an estimated annual deforestation rate of 4,134 ± 390 ha of undisturbed miombo woodland, of which 56.3 ± 11.8% is linked to the post-harvest curing process. This deforestation represents 0.55 ± 0.06% of the wooded area of the Kipembawe Division.
The perception of high incomes from tobacco cultivation has encouraged migration of both agriculturalists and pastoralists into the area, resulting in higher livestock numbers that lead to further degradation. Higher human populations need more woodland resources such as fuelwood and building materials and more farmland for food crops. Continued deforestation will reduce the long-term profitability of tobacco cultivation due to a lack of fuel to cure the crop and could render production unviable.
Action is urgently needed to conserve globally important biodiversity resources while enabling agricultural and pastoral activities to continue. Improved governance, together with sustainable land management strategies and diversification of livelihood strategies, can reduce dependence on tobacco cultivation and contribute to a sustainable future for this ecoregion. 1 INTRODUCTION Land use and land cover change (LULCC) describes the human-induced alteration of the earth's surface (Ellis, ) and often occurs through degradation and deforestation of woodlands and forest. This contributes to global climate change and influences ecosystem service provision (Lambin et al., ), in addition to causing a loss in biodiversity and undermining the capacity of ecosystems to support agricultural output (Foley et al., ). A driver of change can be natural or anthropogenic, and it causes a change in the state of something else (MEA, ). When a driver unequivocally has an influence, it is described as a direct driver, and when they underlie or lead to a direct driver, they are described as an indirect driver (MEA, ).
Indirect drivers can be classified into five categories (sociopolitical, religious and cultural, demographic, scientific and technological, and economic), which can influence direct drivers (Nelson et al., ). Deciduous miombo woodlands cover 2.4 million km 2 of sub-Saharan Africa (Frost, Timberlake, & Chidumayo, ), are home to over 100 million people (Campbell et al., ), and contain numerous endemic and threatened species (Conservation International, 2012). Miombo woodlands are dominated by tree species of the legume subfamily Caesalpinioideae within three genera ( Julbernardia, Brachystegia, and Isoberlinia; Frost et al., ). They are globally important owing to their capacity to store carbon and influence environmental and socio-economic systems (Ribeiro, Syampungani, Matakala, Nangoma, & Ribeiro-Barros, ). They are locally important due to provisioning ecosystem services including medicinal plants, edible forest products, food for livestock, construction materials, and fuel sources (Dewees et al.,; Jumbe, Bwalya, & Husselman,; Malambo & Syampungani, ).
By 2050, sub-Saharan Africa's population is predicted to increase twofold (Eastwood & Lipton, ), leading to increasing pressure upon miombo woodland (Cabral, Vasconcelos, Oom, & Sardinha, ). Sustainable management of miombo woodlands is therefore needed, and they are receiving increasing global consideration (Williams et al., ). Presently, the greatest research focus in miombo woodland surrounds their role in carbon storage (e.g., Shirima et al.,; Williams et al., ), with limited understanding of the drivers of land use change.
Regionally, several direct anthropogenic drivers of LULCC have been identified in miombo systems, including overgrazing, agricultural expansion, charcoal, fuelwood, and timber extraction, rising urbanisation, unmanaged fires, and excessive exploitation of valuable animal and tree species (e.g., Cabral et al.,; Fisher,; Ryan et al., ). Natural drivers of change that are likely to impact miombo woodlands include changes to rainfall patterns and volumes (Seth et al., ), rising temperatures (Pienaar, Thompson, Erasmus, Hill, & Witkowski, ), and altered fire regimes (Andela & van der Werf, ). General descriptions of drivers can provide information to inform regional land management policy, yet they do not identify local-scale nuances necessary for land use and management decisions.
To provide effective, enduring management solutions for miombo woodlands, it is necessary to understand both direct and indirect drivers (Nelson et al., ), especially as drivers differ substantially from region to region (Bond, Chambwera, Jones, Chundama, & Nhantumbo,; Vinya, Syampungani, Kasumu, Monde, & Kasubika, ). This paper addresses this gap by providing empirical data from a miombo woodland landscape in south-west Tanzania, which is currently experiencing rapid land use change. The key anthropogenic drivers of land use change are identified through integrative quantitative and qualitative research methods. 2 MATERIALS AND METHODS 2.1 Study area Miombo woodland represents 95% of forested area in Tanzania (MNRT, ).
Between 1990 and 2000, it is estimated that 13% of Tanzanian miombo woodland was lost (FBD, ). Current estimations of Tanzanian woodland and forest loss range between 372,000 and 580,000 ha/year (FRELT,; MNRT, ). The site for this study is located in the Kipembawe Division (8,766 km 2), within the Chunya District, Mbeya Region of south-west Tanzania (7°54′58.44″S, 33°19′22.84″E, Figure ). The study area is representative of other areas of high rainfall miombo woodland.
Farming is the dominant occupation for the estimated population of 66,752, across 16 villages (National Bureau of Statistics, ). Within the division, village-level Participatory Forest Management Committees oversee five reserves, and the District Forestry Department governs three forest reserves. However, this study found that the reserves are poorly managed owing to insufficient funding and limited capacity in terms of personnel and transport. Access to woodland is therefore largely unrestricted across both protected and unprotected areas. Average yearly precipitation is 933 ± 36 mm ( n = 28 years). Rains typically start in October and occur frequently until May, with very little falling throughout the rest of the year. The soils are shallow and sandy, and the landscape is predominantly flat.
Study area (Kipembawe Division) within the Chunya District, Tanzania. The main trading villages, pilot study village, study villages, and ecological survey sites are highlighted (created from GADM,; Sandvik, ) Colour figure can be viewed at 2.2 Data collection To identify the drivers of deforestation and degradation, a mixed methods approach was taken, combining social and ecological surveys. This enabled a holistic examination of the drivers of land use change by drawing upon a range of complementary primary data sources. 2.2.1 Ecological survey Nine ecological survey sites were selected (described in Jew, Dougill, Sallu, O'Connell, & Benton, ), representing low to high levels of human utilisation of the woodland.
Within each survey site, five transects were conducted to record land use type and utilisation levels. Transects were 10 m wide and 1.5 km long and split into 20-m sections (Doggart, ), sampling 75,000 m 2 at each site.
Within each section, all live, dead, and cut poles and timbers were recorded, and the main land cover type documented. Evidence of utilisation or removal of non-timber forest products and other disturbances was noted, for example logging, tree bark removal, and beehives. 2.2.2 Social survey The social survey consisted of household questionnaires, village-level focus groups, and semi-structured key informant interviews to obtain information on drivers of land use change and agricultural methods. The five villages selected for involvement in the social survey were in close proximity to ecological survey sites with medium and low utilisation levels, allowing social and ecological survey data to be aligned by comparing quantitative data with qualitative data, particularly in terms of agricultural land cover. The four remaining ecological survey sites were not in close proximity to any village and therefore not suitable for comparable study.
Villages were situated within three wards (“study” villages, Figure ). A further village was selected for piloting the research methods (“pilot” village, Figure ). Fieldwork took place February–September 2013, when the research team lived within the community, and therefore, field observations were an additional data source. Government census data were also used to determine demographic patterns within the district. Within each of the five villages, 10% of households ( n = 196) were chosen at random to engage in questionnaires (Meshack, Ahdikari, Doggart, & Lovett, ). These were undertaken with the head of the household, where a household was defined as containing people who eat at least one meal together and sleep in the same accommodation, and the head is the principal decision maker.
Household farming activities were discussed. Questionnaires were conducted in Kiswahili by experienced translators and typically lasted approximately 40 min, including both closed and open questions.
Multiple focus group discussions took place in each village with identified sets of people (e.g., villagers, livestock keepers, and crop producers) determined through key informant interviews with village committee representatives (e.g., Participatory Forest Management Committee and Social Welfare Committee). Focus group discussions lasted for approximately 1 hr, with 2–8 people and an even number of men and women, subject to availability. Overall, 28 focus groups were conducted. The purpose of focus groups was to collect comprehensive qualitative information on relevant issues and to explore key themes and questions that had arisen in household questionnaires. A range of questions was presented, and all answers were considered between group participants with facilitation (Ritchie, Lewis, Nicholls, & Ormston, ). Each session was recorded, and the lead researcher took notes through translation.
Semi-structured interviews took place with 41 key informants at all governance levels from village to regional. Key informants were either involved with a particular programme or project or held extensive knowledge on a specific relevant topic (O'Leary, ). Snowball sampling was used to identify interviewees within the public, private, and voluntary sectors. Interviews explored key themes of relevance to each individual that had emerged through household questionnaires and focus groups. Interviews and focus groups were coded and grouped into themes for analysis, with direct and indirect drivers emerging from the data and subsequently undergoing comparison with the other data sources to determine validity.
3 RESULTS The main indirect drivers of LULCC were identified to be demographic (in-migration) and economic (rising tobacco prices). Direct drivers include the clearing of land for agriculture (in particular tobacco), energy demand for curing tobacco leaves, extraction of wood for household use and construction, and degradation and deforestation caused by livestock and livestock keepers. 3.1 Indirect drivers 3.1.1 Demographic: in-migration Demographic data from household surveys demonstrate high rates of in-migration, with 75% of respondents having migrated to Kipembawe from other regions of Tanzania. The recorded population of Kipembawe in 2012 was 66,752 (National Bureau of Statistics, ), having grown by over 60% from 41,493 in 2002 (Central Census Office, ). Household surveys indicated that the most likely reason to move to the area was to farm (62%), and of these 74% (67 households) said their main motivation was to cultivate tobacco.
A further 27% of household heads moved to join family members who had migrated previously. Other reasons for in-migration included to improve quality of life and for work, mining, education, and government relocation. 3.1.2 Economic: rising tobacco prices Higher tobacco prices encourage in-migration as Ward B Officer 1 (2013) explained: “There is a lot of immigration for tobacco cultivation, when the price is high” (Jew, p. Additionally, current residents may decide to cultivate tobacco or expand their cultivated area in response to rising prices. This is illustrated in Figure, where the average price of tobacco in Tanzania since 1997 is shown in relation to in-migration and initiation of tobacco production in Kipembawe. In years where the tobacco price drops, key informants explained that male household members travel to the Lupa Goldfield near Chunya to practise artisanal gold mining until the tobacco prices rise again; hence, outmigration is not evident in this area. The trend in Tanzanian national tobacco prices per kilogram (source: UN Comtrade, ), the year respondents arrived in Kipembawe (Household Surveys, 2013, n = 150, 46 respondents born in area), and the year respondents began cultivating tobacco in Kipembawe (source: Household Surveys, 2013, n = 167) Colour figure can be viewed at During the 2012/2013 season, top-grade tobacco was valued at US$1.939 per kg and the lowest at US$0.396 per kg.
The price for tobacco was set to increase for the 2013/2014 season, and therefore, Company 1 expected that tobacco would be grown on 10% more land than in 2012/2013. Tobacco farmers ( n = 168, household surveys) cited benefits such as abilities to build a house (116 respondents), buy clothes (91), pay school fees (68), and buy food (41).
Other benefits cited by fewer than 40 respondents included purchasing livestock, opening a new business, and paying for healthcare. 3.2 Direct drivers 3.2.1 Agriculture In the six ecological survey sites that experienced high and medium levels of woodland utilisation, transect data demonstrated that approximately 30% of land cover was agricultural, 7% was regenerating miombo woodland, and 62% was undisturbed vegetation (Table ).
The two dominant cultivated crops were maize and tobacco. Households on average grow maize over 1.2 ha (mode, n = 194, min = 0.2 ha, max = 8 ha) and cultivate 0.8 ha of tobacco (mode, n = 167, min = 0.2 ha, max= 16 ha). According to interviews with the two tobacco companies, there were 7,800 registered tobacco farmers in Kipembawe Division in 2013 and an estimated area of 8,639 ha under tobacco cultivation (Company 1: 6,088 ha; Company 2: 2,281 ha). According to household surveys where undisturbed vegetation was cleared in the years following original clearance to start the farm, non-tobacco farmers ( n = 27) clear 0.09 ± 0.03 ha ( M ± SEM) of undisturbed vegetation per year, and tobacco farmers ( n = 157) clear 0.53 ± 0.05 ha/year, showing that tobacco farmers clear significantly more woodland than non-tobacco farmers. A further five tobacco farmers did not clear undisturbed vegetation but did clear regenerating woodland, ranging from 0.8 to 1.6 ha annually, and two non-tobacco farmers cleared only regenerating woodland (unspecified amount). Table 1. Land use and percentage land cover percentages determined from ecological survey transects (0.375 km 2) and amount of land cultivated derived from household surveys ( n = 196) Land cover and land use% cover from transects Total hectares of crop grown Type Subtype.
aOther crops grown: cassava (5 ha), millet (4 ha), peas (4 ha), rice (2 ha), and sesame (1 ha). Ahrends, A., Burgess, N.
D., Milledge, S. H., Bulling, M. T., Fisher, B., Smart, J. R., Lewis, S.
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