Most respondents (≥70%) on both mountains reported increased temperatures during the dry and the rainy seasons and a reduction in the number of frost days (Figure 2). Most respondents (≥70%) on both mountains also observed a reduction in the duration and amount of rainfall and fog during the long rains and an increase in dry spells and strong winds. Most respondents reported decreased stream flow and fewer hailstorms, and increased rain showers during the dry season. One main difference between the two sites were reports of increased extreme events, particularly floods and droughts: more respondents on the Udzungwa Mountains reported these compared to Mount Kilimanjaro (80% vs. 30%, respectively). However, more respondents on Mount Kilimanjaro reported an increased number of landslides (45% vs. 5%). In the two high-elevation villages on Mount Kilimanjaro, respondents also noted that the amount and duration of the short rains had changed. This was described by a female leader in Foo as follows: 'Vuli (short rains) have become unreliable. Sometimes there is too little rain to grow crops, and sometimes these rains are so long that they end up destroying the crop'.

**Figure 2.** Observed changes in climate and impacts in the physical domain in terms of percentage of respondents in each mountain (*n* = 150 on Mount Kilimanjaro, *n* = 150 on the Udzungwa Mountains). On the Udzungwa Mountains, long rains refer to the only rainy season found on the north-western slopes. We did not specifically ask every respondent on Mount Kilimanjaro about the short rains, so these are not included in Figure 2.

> Within each mountain, few differences were observed between villages at different elevations. On Mount Kilimanjaro, the main differences were that fewer respondents in Mudio (the lowest-elevation village) reported increased rain showers during the dry season, while more respondents in Kokire Mamba reported more landslides. In the Udzungwa Mountains, more respondents in Udekwa reported increased soil erosion (Supplementary Material C).

> Respondents in both mountains reported reduced crop yields and an increase in crop pests and diseases, but the percentage of respondents varied for the different crops, being greater for maize, beans, coffee, and green banana (Figure 3). On Mount Kilimanjaro, respondents reported the most harmful crop pests as including *viwavi jeshi* (fall armyworm), which feeds on the stem and leaves of maize; *kishori* or *mnyauko* (fusarium wilt of banana), which dries the leaves of green banana and coffee; *kimamba* (green scale *coccus viridis*), which coils green leaves for beans and coffee; and *kimatira* and *uwiwi* (coffee berry borer), which attacks the fleshy berry surrounding the coffee kernel. On the Udzungwa Mountains, the most harmful pests were *viwavi jeshi* (fall armyworm) and *michilizi* (yellow striped virus), which affect maize by stunting the growth of panicles and flowers or causing the plant to be sterile; *fangasi* (rust), which, in beans, results in stunted growth; and *utitiri mwekundu* (red spider mites) and *vipekecha majani* (leaf miner), which, for onions, sucks the plant sap, grinds the leaves, and feeds on the plant tissue. Farmers noticed changes in pest incidence with increased temperature, which created favourable environments and changed ecological niches. For instance, farmers observed *wadudu chawa* (thrips) increased in abundance on onion leaves when there is little rainfall and high temperatures. Respondents on both mountains reported reduced milk production and an increase in cattle diseases, but more respondents reported these on Mount Kilimanjaro—probably as a larger percentage of respondents in this mountain own cattle, and milk production is an important component of the diet and culture of the Chagga. Surrounding the Udzungwa Mountains, more respondents reported increased diseases amongst goats. In both mountains, respondents said human health was adversely affected by climate change impacts (Figure 3).

**Figure 3.** Observed impacts in the biological domain regarding percent of respondents in each mountain (*n* = 150 on Mount Kilimanjaro, *n* = 150 on the Udzungwa Mountains). Coffee and green bananas are not cultivated in our study area of Udzungwa Mountains. Although Irish potatoes, onions, pigs, and goats are grown by some farmers on Mount Kilimanjaro, we excluded these in the questionnaire on Mount Kilimanjaro as they are not widespread.

> On Mount Kilimanjaro, the main differences across villages were that fewer respondents in Foo (the highest elevation village) reported decreased yields for green bananas (Supplementary Material C). On the Udzungwa Mountains, only respondents at high elevation villages (Ilula and Udekwa) reported reduced yields for Irish potatoes. Lower elevation villages were not involved in such farming activities.

## *3.2. Adaptation Strategies*

The main adaptation strategies in both mountains were modifying farming or animal rearing. To adapt to climate changes, most farmers shifted to using improved crop varieties (mostly banana, maize, and beans)—preferring traits to improve disease, pest and drought resistance, early maturing, and high yield, depending on the context (Figures 3 and 4). This could be in part because in the past decade, agricultural extension programmes and research institutions have introduced several improved varieties of seeds. Respondents also mentioned increased use of soil conservation techniques, chemical fertilisers, and pesticides. Changing farm location, increasing farm size, and irrigation uptake were mentioned by more respondents on the Udzungwa Mountains than on Mount Kilimanjaro—where there is high population density. However, respondents highlighted that agrochemical use could lead to air and water pollution, while irrigation can lead to soil salinisation. Only around the Udzungwa Mountains did farmers cite changing crop species (e.g., from maize to drought-resistant millet), although there is some resistance due the shortage of seeds, food preferences, and the fact that maize has historically been planted. Notably, farmers on Mount Kilimanjaro reported sowing seeds later in the planting season, while around the Udzungwa Mountains, farmers sowed them earlier. In both study areas, farmers sowed seeds twice in one season when needed. Interestingly, most study participants mentioned changing planting dates.

**Figure 4.** Adaptation strategies used by study respondents (%) (*n* = 150 on Mount Kilimanjaro, *n* = 150 on the Udzungwa Mountains). Coffee and bananas are not cultivated in our study area of the Udzungwa Mountains. Although Irish potatoes, onions, pigs, and goats are grown by some farms on Mount Kilimanjaro, we excluded these in the questionnaire on Mount Kilimanjaro as they are not widespread. NTFPs: Non-timber forest products.

> On Mount Kilimanjaro, most farmers reported increased use of veterinary care and supplementary feed for cattle, while on the Udzungwa Mountains, a few respondents mentioned an increased use of veterinary care for goats and pigs. On both mountains, diversifying livelihoods was also cited, with increasing animal rearing or vegetable production being the most cited on Mount Kilimanjaro and casual labour employment or animal rearing being the most cited on the Udzungwa Mountains. In both regions, some respondents turned into firewood collection and trade, and some started small businesses (Figure 4). Across mountains, less than 30% use information from extension officers or radio (<30%) on when to sow seeds—even though most respondents in both mountains have a radio (Supplementary Material B). Instead, most farmers still used personal observations (e.g., observing when certain plants bloom before the long rains start or when certain birds sing).

> Some differences were observed on Mount Kilimanjaro regarding village elevation. For example, fewer people in Uparu reported increased use of pesticides or fertilisers, and more respondents in Mudio (the lowest-elevation village) reported increased vegetable and fruit production and the trade of animal products. In the Udzungwa Mountains, only respondents in Ilula reported using improved varieties of Irish potatoes. In addition, increased use of pesticides and fertilisers was lower in Udekwa, and more respondents in Ilula reported increased use of veterinary care for pigs (Supplementary Material C).

## *3.3. Differentiated Adaptation Responses by Wealth and Gender*

On Mount Kilimanjaro, wealth had a significant effect on five adaptation strategies. Wealthier households had a significantly higher adoption of the following adaptations: increased irrigation (63.3%), increased use of fertilisers (91.8%), increased use of pesticides (89.8%), increased vegetable farming (63.3%), and trading animal products (77.6%) (Table 1). Around the Udzungwa Mountains, wealth had a significant effect on nine adaptation strategies, including four of those also affected by wealth in Mount Kilimanjaro (Table 2). Poorer households had a significantly higher adoption of the following adaptations: diversify to sell firewood (18.8%), diversify to labour (46.9%) and diversify to rearing animals (20.3%). Interestingly, a major difference between the adaptations of the Hehe compared to the Chagga was that a greater number of poor than wealthy households had diversified to labour (46.9% vs. 10.7%) and selling firewood (18.8% vs. 7.1%). Gender was not significantly associated with adaptation responses (see results in Supplementary Material D).

**Table 1.** Adaptive strategies used by each wealth group on Mount Kilimanjaro (% respondents within wealth group). \* Significant differences across wealth groups at *p* > 0.05, using cross-tabulation tables and chi-square tests.

**Table 2.** Adaptive strategies used by each wealth group on Udzungwa Mountains (% respondents within wealth group). \* Significant differences across wealth groups at *p* > 0.05, using cross-tabulation tables and chi-square tests.

## *3.4. Other Factors Supporting or Hindering the Adoption of Adaptation Strategies*

In terms of factors supporting adaptation, farmers associations help provide loans to buy food or seeds or offer financial help to diversify income or start a small business. Approximately 51% of farmers are members of farmer organisations (i.e., 56% on Mount Kilimanjaro and 46% on Udzungwa). These are mostly women's or loan associations. When asking about the use of climate information, farmers indicate they access information from radio, television, or church.  However, respondents explained that they do not trust the predictions from meteorological agencies because they feel the information given is not useful, which was described by a leader in a Uparo village in Kilimanjaro as due to the fact that the 'Two-day predictions from the radio are too short notice to prepare the fields'. This hinders the use of climate information to inform land management decision making.

Factors that hinder adaptation relate predominantly to land tenure and ownership rights, labour availability, high initial investment costs for adaptation, and limited technical skills for new practices. Food preferences further influence decisions to diversify crops. For instance, some Chagga avoid cassava and sweet potato because these crops are perceived as a 'hunger food'.

#### **4. Discussion**

#### *4.1. Climatic Changes and Impacts*

Our study provides evidence of how Indigenous communities can provide insights on the climatic changes already observed on mountains for a wide range of climate variables beyond rainfall and temperature (e.g., fog, rain showers, hailstorms), as shown by other studies [37–39]. If fog is expected to change considerably due to predicted increased temperatures and raising cloud base in African mountains [17,58] and few meteorological stations record such variables, local peoples' perceptions of change could be used to better understand non-precipitating changes in moistures impacts on crop and fodder production.

In general, the climatic changes reported by farmers in this study agree with previous studies on climate change perceptions in Tanzania's mountains [27,45,48,59–63], but we investigated more variables than previous studies (Table 3 and references therein). For instance, increased temperatures, reduced duration and amount of rainfall, and more dry spells during the long rains were reported by previous studies on Kilimanjaro, Pare, Uluguru, Usambara, and Udzungwa mountains and the Southern Highlands. However, in the Udzungwa Mountains, the early onset of the rainy season was noted instead of a later onset of rainfall—which we report here. Decreased stream flow was previously reported in both mountains [27,45,59,61,64,65]. An increase in extreme droughts was not identified in previous studies on Mount Kilimanjaro, although it was cited in the Pare and Udzungwa Mountains. An increase in wind strength during the rainy season was previously mentioned in the Pare Mountains [64], but not on Mount Kilimanjaro and the Udzungwa Mountains. While our study participants in both mountains reported important changes in fog, only one previous study recorded this change in the East Usambaras [66]. Some of the differences between this and previous studies in the same mountains could be related to local topography or climatic conditions (e.g., villages in ridges being more exposed to wind) [67]. The fact that some previous studies were conducted a decade ago might also explain differences across our and previous studies—if some phenomena have intensified in recent years.

As highlighted by [30,68], tapping into the detail of the climatic changes perceived by local communities allows researchers and practitioners to better understand the nuances of climate impacts on farmer livelihoods and ensuing locally acceptable adaptation decisions. For example, both dry spells during the rainy season or showers during the dry season can negatively affect maize yields, but fog can be a source of moisture for seed germination [58].

The perceived changes in rainfall and temperatures reported by farmers agree with available meteorological data. On Mount Kilimanjaro, rainfall measurements at three different elevations indicate a significant reduction in annual rainfall up to 2004 [68]. However, a recent re-analysis for Kilimanjaro airport showed no significant trend for the period 1973–2013, except for increased rainfall in March but reduced rainfall in April [69], which was also noted between 2001 and 2019 [59]. Respondents suggested this may be due to the late onset of the long rains. Differences in perceived and observed change could also be related to the fact that Kilimanjaro airport is located between 30 and 80 km from the villages sampled—further emphasising the need to capture local perceptions, particularly on mountains where the environment vary significantly over short distances. Available meteorological data also indicate increased temperatures [69,70], in agreement with farmer's perceptions. We did not have access to meteorological data from the Udzungwa Mountains as the only meteorological station in the region is located at much lower elevations.

Regarding impacts in the biophysical domain, only some of the impacts mentioned by our study participants have been reported by other studies. Surprisingly, lower yields for green banana and beans were not mentioned in previous studies on Mount Kilimanjaro, although the latter was reported in the Pare Mountains and [71] reported maize pests in Kilimanjaro. Several Chagga respondents highlighted that organisations should help ensure the productive yield of their preferred staple crop (green banana), which could be considered a cultural keystone species [72], as most instead focus on coffee.

Regarding livestock, previous studies in Tanzania's mountains did not report decreased milk or increased diseases, but these were reported from other locations in Tanzania [73]. A study in the mountains in northern Kenya [58] mentioned reduced fodder availability due to increasing droughts and related weak health of animals.

Previous studies on Mount Kilimanjaro did not mention reduced human health due to climate changes, but this was noted in the Udzungwa Mountains. Reasons related to increasing temperatures influencing the prevalence of waterborne diseases (cholera, typhoid, dysentery, malaria, and amoebic diseases) where there is limited potable water, as suggested by [74] for the Kilombero district and reported by [43]. Non-climatic factors could also affect health, such as reduced stream flow, upstream pollution, and river farming leading to deposition of agrochemicals in watercourses. We were unable to investigate the nuances of reduced human health, and thus future work is clearly needed in this area that combines medical data with insights from communities [75,76].

#### *4.2. Adaptation Strategies*

Previous work on Tanzanian farmers' adaptation to climate change (Table 4) highlighted that a combination of strategies is often used, including, inter alia, agricultural extensification, intensification, livelihood diversification, pooling of resources and labour, and migration [78]. On Mount Kilimanjaro, farmers changed the crop they planted. This is similar to studies in drier parts of Tanzania, where farmers increase the cultivation of sweet potatoes and cassava in dry years (or following a 'bad' year for maize) [20,78]. Coffee farmers grow shade trees to adapt to heat stress, a strategy previously undocumented for Mount Kilimanjaro but recorded in the Jimma Mountains, Ethiopia [77]. In the Usambara Mountains, increased use of agroforestry was cited for non-coffee crops [42], but this was not mentioned by our study participants. Others switched to vegetable or fruit production. Agricultural extensification was only used by some respondents surrounding the Udzungwa Mountains, but not in the southern slopes of Mount Kilimanjaro. This may be in part due to higher land scarcity coupled with high population density in the latter site.

Farmers also changed animal rearing practices as they intensified the use of veterinary care. This was previously undocumented for Mount Kilimanjaro but was mentioned in the mountains of northern Kenya [58].

Although most adaptation strategies focused on modifying farming practices, livelihood diversification was also cited, i.e., selling firewood, establishing a small business, and providing labour—most of which had been mentioned in previous studies. The sustainability of these strategies requires further investigation, especially collecting and selling firewood. Several other authors have highlighted the increased degradation of forests in the Eastern Arc Mountains (which includes the Udzungwa Mountains) [79]. Remarkably, very few households mentioned harvesting or trading non-timber forest products, which is different from previous studies in other African mountains [52]. This may be attributed to: the fact that we studied communities whose predominant livelihood activity is agriculture, fear of reporting illegal actions, changes in law reinforcement in the national parks, growing electrification, the fact that national park authorities in 2019 launched a household tree planting initiative to overcome firewood availability [80], or because the agroforestry farming system in Kilimanjaro has lowered firewood demand among villagers. In contrast to other studies across sub-Saharan Africa [81–83], we did not find evidence of migration due to climate change. Nevertheless, people are likely to migrate for other reasons such as education, lifestyle aspirations, and seasonal or permanent employment.

#### *4.3. Differentiated Adaptation Responses by Wealth and Gender*

We found that wealthier households used more inputs (e.g., irrigation, fertilisers, pesticides) and diversified commodities produced (e.g., horticulture, trading animal products), similar to other studies (e.g., [31]). However, we found one difference to previous work: in Udzungwa, poorer households used labour as diversification, but not in Mount Kilimanjaro. Most Chagga have invested in educating their children—some of which now work in urban areas and send remittances—which could explain why some do not engage in labour. Notably, even poor Chagga households are relatively wealthier than poor Hehe households in terms of assets—for example, owning a radio.

Interestingly, gender did not influence perceptions of climatic changes, impacts, or adaptive strategies. This is in contrast to other studies in African mountains such as Mount Elgon, Uganda [85], where important differences between male and female respondents were reported. The explanation for this may be related to the fact that today, the distinction in gender-based roles in Chagga and Hehe societies is starting to blur and that education and access to information in our study areas are becoming more accessible to women. On the other hand, it could be argued that lack of differences is related to the fact that we interviewed few female-headed households (9.7%), resulting in a reporting bias (Supplementary Material B). Future work should explore the adaptive strategies of poor female-headed households in more detail, e.g., see [86].

#### *4.4. Other Factors Supporting or Hindering the Adoption of Adaptation Strategies*

Despite being climate change literate, most farmers in both mountains still rely on their own observations to judge the timing and quality of the coming growing season. Some farmers mentioned that the signs they used are not as accurate as they once were. These findings were also observed by [59,87], who showed climate variability could undermine farmers' confidence in their existing knowledge and practices and hinder farmers' ability to plan and manage new pests.

An important factor that can support adaptation is membership in farmers organisations. Organisations help farmers access loans to, for instance, buy improved seed varieties, buy agrochemicals, irrigate, and employ soil conservation techniques, as shown in many other studies [57,88–90].

Similar to [42,90], we found that ongoing demographic shifts and out-migration to urban centres means labour availability (e.g., to maintain irrigation canals or terraces) is another growing challenge for farmers left behind in rural origins. This trend is situated amidst the larger context, where Tanzania is currently experiencing a dramatic movement in labour out of agriculture to higher-return sectors. Meanwhile, commercial farms are increasingly claiming a prominent role and competition over land and water is growing [90]. Compounding these factors is the widening inequality associated with the coronavirus pandemic. Health and economic pressures intersect with climate shocks, and farmers have been among those that have borne the brunt—struggling to sell their produce with plunged consumer demand and changing export markets in Tanzania [91].

#### *4.5. Limitations and Future Research Avenues*

Our study has limitations and points towards future research avenues. First, we acknowledge that the sample size of 300 households and 4 villages per site is not statistically representative of the entire population. Nevertheless, we believe that the trends observed are likely to be found in the larger population in these and other mountain regions in the Global South. Future research should consider more villages, a larger population, and more ecological and social contexts. Second, future work should sample female-headed households more exhaustively. Third, future longitudinal research could study local perceptions across different seasons and years. Nevertheless, as our study was conducted in 2020, which was a relatively wet year [92], we argue that perceptions of reduced rainfall are not necessarily related to the particular year but are more representative of a wider trend. Fourth, future research should consider the influence of other intrinsic and extrinsic factors, such as beliefs and intentions of individuals and households [93], access to market, extension services, crop insurance, infrastructure, other farming inputs, land transactions and consolidation measures, as well as agricultural policies [84].

## **5. Broader Implications for Policy and Practice**

Our findings have five major implications for policy and practice.

First, climatic changes are already perceived by local communities on Mount Kilimanjaro and the Udzungwa Mountains, which agree on reports by farmers in other mountains in Tanzania and East Africa. Notably, our participants reported a larger number of changes

and impacts. In mountain regions where complex topography and terrain causes different local climatic conditions [18], local insights based on long-term verified patterns should be used to test how models and their underlying hypotheses are built [94], particularly in meteorologically data-scarce regions or at fine geographic scales, as highlighted by other authors [31,58,95]. Capacity-building among national and regional meteorological departments and sustained interactions between diverse stakeholders can help increase the use of climate information while addressing issues of trust and reliability on the forecasts [96,97].

Second, local farmers utilise a range of adaptation strategies, some of which have evolved over long periods of time, and others which are in response to new emerging patterns [98]. To assess the scalability, sustainability, and replicability of adaptation strategies, each strategy should be evaluated depending on the technical, social, biophysical, infrastructural, economic, and regulatory contexts, communications, stakeholder involvement, and barriers such as financial, human, social labour, land, and access to rapid credit [56,99,100]. Policy interventions can build the adaptive capacity of high mountain communities by supporting social learning and farmers' ability to experiment [96,101], expand their social networks, access external support for nontraditional adaptations, and internally reflect on their adaptation practices [98].

Third, despite widespread climate change literacy, farmers prefer to use personal observations over meteorological forecasts. This highlights the need to tailor forms of dissemination to local needs in terms of timing (e.g., farmers indicated a 2-day forecast was too short notice to start preparing their fields), format (e.g., cost-effective radio communication; advice given by extension workers) [80], and spatial resolution (e.g., farmers suggested forecasts were too coarse to deal with farm-level decisions)—as shown by [102] in the case of rural farmers in West Africa. There is a need to ensure credible communication procedures of forecasts [103].

Fourth, our results show differences in adaptive responses according to wealth groups. Numerous studies have documented how wealthier households generally have more options for adaptation [31]. However, overall, our findings show that the story is not so simple. There is a need to consider wealth when designing adaptation interventions within and across study sites, as some wealthier households might have fewer options than poorer households. In light of growing inequality—particularly during the coronavirus pandemic and wider transformations in Tanzanian society, policies should carefully consider how wealth from subsistence and commercial farming and mixed sector off-farm income can be reinvested in locally produced, employment-intensive goods and services to reduce inequality, secure the livelihoods current and new workers, and drive intensification [90]. Better international cooperation and faster development action is needed to limit the loss of traditional knowledge in mountain farming communities [2,104].

Fifth, previous research has shown how many well-intended national policies for adaptation have prioritised large-scale infrastructure solutions or technocentric quick fixes. Such approaches typically fail to reach mountain communities, overlook the vital importance of context, and mobilise communities' profound attachment to nature from cultivating parcels of land on mountain slopes for centuries [27,65,105]. To identify promising future adaptation pathways, we recommend the use of a 'science with society' participative, transdisciplinary approach [104], an iterative process that brings together actors to engage in knowledge co-production. Appreciating cultural values helps build trust between local peoples and other agencies and provides a closer understanding of differentiated climate hazard exposure, vulnerabilities, risk, and resilience [19].

Nationally, insights are relevant for realising the plans of the Southern Agricultural Corridor of Tanzania public–private partnership's vision to introduce climate-smart practices to farmers, boost agricultural productivity, food security, environmental sustainability, and reduce poverty—particularly in the Kilombero cluster, which will influence the Udzungwa Mountains [56]. Results can inform Tanzania's National Adaptation Programme of Action– which explicitly mentions Mount Kilimanjaro, the Eastern Arc and Southwestern highlands, but not the Udzungwa Mountains [106]. Internationally, results

can inform ongoing processes to secure the resilience of Indigenous peoples living in mountains: not least of which are the Sustainable Development Goal 2 on Zero Hunger (target 2.3.2), Goal 3 on health and well-being, Goal 4 on education (target 4.5.1), and Goal 5 on gender equality and the need to 'leave no one behind'. Results also have relevance for Agenda 2063—and targets of climate-proof investments (aspiration 1.7); ensuring local people are appropriately consulted in landscape planning (aspiration 6); and that citizens are healthy, well-nourished, educated, and strong (aspiration 1.3).

#### **6. Conclusions**

This study shows how local communities' perceptions can be used to identify the nuances of the climatic changes and impacts already observed in mountain regions. It also illustrates how farmers are using a wide range of adaptation strategies, most of which focus on modifying farming practices and how wealth affects adaptation options. Understanding the local context is important in mountain regions [107], but some of the key considerations of the Chagga and Hehe (e.g., effects of wealth on adaptation) can help inform policy and practice in other mountains in Africa and beyond where such climate change risks are likely to be the most acute.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/ 10.3390/land10100999/s1.

**Author Contributions:** Conceptualisation, J.P.R.T., A.C.-S., and E.H.M.; Methodology, A.C.-S. and J.P.R.T.; Data gathering, F.M. and K.R.K.; Formal analysis, A.C.-S., F.M., K.R.K., and J.P.R.T.; Writing original draft preparation, A.C.-S. and J.P.R.T.; Writing—review & editing, K.R.K., A.C.-S., F.M., E.H.M., R.A.M. and J.P.R.T. Project administration and supervision, J.P.R.T., A.C.-S., and E.H.M. All authors reviewed the final draft of the manuscript. All authors have read and agreed to the published version of the manuscript.

**Funding:** We acknowledge funding from the UK Research and Innovation's Global Challenges Research Fund (UKRI GCRF) through the Development Corridors Partnership project (project number: ES/P011500/1). This work was also supported through the African Women in Climate Change Science Fellowship of the African Institute of Mathematical Sciences and the Next Einstein Forum, and the Climate Research for Development (CR4D) Postdoctoral Fellowship CR4D-19-21 implemented by the African Academy of Sciences (AAS) in partnership with the United Kingdom's Department for International Development (DfID) Weather and Climate Information Services for Africa (WISER) programme and the African Climate Policy Center (ACPC) of the United Nations Economic Commission for Africa (UNECA).

**Institutional Review Board Statement:** Ethical review and approval were received for this study by the Ethics Committee of the Department of Environment and Geography and Tanzania Commission for Science and Technology (COSTECH).

**Informed Consent Statement:** Informed consent was obtained from all subjects in the study.

**Acknowledgments:** We are deeply grateful to our study participants, who graciously shared their time, energy, and stories. We thank our field assistants and facilitators for making this research possible. Thank you to COSTECH for granting us a research permit, and local authorities from the eight villages in both Kilimanjaro and Morogoro regions.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

## **Analysis of the Spatial Variations of Determinants of Gully Agricultural Production Transformation in the Chinese Loess Plateau and Its Policy Implications**

**Lulu Qu 1, Yurui Li 2,\*, Yunxin Huang 2, Xuanchang Zhang <sup>2</sup> and Jilai Liu <sup>3</sup>**

	- Beijing 100101, China<EMAIL_ADDRESS>(Y.H<EMAIL_ADDRESS>(X.Z.)

**Abstract:** Exploring the gully agricultural production transformation and its influencing factors is of considerable significance to the evolution of the human–land relationship and multifunctional transformation of gully agriculture in the context of new development. This paper tries to reveal intensive land use under the background of population contraction in the Chinese Loess Plateau and its transformation trend by defining the gully agricultural production transformation (GAPT). Given the representativeness of land-use change in the loess hilly and gully region (LHGR) was taken as a case study, and ArcGIS spatial analysis techniques and geographically and temporally weighted regression model (GTWR) were used to detect the spatio-temporal differentiation pattern and influencing factors. The results show that: (1) GAPT shifts from the high elevation area of 1000–1300 m to the low elevation area of <1000 m, and the transformation process remains within the range of slope 0–20◦ and topographic relief between 40 m and 180 m. (2) GTWR coupled with time non-stationary and spatial heterogeneity has a better fitting effect, which verifies its applicability in the study of GAPT. Social and economic factors were the main driving forces of GAPT in Yan'an City in the past 20 years, and they were increasing year by year. (3) The spatial-temporal distribution of the driving factors of the agricultural production transformation in Yan'an City is different. The intensity of the population factor and the slope factor is always in the dominant position, and the high value distribution area of the land average GDP factor forms a funnel-shaped pattern of "core edge" in the north and the central and western regions, and its changes tend to "flow" to the core. (4) The gully agricultural production transformation can reflect the general law of rural land use transition in gully areas, and thereby provide policy ideas for gully development. Overall, this study's content can provide scientific guidance for the sustainable development of gully agriculture and the revitalization of watershed and land consolidation in gully areas.

**Keywords:** gully agricultural production transformation; rural development; sustainable land use; geographically and temporally weighted regression; gully land consolidation

## **1. Introduction**

The Loess Plateau is a special region integrating agriculture from farming areas to pastoral areas, ecologically fragile areas, and economically poor areas in China. Over a long period of time, the irrational use of local resources has caused vegetation degradation, soil erosion, and severe land productivity reduction [1,2], reflecting the contradiction between ecological protection and economic development, and a key area connecting precise poverty alleviation and Rural Revitalization [3]. Ecological protection, human land system coordination, and sustainable development are always the basic propositions of high-quality development in the Loess Plateau [4,5]. The loess hilly and gully region (LHGR) is characterized by undulating hills and gullies, and its unique geographical

**Citation:** Qu, L.; Li, Y.; Huang, Y.; Zhang, X.; Liu, J. Analysis of the Spatial Variations of Determinants of Gully Agricultural Production Transformation in the Chinese Loess Plateau and Its Policy Implications. *Land* **2021**, *10*, 901. https://doi.org/ 10.3390/land10090901

Academic Editor: Rob Marchant

Received: 18 June 2021 Accepted: 17 August 2021 Published: 27 August 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

characteristics have shaped a distinctive rural man land system [6]. In the process of social and economic development and urbanization, the loess hilly and gully region has suffered from the dual disturbance of natural ecology and human activities. With the implementation of the Grain for Green Project (GGP) and Gully Land Consolidation (GLC), the regional vegetation has been significantly improved, and the contradiction between people, food, and land has been gradually eased, the transformation and development trend of rural man land system in the loess hilly and gully region is obvious [7].

Since the reform and opening up, the operation characteristics of the rural man land system in the LHGR can be summarized into three stages: sloping agriculture stage, vegetation construction stage, and gully agriculture development stage. The process has involved the extensive planting and low income of agricultural production and then the sustainable saving of production practice, and the overall trend of transformation to modern agriculture [8,9]. At present, the related research on the rural man land system in the LHGR mainly focuses on the perspective of new types of business entities, ecological governance and industrialization, focusing on the spatial form of the core elements (settlement and land use) of the rural man land system [5,10,11], typical patterns [12–14], evolutionary process [15,16], dynamic factors and mechanisms [17,18].

Human activity in the Loess Plateau has a long and complicated history of more than a thousand years of human settlement [19]. In the new period, there is a shortage of high-quality cultivated land in the rural areas of the LHGR, the development space of construction land in the valley is limited, the ecological restoration of the gully region coexists with the rural decline, the slow development of the gully countryside and the "rural disease". Five problems, including high-speed non-agricultural transformation, overfast aging, deep poverty, severe fouling of soil and water environment, and increasingly hollowing land management of the countryside, are more prominent, and the development of the rural human-earth system in the LHGR urgently needs to be reconstructed [20,21]. With the continuous development of Metrology geography and human-earth system science, the research on the evolution of the human-earth system is constantly updated and in-depth, and the mutual feed and correlation between system elements are emphasized. The dynamic analysis of the system and the model methods such as GWR, ESDA, geodetector, and other model methods has been paid attention to by the academic circles [22,23], gradually embedding spatial factors into the system model to reveal the driving mechanism of the system factors.

The Loess Plateau can be divided into geomorphic zones on the basis of geologic structure and topography. However, terraces have often been built on the slopes of loess Liang or Mao to create fields for agriculture, and check dams have been constructed in gullies and valleys [24]. The evolution of the gully man land system with gully farmland as the core has both temporal and spatial attributes. The changes in temporal and spatial geographical location will cause changes in the relationship or structure among variables [25]. The non-stationarity of time (lag effect) needs to be included in the scope of the model. At the same time, the spatial and temporal dimensions should be included in the driving force analysis model. It is of great significance to explore the spatiotemporal characteristics and laws of the driving forces of rural land conversion. The rural man land system in the LHGR is a system with different spatial changes in nature, economy, and environment. Its focus is more dependent on the agricultural production of the gully farmland unit [26].

In this context, this research focuses on the evolution of the rural man-land system with gully farmland as the core. The key point is to analyze the temporal and spatial relationship between people and land in the system, and integrate it into a framework to explore farmland changes and the spatio-temporal influence mechanism, explore "cure" approaches, and prescribe the right prescriptions to consolidate the effects of the Grain for Green Project and Gully Land Consolidation, thereby contributing to rural revitalization and regional sustainable development.

In view of this, this paper intends to select 30 Landsat TM/OLI data in four periods of 1995–2000, 2000–2005, 2005–2010, 2010–2018, and apply the CART decision tree classification algorithm for remote sensing image interpretation to obtain the corresponding time series of gully farmland change information, realizing the identification of gully farmland and the driving force analysis of gully farmland spatio-temporal evolution, analyzing its influence mechanism. Thus, it provides a scientific reference for the sustainable development of gully agriculture and the revitalization of watershed in the Chinese Loess Plateau, especially in the LHGR. Implications for rural development policy related to land consolidation would be also addressed.

#### **2. Theoretical Analysis**

#### *2.1. A Theoretical Model for Gully Agricultural Evolution in Gully Areas*

Since the economic reforms and open-door policy were initiated in 1978, the agricultural development in the LHGR has generally experienced three stages: the traditional sloping agriculture stage, transition stage, and modern gully agriculture stage. Firstly, in the traditional sloping agriculture stage, the agricultural production was mainly on the slope, and sloping farmland was the primary source of production in the regional agricultural production system. Due to the relatively high altitude, the agricultural production was large-scale planting and little harvesting, and the land use was extensive. Secondly, with the large-scale GGP implemented in 1999, the sloping land was converted to farmland, and the scale of the area continued to decrease. The slope farming was gradually withdrawn, and vegetation replaced the sloping farmland. The surface transformed from "yellow" to "green", and agricultural production has now shifted from high-altitude slopes to low-altitude gully. The living spaces also gathered from high-altitude areas to low-altitude areas, and the gully had become the key area for the production and living in the LHGR. Thirdly, in order to develop the agricultural production in the gully, the GLCP's "consolidation of the gully to protect the ecology and creating land to benefit the people's livelihood" was implemented, achieving the coordinated development of ecology and production (Figure 1). In addition, through the GLCP, the comprehensive agricultural production capacity was improved, and the integration of "three industries" and the development of agricultural conservation were promoted [21,27].

**Figure 1.** The evolution theoretical model of the gully agricultural production development.

## *2.2. The Evolution of the Gully Agricultural in the LHGR*

Since 1999, the Grain for Green Project (GGP) in the LHGR, especially the Gully Land Consolidation (GLC) project in 2012, the gully rural human–land relationship has changed subtly), gradually shifting from the traditional sloping agricultural system to new gully agricultural systems; the agricultural system of the LHGR is transforming. The core of this transformation is to achieve the "win-win" goal of ecological and economic benefits, and mainly manifested in two aspects: food crop production of the gully farming system and ecological conservation of the gully forestry system in the LHGR. Thus, changes in land use patterns correspond to the transformation of functional effects driven by economic and social development and innovation that are compatible with the stage of economic and social development [20,28]. The traditional farming system in the LHGR is centered on the main production of food crops. As time goes by, the gully watershed becomes the distinctive land use pattern of the gully area. Adapting to the stage of social and economic development, agricultural production gradually transforms from extensive development and utilization of land expansion to intensive utilization under an ecological economy. The goal of this study is to deconstruct the multiple elements of the agricultural production system in gully areas from the perspective of farmer subject and regional production space and type change, evaluating the interaction mechanism that drives changes in the human–land relationship in the LHGR.

#### **3. Materials and Methods**

#### *3.1. Geography of the Study Region*

The loess hilly and gully region of Yan'an City is located in the center of the Loess Plateau, with a total area of about 18,729 km2. It is the combination of the middle and upper reaches of the Yellow River Basin and the northern agro-pastoral region, covering eight districts and counties in the north-central part of Yan'an: Baota District, Yanchang County, Ganquan County, Ansai District, Yanchuan County, Zichang County, Zhidan County, and Wuqi County (Figure 2). The topographical conditions of the study area are complex, with crisscrossing ditches, streams, slopes, beams, and ridges, and the topography is very typical. Since 1999, as the first batch of pilot areas for the Grain for Green project and Gully Land Consolidation, this area has taken the lead in realizing the transformation of the surface color from "yellow" to "green" and the production space from "sloping" to "gully". In the context of urban-rural integration and the high-quality development of the Yellow River Basin, it is typical and representative to carry out the research on the characteristics and mechanism of gully farmland transformation in the loess hilly and gully region of Yan'an City [29].

**Figure 2.** Location and investigation sites of the study area. (**a**) shows the digital elevation map of the study area, (**b**) shows the distribution of investigation sites in the study area, and (**c**) shows the loess hilly and gully region.

### *3.2. Data Sources and Processing*

The data used in this study are mainly divided into two categories: remote sensing data and socio-economic data. The socio-economic data were mainly obtained through field surveys, field interviews, and statistical yearbooks. Field interviews mainly include four dimensions: natural ecological dimension, farmers' livelihood source and improvement dimension, agricultural planting and development dimension, ecological landscape, and environmental safety dimension. The remote sensing data originated from the United States Geological Survey (USGS) during 1995–2018 (http://glovis.usgs.gov/) and Landsat image data with a spatial resolution of 30 m from 1995 to 2018 were selected as the basic data for extracting gully farmland. The land use types were interpreted by referring to the classification method of CAS's resource and environment information database in 2018 (http://www.resdc.cn/). Besides, the crop vegetation grew luxuriantly from April to October, and the identification accuracy of the image was higher than in other months. After testing, the accuracy of the land-use types was over 85%.

The 30 m resolution digital elevation data in 2018 (http://www.gscloud.cn/), Google Earth images and high-resolution land classification data were used as training samples and verification basic data. Additionally, the selected vector road network data come from Open Street Map website in 2018 (http://www.openstreetmap.org), and the socioeconomic data come from the Yan'an Statistical Yearbook and field investigation, and the socio-economic data was spatialized based on ArcGIS 10.4.

#### *3.3. Research Methods*

#### 3.3.1. CART Decision Tree Algorithm

The classification methods such as artificial neural networks (ANN), decision trees (DT), and support vector machines (SVM) are widely used in the classification of remote sensing images [30]. Among them, the decision tree classification method can effectively excavate the spectral characteristics of the image and can solve the problem of the overlap of the remote sensing image spectrum to a greater extent. The commonly used algorithms in the DT decision tree classification method are: C4.5, CART, ID3, etc. [31], and the CART algorithm uses the Gini coefficient of the balanced distribution of income in economics as the criterion for determining the optimal test variable. The formula is as follows:

$$\text{Gini (D)} = 1 - \sum\_{i=1}^{n} p\_i^2 \tag{1}$$

In the formula, D is the data set, *n* is the classification number, and *pi* is the distribution probability.

Compared with other decision trees, the model of the CART decision tree is simple. The classification threshold is determined by training samples, and the decision tree is automatically established (Figure 3). Therefore, it is less affected by other factors, and the recognition accuracy is higher.

**Figure 3.** Decision tree classification process. Note: B1–B8 respectively correspond to the 8 bands of the composite image.

### 3.3.2. Gully Farmland Classification

Gully farmland can also be called "dam land" (Figure 4), which is a new form of agricultural production land of different types (Table 1) [32]. The identification rule of gully farmland is as follows: Step 1: The range of farmland in 2018 is extracted as the background data of gully farmland, and then the pixel (T) of other years is obtained based on the change of the range of the farmland. Besides, the suspected gully farmland range with the specific research period (1995–2018) is obtained based on the above judgment. Step 2: Based on the range of gully farmland in year (t-1), and the overlapping part of the suspected gully farmland in year (t-2) and the suspected gully farmland in year (t-1) is extracted again. This part is judged to belong to the range of gully farmland in year (t-2). Step 3: According to this method, the range of gully farmland in a continuous sequence of prescribed years is obtained.

Typical gully remote sensing image Local remote sensing image

The overall landscape

**Figure 4.** Gully farmland landscape in loess hilly and gully region.

## **Table 1.** Reference standards of GUFL types.

Note: Fruit forest zones (FFZ); Greenhouse vegetables (GV); Pond farming (PF); Grain planting (GP); mulberry leaves (ML); tobacco (T).

## *3.4. Modified Binary Logistic Regression Model*

Binary logic regression is a valuable tool to analyze the relationships between dependent variables and independent variables, which is widely used in the analysis of land use driving forces [33–35]. Furthermore, spatial autocorrelation between land use transfer probability and land use types with adjacent grids couples to modify the deficiency of simple logical regression analysis. Comprehensively considering the spatial representation and driving factors difference and combining with the actual situation in the LHGR, 21 indicators of four driving factors are selected. According to the relevant research experience and the actual situation of the development in the LHGR, this paper attempts to comprehensively select the factors that affect the development of gully agriculture from different dimensions aspects: nature, social economy, and humanities. Among them, including socio-economic (SE), hydrothermal condition (HC), natural background (NB), and location condition (LC) (Table 2). Overall, the analytical principle of the driving mechanism is to explore the main driving factors of gully farmland change under the existing social-ecological background, and the time limit of each index is 2018.

**Table 2.** The driving index of GAPT.

## *3.5. Geographically and Temporally Weighted Regression Model*

Unlike the widely used Geographically and temporally weighted regression (GWR) model, which only takes spatial variation into account when estimating an empirical relationship [36], GTWR captures spatio-temporal heterogeneity based on a weighting matrix referencing both spatial and temporal dimensions. In this study, a GTWR model was fitted using the following structure:

$$Y\_{i} = \beta\_{0} \left( X\_{i}^{t}, Y\_{i}^{t}, T\_{i} \right) + \sum\_{m} \beta\_{m} \left( X\_{i}^{t}, Y\_{i}^{t}, T\_{i} \right) X\_{im} + \varepsilon\_{i} \tag{2}$$

where: *Yi* is the value of monitoring site *i*; (*Xi, Yi*) represents the central coordinates of monitoring site *i*; *β*<sup>0</sup> denotes the intercepts at a specific location (*Xi*, *Yi*) and year *T;* and *β*1–*β<sup>m</sup>* are the location-time specific slopes for 1-m, respectively. *ε<sup>i</sup>* is the error term for sample *i*.

## **4. Results**

## *4.1. Spatial Distribution Characteristics of GAPT*

The center of gravity coordinate migration of gully farmland in 1995–2018 is shown in Figure 5. From 1995 to 2000, the migration direction of the gully farmland in the study area tended to the northwest of the region as a whole. After 2000, the center of gravity of the gully farmland showed a divergent distribution trend of migration to the surroundings. In the past 20 years, the gravity center of gully farmland moved northward, among which, Wuqi County, Baota County, and Ansai County have high consistency in migration direction. The migration direction of gully farmland in Baota County is always northward, while Wuqi County and Ansai County move southeast. The spatial conversion intensity of the gully farmland in Ganquan County and other counties is relatively intense, and the migration direction remains unchanged before 2005, and gradually expands to the opposite direction after 2005. In other counties, the direction of change in the stages of gully farmland is divergent, and the overall characteristics are relatively insignificant (Figure 5).

**Figure 5.** The center of gravity coordinate migration of gully farmland in districts of Yan'an City.

The spatial distribution characteristics of different types of gully farmland are shown in Figure 6. The growth of the gully farmland is mainly distributed in the relatively gentle low-altitude flat dam area, including elevation (700–1300 m), terrain relief (40–180 m), and slope (0–20◦). The growth of oilseeds (IV), mulberry leaves (V), and tobacco leaves (VI) were negatively correlated with the distance between rivers, county roads, and provincial roads, and the increase was mainly within 5 km from rivers and 18 km from provincial roads. In general, the influence of the regional centers of townships on the cultivation of gully farmland was more significant than that of the regional centers of districts and counties. There was a strong market location orientation between economic fruit forest (I) and greenhouse vegetables (II), which was more manifested as agglomeration within the township, showing a "bottom-up" market location-oriented law.

**Figure 6.** Spatial variation of gully farmland in Yan'an City. Note: Fruit forest zones (I); Greenhouse vegetables (II); Pond farming (III); Grain planting (IV); mulberry leaves (V); tobacco (VI).

## *4.2. Analysis of the Main Influencing Factors on GAPT*

Drawing on the enlightenment of existing research and the relevant methods [37–39], the gully farmland expansion region (1), contraction region (−1), and no obvious change region with a total of 4861 in the four periods of the study area were analyzed in ArcGIS 10.4. The numbers of the three types of regions are roughly equal to ensure the stability of the coefficients of the explanatory variables in the model. During the study period, the sample data were processed by GTWR, and all passed the significance test (*p* < 0.05) (Table 3). Moreover, a linear regression OLS model was used to verify the applicability of GTWR. It could be seen that the R2 value of the GTWR model was higher than that of the OLS model, and the Akachi information criterion (AIC) index was lower than that of the OLS model (Table 4). AIC was an important indicator of good model fitting, and the smaller the value, the higher the accuracy [35]. The AIC value (11.38/11.54 = 98.61%) and R2 value (0.22/0.14 = 157.14%) of the GTWR model were significantly higher than OLS. Since the GTWR model added a time dimension, it was higher than that of the traditional model in dealing with spatio-temporal non-stationary data.

**Table 4.** Comparison of model diagnostic results.

Comprehensively considering the spatial representation and driving factors difference and combining with the actual situation in gully areas [40,41], 21 indicators of four driving factors were selected, including nature, economy, population, transportation, location, and policy. To eliminate the influence of multi-factor collinearity, this paper firstly analyzed the correlation of the driving factor set, and the result showed that the correlation of selected factors was significant. Then, principal component analysis was performed on the driving factor set, and the load matrix and coefficient matrix of each component were obtained. Moreover, regression analysis was used to obtain the comprehensive score value of each sample. The dominant factors of driving variables are shown in Table 5.

**Table 5.** Dominant factors of principal component driving variables.

#### *4.3. Spatio-Temporal Differentiation of Influencing Factors of GAPT*

According to Figure 7, it can be seen that the high value of F1 in the study area during the 20 years has shifted from west to east first, and then expanded from south to north. The driving difference is reduced and gradually tends to be balanced, eventually forming a clustering and distribution situation around the Baota district. The positive high value from 1995 to 2000 was concentrated in Yanchang-Ansai-Wuqi, while Zichang, Yanchuan, and the central area of Baota were relatively low value. From 2000 to 2005, the negative high value shifted to Zhidan, and the positive and negative effect intensity was significantly different between northwest and southeast. The positive high values from 2005 to 2010 were mainly distributed in Ganquan and Ansai, and the difference in the driving factor value range narrowed and tended to be balanced. The pattern evolution from 2010 to 2018 gradually formed a circle with the Baota as the core, the positive high value of Ganquan, and the negative low value of Yichuan. From the perspective of the evolution of economic factors, the economic development of the Baota has a higher spillover effect on the expansion of gully farmland. The location dominant factors (F3) were quite different, and the positive effect of the township was stronger than that of county administrative centers in which population and economy were the two most significant factors. The population was also the direct driving force for the conversion of gully farmland, and economic development was the core driving force. The location characteristics directly affected the population flow and capital flow and promoted the transformation of gully farmland. In addition, some related policies (Grain for Green project) and engineering measures (Gully Land Consolidation) had accelerated the conversion of gully farmland, thereby promoting the spatial transformation of gully farmland.

**Figure 7.** *Cont*.

**Figure 7.** Spatio-temporal variation pattern of dominant factor coefficient of gully farmland expansion.

According to Figure 7, the changes of F3 and F5 were relatively similar. Normally, natural factors had no significant effect on gully farmland conversion from 1995 to 2018, and the overall north-south changes in space were obvious, mainly due to the difference in topography and geomorphology in the north and south of the study area. According to F4 (traffic location dominant), the influence intensity distribution of distance to national road and distance to main highways presents two significant characteristics: First, it formed a distribution pattern of high in the south and low in the north at the regional level, and the high-value region gradually shifted from the southwest to the east of Yan'an City over time. Second, some high-value regions were scattered in some county boundaries, such as the southwest of Yanchang near the border of Baota District, the northwest border of Yanchuan, the southwest part of Baota, and the border area of Ansai and Zhidan.

The distance to the provincial road (F4) had an overall evolutionary trend toward being high in the central area and low in the surrounding area. From 2000 to 2005, the vertical expansion to Baota district was a "W"-shaped high-value area layout. From 2005 to 2018, it further expanded to the north to Ansai County, and the positive effect on the expansion of gully farmland in the Baota area was further enhanced. Water dominated factor (F10) was mainly distributed in Wuqi County and Yanchang County along Luo River and Yanhe River from 1995 to 2000. The high-value area gradually moved down from 2000 to 2005. Since 2005, the scope of high-value areas was further expanded, showing a distributed pattern of dots in the area.

From the perspective of the driving force change range of gully farmland conversion, the largest growth of driving factor was the economic factor of per land GDP. From the perspective of the regional change pattern of the driving force, various geomorphic factors have different effects on the expansion of gully farmland, which was closely related to the geomorphological differentiation characteristics of the gully area. The impact of economic development on gully farmland presents a "core edge" funnel-shaped distribution, and its changes tended to "flow" to the core. The influence of the neighboring factors on the roads in Yan'an had its own characteristics, which was closely related to the spatial distribution of the road network. Areas with significant changes were concentrated in the central area of Yan'an City and focused on the Baota area, roughly forming a "T"-shaped distribution pattern.

## *4.4. Analysis of GAPT Response Mechanism*

The factor coefficients of the four stages in the study area from 1995–2000, 2000–2005, 2005–2010, and 2010–2018 were statistically separated, and the average factor coefficients of each stage were obtained as shown in Figure 8.

**Figure 8.** Time series distribution of factor average coefficient change in each period.

(1) Judgment of the whole area core factors: From 1995 to 2018, the conversion of gully farmland of the study area was mainly based on the population and GDP per land area of socio-economic factors. The slope of natural factors and the distance from the water area of neighboring factors were the core influencing factors. Among them, the population factor and slope factor had always been in a dominant position, and they were the main driving factors for the conversion of gully farmland. The intensity of population was higher than that of the slope during 1995–2000 and 2010–2018. The driving effect of the slope on gully farmland conversion during 2000–2005 and 2005–2010 was obvious.

(2) Temporal evolution of the whole area core factors: Slope was the most influential factor in the conversion of gully farmland. The intensity of its effect was a fluctuating and rising trend from the beginning of 1995 to 2010, and the intensity was the greatest in 2010. The relationship between population and the transformation of gully farmland was negatively correlated in the first 10 years. The overall effect of the population on the expansion and transformation of gully farmland showed a downward trend. Slope aspect and topographic relief factors were positively correlated with gully farmland shrinkage. From the whole study period, the intensity of aspect effect intensity first decreased and then increased, and remained flat. The influence of traffic neighborhood factors generally showed a slight decrease. The gully was negatively correlated with the transformation of farmland shrinkage. The distance from the center of the county was negatively correlated with the shrinkage of gully farmland. The intensity of the impact first decreased and then increased, showing an overall increasing trend. There was a positive correlation between the distance from the water area and the center of town and the change of gully farmland shrinkage in Yan'an City, and the intensity of these factors was gradually declining.

At the county level, it was the basis of regional differentiation governance to identify the driving force of gully farmland contraction. In order to identify the difference in the conversion drive of the gully farmland between the counties and determine the time series change of the drive strength, the incidence of factors driving the transformation of gully farmland contraction was compared among the counties as follows (Figure 9). Among these, the positively related variables were directly taken as logarithm exp (g), and negatively related variables were converted into positive cumulative values through the factor of 1/exp (g).

**Figure 9.** The positive incidence rate of GAPT.

The core driving factors for the conversion of gully agricultural land in the past 20 years were population, topographic relief, slope, aspect, and GDP per land. The first driving factor of Yanchang and Zhidan was population. The per land GDP in Baota was the primary driving factor before 2000, and then population became the primary driving factor after 2000. Moreover, Ganquan, Ansai, and other counties showed obvious phased characteristics. In regional spatial distribution, the counties with strong population and economic driving force were concentrated in the east and middle of Yan'an. Most of the counties with strong driving forces such as slope, aspect, and topographic relief were in the west. There were significant differences between the eastern and western regions at the county level. From the perspective of the time sequence of the slope of the dominant factor affecting the conversion of gully farmland. Except for Ganquan, the driving force for the conversion of gully farmland by the slope of the counties had not declined. It showed that the restriction of natural background conditions was still strong.

## **5. Summary and Implications**

#### *5.1. General Law of Rural Land Use Evolution in Gully Areas*

Rural development gets a lot of attention around the world. However, compared with cities, rural areas are backward for reasons that include migrant mobility, poverty, labor quality, biased policy, and week rural land management [42]. Generally, the cultivated farmland within the rural man-land system in plain areas is evenly distributed [43], and the effect of intensive cultivation and utilization is significant, which is easier to integrate the land resources than that in hilly areas.

Hilly rural settlements and agricultural land distribute scattered, and the tillage radius is wider than that in the plain. The unique geographical characteristics shape the unique rural man land system to a certain extent. In addition, environmental elements are the essential factors influencing spatial distribution [44]. The natural environment, including temperature, precipitation, terrain, and rivers, is the basis for the formation and development of agriculture and the countryside [45]. The farming environment created by conscious social labor in the process of agricultural production has a significant impact on its development and distribution [46]. In the socioeconomic environment, dynamic changes in the spatial pattern of GAPT are determined by factors of urbanization rate, main highway density, per land GDP, the proportion of primary industry employment and land consolidation planning, etc.

Moreover, the influence of the commodity economic location environment on rural land use and development continues to increasing [47]. Gully agriculture tends to develop in the win-win situation of ecology and economy. GAPT is comprehensively affected by a variety of internal and external factors, which not only depend on the external resource and environmental status of the gully, but also have a comprehensive effect on the gully's rural internal economy, society, ecology, and culture. On the micro-valley level, GAPT is related to the benefits and efficiency of exchanging energy. On the macro-regional level, GAPT is a summary and generalization of the spatial differentiation of gully regional production-living-ecology.

This paper represents the general law of gully rural land use in LHGR, and the process of GAPT can reveal the LUT mode in China's loess plateau and modern gully man-land system development in LHGR (Figure 10). With rural population outflow, GAPT will promote the gully living space reconstruction and intensive use of production, and the man-land system development tends to be coordinated and efficient. Specifically, GAPT reflects the characteristic of gully agriculture crops, ecological maintenance, and rural living environment construction in LHGR. The combination accelerates rural transformation in LHGR, which has an indicative implication for the high-quality development of agriculture and rural in China's Yellow River Basin.

**Figure 10.** The transformation model of gully rural.

## *5.2. Policy and Practical Implications of Gully Agriculture Development*

According to the characteristics of GAPT types, rational rural land use and agricultural planning can improve the gully agricultural production environment by enhancing the conversion rate of GAPT and improving regional infrastructure supporting engineering. Policymakers should establish adaptive measures for gully land consolidation and formulate structural readjustment measures for different gully agricultural planting types to promote intensive land use.

The phenomenon of GAPT in the LHGR reveals the general law of the evolution of the river basin and rural man-land system driven by ecological construction and gully land consolidation engineering, that is, ecological security and food security. In the new era, the gully agriculture of the Loess Plateau should be based on the economic-ecological "winwin" benefits, focusing on ecological civilization and green sustainable development, and using the two major projects—the Grain for Green project and Gully Land Consolidation as important platforms for transformation and development, and coordinating watersheds up-and-down collaboration, gully and slope collaboration, multi-scale classification and coordination, giving full play to the technical support role of geographic engineering, and in-depth exploration of new ways to optimize gully agricultural production methods and innovate business management models. As the goal, the ecological economy is "win-win" as the direction, and modern geographic engineering is the technical means to finally achieve high-quality development of quality benefits and development efficiency [48]. GAPT in the LHGR has promoted the integration of policies such as the Grain for Green project and Gully Land Consolidation. On the premise of protecting and improving farmers' livelihoods, it is important to promote the integration of the three beings (production, living, ecological) in the gully region and bring to light the significance of promoting the integration of the three industries and high-quality development of the Loess Plateau.

#### *5.3. Limitations and Prospects*

There are still some shortcomings. For data and technical methods, this paper uses remote sensing image data from April to October to interpret the types of gully agriculture, and the crop and grassland are easy to distinguish. There may be some misclassification conditions. In addition, the spatial-temporal geographic weighted regression model realizes a comprehensive measurement in the spatial-temporal perspective and more precisely describes the temporal and spatial evolution characteristics of the driving factors and the dominant driving mechanism. However, how to improve the accuracy of image classification based on the spatial location information of ground objects and further optimize the "circle-belt-region" multi-level spatial structure system of human land system in rural areas of loess hilly and gully region [5,49], still need to be further explored.

It is undeniable that GAPT has significant positive economic benefits to gully rural areas, but whether it can bring ecological benefits is still unknown. In addition, policy as a non-quantitative factor is essential and crucial in GAPT. With the development of gully land consolidation engineering, the amount of gully farmland is also changing. At the same time, the willingness of farmers and the reform of property rights are also factors to be considered. Furthermore, by selecting typical types of areas, the process and mechanism of the changes of different types of regions are explored from the micro-scale, and then the scientific principles of the rural man land system based on gully farmland and the mechanism of rural "human-geosphere" are revealed should also be the focus of future work.

## **6. Conclusions**

Under the background of "Grain for Green" (GFG) land management policies and the rural population transfer, the focus of gully farmland distribution gradually migrates down to low-altitude flat dam areas. Spatio-temporal distribution of different driving factor coefficients of GAPT has different effects. The intensity of the population and slope is always in the dominant position. The high-value area of GDP per land forms a funnelshaped pattern of "core edge" in the northern and central-western regions, and its changes tend to be the core "flow". In the Loess Plateau, GAPT is driven by many factors, and the regional background differences and different actors will promote the development and contraction of gully farmland in different directions, but in principle, they are driven by the major national and regional development policies. In the development process, we should pay attention to the dual orientation of ecology and economy, maximize the sustainable use of gully farmland, explore the coordination and optimization of gully farmland and human settlements, and realize housing and industry synergy.

Current studies on gully agricultural production on the Loess Plateau based on gully land consolidation have good theoretical and practical significance. Taking a typical loess hilly and gully region of Yan'an City located in the center of the Loess Plateau as the case study area, this paper revealed the intensive land use under the background of population contraction in the Chinese Loess Plateau and its transformation trend by defining the gully agricultural production transformation (GAPT). To some extent, the multiple elements of the agricultural production system in gully areas from the perspective of farmer subject and regional production space and type change has been deconstructed towards the gully agricultural production development. However, more empirical studies in the gully region are still urgently needed to verify this key topic. In addition, we proposed a theoretical model for gully agricultural evolution in gully areas, this theoretical model needs further verification. It is worth mentioning that, we have evaluated the interaction mechanism that drives changes in the human–land relationship in the LHGR. We will further observe, monitor, and reevaluate the GAPT and report new progress in the near future.

Overall, the study area's typicality reflects the general law of rural land use evolution in gully areas. Policymakers can implement the adaptive measures for modern gully land consolidation measures and formulate structural readjustment measures for different gully agricultural planting types to promote intensive land use, ultimately achieving a balance of the regional modern rural man-land relationship.

**Author Contributions:** Conceptualization, Y.L.; Formal analysis, J.L. and L.Q.; Investigation, X.Z.; Y.L. and L.Q.; Software, Y.H.; Writing—original draft, L.Q.; Writing—review and editing, Y.L. and L.Q. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Key Program of the National Key Research and Development Program of China (Grant 2017YFC0504701) and the National Natural Science Foundation of China (Grant No. 42101202, 41801172 and 42061037) and Fundamental Research Funds for the Central Universities (2021CDJSKJC02).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** The insightful and constructive comments and suggestions from the anonymous reviewers are greatly appreciated.

**Conflicts of Interest:** The authors declare no conflict of interest.

## **References**

## *Article* **Land Abandonment in Mountain Areas of the EU: An Inevitable Side Effect of Farming Modernization and Neglected Threat to Sustainable Land Use**

**Thomas Dax 1,\*, Karin Schroll 1, Ingrid Machold 1, Martyna Derszniak-Noirjean 2, Bernd Schuh <sup>2</sup> and Mailin Gaupp-Berghausen <sup>2</sup>**

**Abstract:** In a period of rising concern for sustainable land management systems to achieve food security at a global scale, land-use changes demand increased attention. This study assesses the past observations and future risk calculations for land abandonment across European regions, highlighting the particular risk for mountain areas. It draws from a study commissioned by the European Parliament to investigate the situation and probability for high and very high risk of land abandonment until 2030. Revealing that land abandonment is at three times higher risk in mountain areas than in non-mountain areas, the need for action to cope with this pressure is the core result. We reveal that the high disparity in agricultural competitiveness between regions (at fine geographical scale) is the main driving force leading to the spatially uneven performance of land management. Viewing this wide set of drivers and mitigation options, land abandonment is understood as the outcome of a multitude of factors of socio-ecological systems and a combination of farm-specific, internal regional and trans-regional factors. The present dominance of narratives of effectiveness leaves little scope for mountain regions under threat of abandonment and marginalization. In this situation, policy reform would address the issue but this might turn out to be influential only if the complex nature and trade-off of the comprehensive policy framework are prioritized.

**Keywords:** land-use change; land management; spatial effects; small-scale context; socio-economic drivers; policy assessment; CAP; policy trade-offs; mitigation policies

## **1. Introduction**

In a period of rising concern for sustainable land management systems to achieve food security at a global scale, land-use changes demand increased attention. However, these are not merely linked to quantitative aspects of food provision, but inherently refer to the beneficial effects linked to socio-ecological systems of primary production. The threat of neglecting such crucial issues of food and forestry production is at the core of current discussions about how to realign dominating features of sector activities and respective policies [1] with sustainable development goals. Over past decades, with the emergence of agricultural surplus and due to the high divergence of farming conditions, productivity and viability, more and more agricultural land have become fallow land and are regarded as "abundant" for market production. This process has led to land abandonment processes in almost all European countries [2]. Due to the wide scope of drivers, it occurred gradually at the plot level and was felt particularly at local scales, thus hampering general recognition and awareness of the phenomenon.

Following deep concern over the concentration of agricultural production [3,4], the impacts of modern agribusiness on the cultural basis of farming and its close relationship

**Citation:** Dax, T.; Schroll, K.; Machold, I.; Derszniak-Noirjean, M.; Schuh, B.; Gaupp-Berghausen, M. Land Abandonment in Mountain Areas of the EU: An Inevitable Side Effect of Farming Modernization and Neglected Threat to Sustainable Land Use. *Land* **2021**, *10*, 591. https://doi.org/10.3390/land10060591

Academic Editors: Rob Marchant and Aida Cuni-Sanchez

Received: 10 May 2021 Accepted: 1 June 2021 Published: 3 June 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

with place-specific land-use systems [5], land abandonment was an early issue of farm modernization prospects. As early as in the 1970s, agricultural policies in the European Economic Community (EEC), but also in several European countries (Switzerland, Austria, Norway), recognized the limited farmland viability in certain regions and created policy instruments for "less-favored areas" to cope with these challenges [6]. In the initial concept of that policy, it was understood as a farming system under threat, being particularly "marginalized" and prone to lose, with the ongoing land abandonment, its viability and functions for the society [7]. The ongoing technological developments and market changes further increased apprehension about the continuation of land management, and through it, the preservation of positive environmental and socio-economic benefits of agricultural activities [8] in these "disadvantaged" areas.

Pressure for giving up land further increased due to continuous productivity increases and the intensification of agriculture. However, propensity for land abandonment is not equally distributed across the regions, land types and management systems of the EU [9]. While these processes of land use are not exclusive to European regions, the legacy of place-specific land-use patterns marked by significant diversity in land cover and land management is particularly strong here. Moreover, it is historically linked to rural livelihood features that translate to both high attachment to the specific regions and attractive assets seen in those "rural amenities" by non-rural populations and visitors from other areas. Rural amenities are thus seen as strongly enhancing tourism development (of different forms) in close relationship to specific land-management systems [10].

Studies on transitions of land management regimes over long time periods recognize the ongoing but uneven intensification process of agricultural land use and urban growth. Jepsen et al. [11] observe that science has traditionally addressed mainly the spatial determinants of land-use change, neglecting, to some extent, the array of other aspects influencing land-use decisions. Moreover, as historical data are often unavailable, the periods of land-use change observation tend to be short and might overlook the influences of contrasting and interrelated drivers of land-use change. Despite large-scale processes of industrialization across European regions, high path dependency preserved a "marked spatial heterogeneity in land-management regimes" until around 1950 [11] (p. 63). Since then, periods of collectivization, intensification, de-intensification and commercialization and rising environmental awareness have led to divergent and spatially very different developments. A recent study of land-use change across Europe depicts the substantial loss of utilized agricultural area (UAA) over recent periods [12]. This overall trend is linked to three main components:

While the general land-use changes toward urbanization or afforestation are obvious trends, the critical phenomenon of land abandonment may be mostly linked to the third aspect. It is captured by the formulation in the recent Joint Research Centre's (JRC) study, providing the following most commonly used definition of land abandonment: According to the authors it "refers to land that was previously used for crop or pasture/livestock grazing production, but does not have farming functions anymore (i.e., a total cessation of agricultural activities) and has not been converted into forest or artificial areas either" [12] (p. 1).

Analyzing the causes and main influences on land abandonment processes requires facing contrasting land-change tendencies across spatial units. What is more, trends might be divergent for small, medium and large scales, pointing to a diverse relevance of drivers for different areas and levels. Thus, understanding land abandonment as "a complex multi-dimensional process with interlinked economic, environmental and social aspects" [13] (p. 2) implies its heavy impact on regional sustainability pathways [14]. While its occurrence and relevance are particularly linked to specific and changing spacetime contexts [15], many studies acknowledge a wide range of influencing drivers and a high degree of interrelated factors impacting land managers' decisions [13,16–18]. With the rising concern for food security at a global scale [19] and the rising focus on food sovereignty [20,21], the loss of agricultural land has become a crucial issue in many regions. The increased attention is palpable through a number of case-specific and comparative studies at the European scale [22], highlighting the great diversity of drivers and trajectories in various spatial contexts (for example, explored in another Special Issue of this journal, *Land* [23]). They reveal that a complex interplay of a wide set of "sociodemographic, economic, technological, policy and institutional factors but also cultural factors may drive (land) abandonment" [23] (p. 4).

Even if these studies regularly underpin that landscape changes are highly dependent on specific political, institutional, economic, cultural, technological, natural and spatial factors as drivers [24], there is a conspicuous spatial pattern for land-abandonment processes. Comparative monitoring of the extent of past changes and model calculations of impending land abandonment risk [12] point to mountain areas as specific, remote locations that are particularly affected. They underline that land abandonment has to be seen as "a complex multi-dimensional process with interlinked economic, environmental and social aspects" [13] (p. 2) that plays out in areas of natural constraints much more than in other small areas limited in their production capacity by local biophysical or other development conditions.

It is a main research hypothesis that a spatially differentiated view to focus on land use and landscape development is required, and analyses on the implications for mountain areas are a priority concern where land management challenges cumulate.

With a focus on the complex set of socio-economic pressures on mountain economic activities and living conditions in those regions, the particularly high-risk level is no surprise. Klein et al. [25] provide survey data from 57 mountain 'socio-ecological systems' from all parts of the world and reveal similar and diverse problem patterns and opportunities across these mountain locations. In terms of land change, there is a high degree of stressors and challenges for ecosystem services provision observed in many respects [26]. Highlighting the particular, large array of pressures but also a simultaneous, large scale of mountain-specific opportunities, these tensions are framed as 'paradoxes' that create substantial challenges for sustainable development options. As a specific concern, land abandonment may be more pronounced in areas with limited production capacity and productivity, e.g., in areas facing natural constraints (ANC). In particular, agriculturally less-favored areas, such as mountain areas, islands and other remote parts of Europe, are reported to face significant challenges in retaining vital farming structures and have long been confronted with a steady decrease in agricultural land use [27]. Location in socio-economically disadvantaged areas add to these "handicaps", impeding swift market integration, access to value chains or networks. Instead, the focus is on niche development and innovative, quality schemes of mountain products [28,29] and the elaboration of specific mountain value chains [30] to bypass unfavorable challenges of mass production and remoteness. However, land abandonment is not only a sector issue, but has wider implications for societal development, ecological performance, and the rural fabric [31]. As farm holdings with reduced viability prospects are often regarded as particularly prone to abandonment processes, particularly wide-spread, small-scale structures (e.g., in Southern

or Eastern Europe) could aggravate negative regional performance of the whole economy and contribute to demographic decline, often referred to as 'shrinking rural regions' due to the long-time persistence of downward trends [32].

In many remote and mountain contexts, the progressive reduction in farm numbers and agricultural land use has implied a loss of important landscape features and ecological performance [7]. This structural change has been accompanied by agricultural land abandonment in substantial parts of the EU's rural regions. Particularly, the focus on intensive land management systems has propagated these adverse effects and is adding to the pressures experienced in low-productivity areas such as the mountains. Any strategy to cope with abandonment aspects need to address the balance of intensive and extensive areas, and the capacity to achieve a shift toward sustainable production management systems, including the spatially uneven potentials and capacity to shift toward sustainable pathways.

Scale and location are thus crucial considerations when assessing the drivers, as well as the effects (both negative and positive) of land abandonment. In this paper, we focus on the large-scale observations of land abandonment for mountainous areas, as these are primarily affected by the ongoing tensions and for decades have been threatened by these processes. We begin by synthesizing quantitative model results on risk assessment across the EU and revealing the particularly high threat for mountain regions. The strong pressure on land-use change is linked to a wide set of drivers, implying the effects of natural constraints for land uses and harmful effects to other sectors. In the discussion, emphasis is put on highlighting the dichotomous trends in land management, favoring a continuous orientation toward competitiveness without responding adequately to rising social and ecological challenges. This will be taken up in the conclusion to underpin the necessity to review existing policy frameworks and the implications of ongoing landuse trends of mountain regions, expressed, in particular, through exacerbated levels of land abandonment, on ecological performance, landscape development and the specific amenities of those areas.

#### **2. Materials and Methods**

The paper builds on a study that was commissioned by the European Parliament to provide a deeper and up-to-date understanding of land abandonment in the EU based on available data and information, including its development, drivers, mitigating measures across EU policies, not limited to the EU's Common Agricultural Policy (CAP) and respective scenarios [22]. The resulting report was provided to assist the members of the Committee on Agriculture and Rural Development (AGRI Committee) of the European Parliament in discussing the legislative proposals regarding CAP post 2020, including the Commission Communication and Action plan on "A long-term vision for Rural Areas", expected in 2021. Its scientific assignment was to complement existing evidence with specific research on land abandonment to differentiate trends of diverse land management systems and types of regions, and to draw conclusions relevant to policymaking regarding the post-2020 CAP policy [33]. This paper retrieves spatial information relevant for mountain area trends and highlights the particular affectedness of this type of region for land-abandonment risk.

With a view on assessing the pace and occurrence of land abandonment across mountain regions in Europe, the paper benefits from an intensive literature review on studies exploring both general trends and the differentiation of land-use change at a fine geographical scale within mountain areas. It is particularly necessary to include the fine-grained internal divergence of mountain contexts where abandonment and intensification of various uses might take place in closely adjacent areas. Moreover, trends are quite different from mountain range to mountain range, and thus any large-scale appraisal should point to these internal divergent and context-specific developments [34]. The analysis is based on quantitative data used by the Joint Research Centre (JRC) of the European Commission [12] as well as other data sources, such as Corine Land Cover data for land use change. These data are the basis for further quantitative and qualitative analyses. In particular, GIS analyses and quantitative analyses, such as Naïve Bayes classifier [35], were applied to provide different geographical resolutions of land abandonment and define groups of regions with specific regional characteristics. This also resulted in probability checks for increased risk level of mountainous areas due to these data sources. Qualitative data collection and analysis via desk research, four case studies in diverse European regional contexts, and expert interviews complemented the statistical database, particularly to take account of the multiple set of drivers impacting land use and change in management decisions. This also provided input to scenario building on future development perspectives with regard to land abandonment outcomes. The results from specific case study exploration and scenario building are presented here only in relation to mountain development aspects, as contextual aspects are highly uneven between specific geographical places.

Quantitative and qualitative assessments of land abandonment trends and influencing aspects were also analyzed against a review of CAP instruments and their implications, both favoring and disfavoring aspects of abandonment and future scope to address challenges of sustainable land-use pathways. This appraisal mainly reflects the large-scale European perspective and would need place-sensitive investigations for differentiating implications on individual regions. Methodological issues have strong implications for the resulting effects in CAP reform discourse and policy framework outcomes. Many analysts underpin the need for more place-specific commitment in policy reform, a stronger need for shifts in policy objectives toward integrating climate-change needs and ecological challenges [36] and addressing more actively agroecological approaches in the future policy [37].

#### **3. Elevated Risk of Land Abandonment in Mountain Regions**

The description of observed trends of land abandonment supports the assumptions for the stronger relevance of the issue within mountain areas. In the first part of this section, the respective quantitative assessment of risk calculation (at different levels) is presented in a comparative manner between mountain and non-mountain areas of the EU-28, and for different mountain ranges within the EU. This analysis is then supplemented by a discussion of the driving forces for this spatial feature of land-use change and an assessment of policy framework addressing this challenge.

#### *3.1. Risk Assessment in Mountain Regions of the EU*

The analysis focuses on the survey presentation of the status and trends of quantitative development of land abandonment for all countries of the EU and differentiation by regions and geographical types. Building on the long-term concern of land-use changes across European regions [38], the cumulative effects of concentration and abandonment trends are analyzed. With regard to mountain areas, a comparative view against the situation in non-mountain areas reveals significant differences in threats experienced in mountain contexts. Analyses at the NUTS-3 regional level (nomenclature of territorial units for statistics) show that the higher the share of mountainous areas, the higher the risk of land abandonment (and vice versa—the higher the share of non-mountainous areas, the smaller the risk of land abandonment as shown in Figure 1).

The relationship is presented against five classes of regions, ranging from very low characteristics of mountain areas (less than 20% of total area to be classified as mountainous) to very high area shares (of more than 80% of mountainous areas). These classes are matched with the probability of regions belonging to different risk levels of land abandonment. Similar to geographical classes, the risk level is also grouped into five classes (labeled as "very high, high, moderate, low, very low" probability of risk). From the model output (Table 1) the probability of regions belonging to either a high or even a very high risk of land abandonment attains more than 50% for regions with a very high share (more than 80%) of mountainous areas. The strong correspondence of a high share of mountain areas and a high share of risk is underpinned by the respective results for other classes. In mixed regional contexts, moderate risk levels prevail but these might also appear for

regions with low mountain profiles where aspects other than geographical specificity (e.g., structural features) might prevail as influencing aspects.

**Figure 1.** Average share of five risk levels for mountain and non-mountain areas in EU.

**Table 1.** Conditional probability <sup>1</sup> of mountainous areas in EU per risk level.

<sup>1</sup> Based on DEGURBA data from EUROSTAT (2019), calculation based on model output by Naïve Bayes classifier. Source: [22] (p. 33).

The model results for the various classes of mountain regions so far presented focus on the European assessment, on average. To understand and grasp the actual local situation, geographical differentiation and fine-grained spatial attribution are required. In the first instance, data for the main mountain ranges in Europe are reported in Table 2, highlighting the great variance of risk levels between these contexts. From this overview, the high incidence for risk in mountain ranges, in general, can be deducted (20.5% of high or very high risk in mountain areas vs. 7.7% in non-mountainous areas) and at the same time, the specific relevance for several mountain ranges particularly affected can be observed. In particular, the Eastern Mediterranean Islands and the Alps are affected by a specifically high degree of risk level. Such diverse contexts, such as the Balkan mountain regions and those of the British Isles, show substantial shares of high or very high risk levels. In interpreting these results for mountain ranges, we have to acknowledge that these comprise large areas and thus might include quite different internal divergences. To some extent, we might delineate the very wide-spread relevance of medium risk for almost all mountain ranges across Europe, thus indicating that geographical features of mountains might induce factors for increasing risk levels. In most mountain ranges, this class is relevant for more or less than half of the respective space (at EU-28, average 42.9%, and for non-mountain areas, just 21.8%).

**Table 2.** Risk for land abandonment in European mountain ranges (in %).

<sup>1</sup> Only high risk observed in this mountain range. Source: [22].

The results correspond to the findings of many case-specific studies on selected regions that observe the high incidence of land abandonment in mountain areas [7,9,27,39–43] and underscore the particular risk for future developments [9,44,45].

As risk of land abandonment is variable at low geographical detail, presentation in an overview map for the European situation requires applying a more large-scale unit level. The respective map (Figure 2) summarizes findings from a model calculation by the JRC study [12] and depicts the average risk level at the NUTS-3 average, ranging from very low (green color) to very high (dark red color). It reveals the particular relevance of land-abandonment risk for mountain regions of Europe in comparison to lowland areas, which are, to a large extent, characterized by very low or low risk levels. Exceptions from this general picture are visible for remote regions in Scandinavia, Baltic countries and Eastern border regions of EU New Member States. On the other hand, most large-scale mountain ranges dispose at least moderate, if not high or very high, risk levels. This very crude visualization of spatial distribution of the land abandonment challenge underpins the general appearance of land-use changes at a high level. It should be emphasized that place-specific, local influences might lead to locally very different effects and internal divergent trends within small distances. Community development analyses, such as those for a Tyrolean municipality [43], might be accessed as instructing examples for this phenomenon. These fine-grained territorial differences link to the need to understand the driving processes of land abandonment more comprehensively, an aspect which is explored in detail in the following section.

**Figure 2.** Risk of land abandonment at the NUTS 3 level and mountain areas.

## *3.2. Drivers and Policy Framework to Respond to Land Abandonment Challenges in Mountain Areas*

The significant changes for land use and risk for land abandonment addressed for mountain areas across Europe are the result of a highly diverse set of influential forces impacting land use in these areas and the relative developments of land management with regard to other, more favored agricultural areas. In assessing the main drivers for the regional development of land use, it seems crucial to frame the analysis in such a comparative view. Without taking into account developments in other regions, any analysis would fail to incorporate the full set of contributing forces for land abandonment.

These large-scale, systemic aspects are at the core of many previous studies, which underscored both the scope of different factors impacting the amount and specific areas used for agricultural purposes within a particular area, and the diverse orientation of effects. It seems important to underscore that for some bundles of indicators, reinforcing effects (for others, contradictory effects) and various types of mixed effects might occur. Perceiving pressures on land use and the resulting changes in landscapes in specific geographical areas such as mountain regions, particular concern for shifts in spatial features and functions has been raised over past decades [7]. Accompanying the policy commitment to address the foundations for land management decisions, a wide array of influencing aspects have been discerned. Even if the complexity of drivers is widely acknowledged the details of cause-effect relationships, triggering events and action, the scale of implications and

interrelations, and the relative dependencies between different spaces and land use and land management systems remain largely unclear at a general level. Beilin et al. [46] reveal in their analysis of agricultural land abandonment across diverse bio-physical regions (Sweden, Portugal and Australia) that historical trajectories of land cover change and the relative importance of each driver and its scale of action might have a different impact for future trends. "Pressures and attractors encouraging agricultural abandonment" would, thus, depend significantly on that study on place-specific features of the interplay of land management and social and institutional aspects.

Investigating the main drivers of land abandonment at the EU level relies, therefore, on a good understanding of the variability of influential factors, their relative weight, complementary or reverse effects and the complexity of cause-effect chains. This seems particularly pertinent in spaces that are experiencing challenges for competitiveness and productivity of land management systems, such as mountain regions. As the specific problem patterns of these areas are key for future land-use decisions, it is crucial to include an analysis of the interplay of non-land management based regional activities, economic uses and service provision with the organization and trends of land-use persistence. This view extends also to consider widespread environmental degradation trends, increasing land-use conflicts, and actors and triggers in complex socio-ecological systems [47]. Such a comprehensive perspective on ecological and regional contexts implies an in-depth account of rural areas' perspectives and the role of strategies to enhance the viability of rural regions. The preparation of the Communication on the Long-Term Vision of Rural Areas (LTVRA) by the European Commission [48], to be approved in mid-2021, sets out an appropriate action plan and demands commitment for inspiring local and regional activities with significant knock-on effects for land abandonment implications.

A number of studies assessing the features and causes of land abandonment [7,11,14–16,49,50] have contributed to elaborating a wide set of drivers. which are presented in groups in Figure 3. As the interrelations in the presentation underpin, there are complex interactions between these groups of drivers and between every single impacting factor as well.

**Figure 3.** Drivers of land abandonment and integration into sustainable development pathways. Source: Modified from [14] in [22].

The interactions addressed emphasize how land abandonment is dependent not just on biophysical aspects, management strategies and agricultural market conditions, but also on economic development, socio-cultural drivers, and regional and institutional aspects. Despite the wide array of factors influencing land abandonment, management issues and structural adaptation remain key driving forces. The complex nature of linkages between these primary, internal drivers and the aforementioned large-scale, mainly external triggers contributes to the difficulty of observing land-abandonment processes [15]. Moreover, assessment is exacerbated when geographical specificities add restricting requirements through limited access and institutional weaknesses. Sectoral approaches might limit a reflected position on the core role of governance frameworks to address the facilitating role of local and regional actors in coping with marginalization and abandonment issues [51,52].

Deficiencies of mountain areas to address and mitigate these effects in the shortterm underpin the long-term nature of land-use adaptation processes. In particular, this is related to the under-provision of highly demanded public goods that are linked to specific land management types, related to region-specific land use systems, such as lowintensity grassland, pastures and mountain meadows. 'Paradox' captures are increasingly experienced in these contexts [25]. The need for including local actors in "planning to envision mountain socio-ecological systems" [53] to realize place-sensitive management schemes is often hampered by failures in institutional settings and inadequate policy frameworks. In 'marginal' areas, land-use systems tend to be limited [54]; hence, the pressure on policy regulations to secure public goods provision is pervasive. As these values are not adequately recognized in current support mechanisms, there is a need for a 'step change' in CAP policy [55] to 'correct' policy failure.

This need to revise the agricultural policy framework touches a crucial issue of reorienting the CAP policy outline and the implementation to address, from top-down, the risk of falling short of providing an effective and sufficient scope for instruments addressing ecological needs and, particularly, mitigating the detrimental effects on land management, implicating higher levels of abandonment [56] (p. 20). It seems particularly crucial to address ecological needs and the diversity of place-sensitive agricultural systems through shaping land management adaptation in different spatial contexts [36]. In this vein, it is, moreover, argued that failure to "remunerate(e) farmers for all the services they provide could lead to land abandonment and closed landscapes" [56] (p. 26). These gaps in the policy setting are particularly distinct in relation to policies for ANCs, permanent grassland or support for organic farming systems, as well as with regard to the biodiversity strategy and nature protection approaches [56] (pp. 38 and 61).

In assessing the policy framework addressing the wide range of drivers, it appears decisive that general European policy instruments might provide an important, basic framework, which is, however, not sufficient for addressing the multiple threats to which mountain areas are exposed when aiming at mitigating land abandonment trends. Even if implementation details address local specificity and relate to particular development options or obstacles, the orientation and effectiveness of policy instruments should be differentiated by their scale of impact. Figure 4 matches the scale of policy application with the diverse intervention programs. It indicates the crucial farm level (farm unit and even parcel unit) which many instruments of CAP Pillar 1 address by shaping fundamental aspects for market integration and individual management decisions [18]. Space specificity is core here, and has long been a building block of CAP. In particular, the less favored area (LFA) scheme, which aimed at (partly) 'compensating' relatively low competitiveness chances [57] and simultaneously addressing demographic and ecological problems and opportunities of rural areas [6], is the long-term leading instrument in this respect. The evolving policy discourse revised its orientation and refocused its remit much stronger toward its core function to cope with its characteristics of ANCs as it has been termed for several years [58]. However, these instruments, targeted at land managers themselves, need to be complemented by interlinked action at local and regional levels, as well. As the discussion on the interrelated system of drivers suggests, rural economic development, environmental performance, human-nature relationships, skills development, educational attainment, enterprise involvement, market chain development and many more aspects, impact on the outcome in land use change and require adapted policy considerations. Beyond that, national and global influences are experienced as a decisive backdrop to many local and regional actions. This underscores the view that land abandonment is embedded in these larger frameworks, which are hardly affected by local management decisions and related policies.

**Figure 4.** Relevant policies for land abandonment and scales of intervention. Source: [22] (p. 48).

The conceptual framework analyzed for mountain regions is, thus, largely dominated by strong external pressures [25] but also a need for an approach going beyond a simplified, linear, cause-effect relationship as often expressed in monitoring and evaluation practices and concepts of theory of change. As they convey just one plausible development pathway and unique outcomes for specific policy instruments, such views neglect alternative opportunities. We have to conceive the policy arena much more as complex settings of contradictory policy objectives with manifold, unexpected outcomes. Complex evaluation thinking is increasingly addressing these limitations of previous assessment practice, advocating a systemic evaluation design that supports sustainable development pathways [59]. In essence, these approaches are particularly centered on the aspect of how to realign "human and natural system interests (that) too often stand opposed to each other (or outright ignore each other)" [60] (p. 46). Emerging evaluation concepts encompass such an interrelated view of human action effects on natural systems, emphasizing an increasingly intensive array of "contextual conditions and drivers", "baseline assumptions" and scope for intervention design within the socio-political "landscape" [61].

### **4. Discussion**

As Lambin et al. [62] explored long ago (2001), viewpoints on causes for explaining land use and land-cover change are often superseded by generalized myths and a simplified perception of cause-effect relationships, as well as potential policy interventions. This also applies to land abandonment analysis in mountain regions. In our analysis of the current challenge of land abandonment in European mountain regions, we aimed to capture the trend that "various human-environment conditions react to and reshape the impacts of drivers differently, leading to specific pathways of land-use change" [62] (p. 266). As abandonment is a highly complex and gradual process, the observation and differentiation for types of areas at the European level are methodologically difficult. Capturing the various steps of initial reduction in agricultural and forestry intensity, and gradual transition toward temporary, transitional and permanent abandonment requires fine, geographical detail, and the process steps usually can only be delineated along specific cases. Past and recent studies have underscored the great variety of processes and diverse sets of influencing drivers, and sector and institutional responses.

As the background study [22] underscores, the pertinence of the problem to secure land management in mountain areas is at the origin of the evolution of important policy instruments [63] and is impacting current discourses of policy reform [33,36,64]. The limited effectiveness of related policies points to inherent obstacles and an assessment bias toward the prioritization of farm competitiveness versus other, interrelated policy goals. While respective studies are largely aware of this trade-off, the adaptation of policy and national implementation are adopting such perspectives only by and by. This expert-policypractice tension with regard to face land abandonment pressures in mountain regions as a persistent and trans-boundary problem involves a series of inter-related aspects:

Large-scale influences are increasingly experienced as persisting pressures for mountain regions [25]. The observed land-use changes and impending risk for land abandonment in this study [22] underpin a long-term concern for viable practices of land management in mountain areas. The spatial dimension of agricultural transformation [79] is particularly driven by spatial concentration processes, due to the intensification of a part of the sector [4,62] leading to regional concentration effects [3] with tremendous consequences for mountain areas. Even within these areas, internal shifts in land use and intensification and

abandonment processes have to be differentiated [27]. The CAP is shown to pay substantial attention to these challenges in elaborating the dedicated policy instrument of LFA/ANC, which, however, falls short of "compensating" or overcoming the pressures, particularly in remote mountain areas. Much of the remaining policy concern is due to the implicit tradeoffs in the overall policy framework and the limitations of policy action to tackle inherent market mechanisms and socio-ecological frameworks [80]. Therefore, "(p)olicy responses to land abandonment in Europe must move beyond agriculture-oriented schemes under the purview of the Common Agricultural Policy to incorporate a range of independent, holistic rural development programs" [65] (p. 1). Such a perspective advocated by various researchers should be adopted by policymakers to overcome overly narrow agriculturaloriented rural policy, including particularly contextual conditions of mountains, long-term trends of 'rural shrinkage' [32] and human-nature relationships at the core of land-use decisions and outcomes. The current pandemic intensifies these pressures and aggravates the need for diverse, context-specific local support in mountain areas. Ongoing discussions of CAP reform might improve the respective responses, but reluctance to open up to such an encompassing policy framework and regional development practice seems widespread. On the other hand, prospects to turn toward a pathway-enhancing action, inspired by the 'Long-Term Vision for Rural Areas', which is now being initiated, still seems limited.

#### **5. Conclusions**

Land-use development is not just an issue of adjusting management to local and regional contexts and sector performance, but is dependent on the interplay of all sectors and drivers of respective socio-ecological systems. In providing a comprehensive survey on the experience and future risk for land abandonment, analyzed at various scales and for geographically specific areas, the high incidence for uneven spatial effects becomes clear. Mountains have long been under pressure with regard to maintaining agricultural land management that has increasingly concentrated on extensive grassland systems. This has led to place-specific habitats that dispose of, to a large extent, high nature value farmlands. These consequences present the particular threat arising from the further decrease in managed land within these geographical contexts.

Land abandonment is, however, characterized by a much more complex system of cause–effect relationships and effects. The analysis of a host of diverse studies in the past and our synthesizing study for the European Parliament provide powerful evidence of a combination of many factors for which sometimes, one is predominant over the others. However, in general, mutually reinforcing effects lead to 'spiraling down' processes for land management. These cause-effect chains and threatening outline are particularly valid in mountain regions with high internal differences and specific regional 'hotspots' of abandonment. From current observations and data analyses, there is no ease in this problematic situation in sight for the coming decade.

The main driver for the land-use management and land change prospects are contingent on ongoing intensification processes, due to technological changes and uneven agricultural competitiveness conditions. As long as the approval for a narrative to foster efficiency, technological progress and growth in productivity, i.e., intensity, is the leading paradigm, any strategy to cope with land abandonment and marginalization processes will face almost insurmountable obstacles, as it always would act in a framework of 'compensation'-relative deficiencies in competitiveness.

The deep concern for dealing with the 'failures' or 'restricted impact' of past CAP has spurred concern to reflect the challenge of land abandonment and to revise the agricultural policy framework. It touches on a crucial issue of reorienting CAP policy outline and implementation to address, from top-down, the risk of falling short of providing effective and sufficient scope for instruments addressing ecological needs and mitigating the detrimental effects of land abandonment, particularly in mountain regions. It seems, therefore, necessary to conceive appropriate policy measures beyond CAP, including a comprehensive array of policies with interrelated effects on land management aspects. Such a wide scope of policy instruments would include regional and social policies (structural funds), environmental regulation, infrastructural organization and public services for remote regions, and should be addressed also by the recent EC policy reorientation in Green Recovery.

**Author Contributions:** Conceptualization, T.D. and B.S.; methodology, T.D., I.M., K.S., B.S. and M.D.- N.; validation, T.D. and B.S.; literature review T.D., I.M., M.D.-N. and B.S.; formal analysis, M.D.-N., K.S. and M.G.-B.; data curation, M.D.-N., M.G.-B., K.S.; writing—original draft preparation, T.D., B.S., M.G.-B. and I.M.; writing—review and editing, T.D. and B.S.; visualization, K.S., M.G.-B.; synthesis discussion, T.D., I.M., K.S., B.S., M.D.-N., M.G.-B.; supervision, T.D. and B.S.; project administration, M.D.-N. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received external funding by the Directorate for Structural and Cohesion Policies of the European Parliament, contract IP/B/AGRI/IC/2020-037.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** In this section, you can acknowledge any support given which is not covered by the author contribution or funding sections. This may include administrative and technical support, or donations in kind (e.g., materials used for experiments).

**Conflicts of Interest:** The authors declare no conflict of interest.

## **References**

## *Article* **Forests, Farms, and Fallows: The Dynamics of Tree Cover Transition in the Southern Part of the Uluguru Mountains, Tanzania**

**Mathew Mpanda 1,\*, Almas Kashindye 2, Ermias Aynekulu 3, Elvis Jonas 4, Todd S. Rosenstock <sup>3</sup> and Richard A. Giliba <sup>5</sup>**

	- <sup>3</sup> World Agroforestry (ICRAF), P.O. Box 30677, Nairobi 00100, Kenya<EMAIL_ADDRESS>(E.A<EMAIL_ADDRESS>(T.S.R.)
	- <sup>5</sup> School of Life Sciences and Bio-Engineering, The Nelson Mandela African Institution of Science and Technology, Arusha P.O. Box 447, Tanzania<EMAIL_ADDRESS>	- **\*** Correspondence<EMAIL_ADDRESS>or<EMAIL_ADDRESS>
**Abstract:** Forests and woodlands remain under threat in tropical Africa due to excessive exploitation and inadequate management interventions, and the isolated success stories of tree retention and tree cover transition on African agricultural land are less well documented. In this study, we characterize the status of tree cover in a landscape that contains forest patches, fallows, and farms in the southern part of Uluguru Mountains. We aimed to unveil the practices of traditional tree fallow system which is socially acceptable in local settings and how it provides a buffering effects to minimize forest disturbances and thus represents an important step towards tree cover transition. We assessed land cover dynamics for the period of 1995 to 2020 and compared tree stocking for forest patches, fallows, and farms. We found that tree biomass carbon stocks were 56 ± 5 t/ha in forest patches, 33 ± 7 t/ha in fallows, and 9 ± 2 t/ha on farms. In terms of land cover, farms shrank at intensifying rates over time for the entire assessment period of 1995–2020. Forest cover decreased from 1995–2014, with the reduction rate slowing from 2007–2014 and the trend reversing from 2014–2020, such that forest cover showed a net increase across the entire study period. Fallow consistently and progressively increased from 1995–2020. We conclude that traditional tree fallows in the study site remain a significant element of land management practice among communities, and there appears to be a trend towards intensified tree-based farming. The gains in fallowed land represent an embracing of a traditional land management system that supports rotational and alternate uses of cropping space as well as providing a buffering effect to limit over-exploitation of forests. In order to maximize tree cover and carbon stocks in the farm landscape, this well-known traditional tree fallow system can be further optimized through the incorporation of additional innovations.

**Keywords:** deforestation; shifting cultivation; traditional fallow; swiddens

## **1. Introduction**

Tropical forests account for 45% of global forest cover, and their deforestation significantly contributes to CO2 emissions from biogenic sources [1,2]. Broad scientific recognition of the considerable leverage of forest management in the global climate system has catalyzed efforts to better understand trends in deforestation and forest degradation [3,4]. In the tropics, swidden agriculture is strongly associated with deforestation [5]. Farmers practice swiddens by clearing forest vegetation, burning it, and then planting crops. Swiddens are believed to reduce carbon in the landscape via the destruction of relatively large aboveground carbon stocks, including forest resources [6,7]. For example, swidden is estimated to have removed one-third to one-half the aboveground carbon stocks in primary forest

**Citation:** Mpanda, M.; Kashindye, A.; Aynekulu, E.; Jonas, E.; Rosenstock, T.S.; Giliba, R.A. Forests, Farms, and Fallows: The Dynamics of Tree Cover Transition in the Southern Part of the Uluguru Mountains, Tanzania. *Land* **2021**, *10*, 571. https://doi.org/10.3390/land10060571

Academic Editor: Bruno Martino

Received: 12 March 2021 Accepted: 14 May 2021 Published: 28 May 2021

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**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

across Brazil, Cameroon, and Indonesia [8]. Nevertheless, the exact impact of swiddens and land use change on carbon dynamics differs with vegetation types and management models [9], and the varying types of swidden techniques used to fell forests, ranging from selective to total clearance, add further complexity. Varying swidden techniques create uncertainties and variability in the rates of deforestation and regrowth, as well as in the carbon density of various land uses throughout the landscapes [10,11].