Agricultural Land Use Dynamics Research Articles

Seasonal dynamics of agricultural land use impacts on earthworm communities: Insights into diversity, abundance, and functional composition

Earthworms play a key role as soil bioengineers, but livestock farming and croplands can impact on the composition, structure, and functioning of earthworm communities. This study aimed to quantify the effects of replacing natural grasslands with crop rotations on the seasonal dynamics of key attributes of earthworm communities and explore soil physicochemical properties as mechanisms behind these effects. We conducted paired samplings on seven sites across Uruguay, comparing earthworm communities in soils under grazed natural grasslands and adjacent croplands, considering earthworm species composition, richness, diversity, and evenness, biomass, density, and mean body weight, over five consecutive seasons. Results revealed a clear negative impact of croplands on earthworm communities, showing substantial reductions in all considered attributes. Observed changes were attributed to environmental filters limiting the occurrence of larger exotic earthworm species in croplands, and variations in soil physicochemical properties were identified as potentially mediating some of these effects, including soil water regime (particularly affecting juvenile individuals) and soil organic matter content. These impacts resulted in a shift to dominance of r strategists (smaller, surface-feeding species such as Microscolex spp. in croplands compared to larger deeper-burrowing species in grasslands) and reduced functional composition of earthworm communities. Furthermore, seasonal dynamics revealed that differences between land uses were more pronounced during wetter periods, underscoring the relevance of seasonal variations when evaluating land use impacts on earthworm communities. Earthworms mean body weight resulted a useful attribute to be included in earthworm's assessments, allowing to identify impacts in community functional composition and suggesting size dependent mechanisms. Additional research is required to comprehensively understand the mechanisms behind these patterns and to develop more sustainable agricultural practices by considering soil fauna.

Картографирование динамики сельскохозяйственного землепользования в сухостепной зоне Северного Казахстана

The agricultural use of land resources in the dry steppe regions of Northern Kazakhstan has undergone significant changes over the past century and continues to change at the present time. The middle position in the region is occupied by the Akmola region, one of the leading agricultural regions, where 9.3 % of the country’s gross agricultural output is produced. The main branches of agriculture in the region are grain farming and dairy and beef cattle breeding. The study of the change in the ratio of the areas of arable and pasture lands, the changes that have occurred in the state of agricultural landscapes as a result of long-term use is important for the further development of the agricultural industry in the region. Using the example of the Astrakhan district of the Akmola region, typical for the dry steppe zone, an analysis of changes in the structure of agricultural land from 1953 to 2020 was carried out. with the help of a series of digital maps compiled from detailed maps of scientific reference atlases of Northern Kazakhstan in 1964, 1970, remote sensing data of different times, archival materials and data of modern statistics. The resulting maps visualize significant changes in cropland areas within the study area over the past 70 years. Seven main periods were identified, during which the most significant changes in the structure of land use occurred as a result of political, economic and natural factors. During the years of development of virgin and fallow lands, the highest rates of increase in arable land were observed, which continued on a smaller scale until 1990. The period from 1991-1999 characterized by a large-scale reduction of arable land and abandonment of pastures. This was followed by two periods of gradual restoration of the use of arable and rangelands. Compiled comprehensive map of the dynamics of agricultural land use from 1988 to 2020 made it possible to identify spatial and temporal patterns in changes in the structure of agricultural land use, to determine to which specific natural types of lands the abandoned lands belonged and to which restored ones. Spatial analysis showed that over the past 30, more than half of the area’s area has not changed the type of land use. The results obtained are used to select the optimal ratio of arable and pasture lands in the structure of agricultural land use, as well as to develop a strategy for the rational use of agricultural land in the zone of risky farming.

Ecosystem services as the products of land system dynamics: lessons from a longitudinal study of coupled human–environment systems

We address understanding of whole-system and landscape-based approaches to the ecosystem services framework by considering the supply of provisioning services and the dynamics of agricultural land use in Scotland between 1940 and 2016. To characterise and understand the dynamics of change in provisioning services from agriculture in Scotland over the period 1940–2016. To identify ways in which funds of capitals and flows of inputs and output ecosystem goods are linked to land management practices and policies at a national scale. Data describing agricultural land use, production, financial and energy inputs and outputs, and drivers of change in land use in Scotland are analysed with an accounting framework that links funds of natural, human, physical and financial capital, with flows of goods and services. Flow–fund ratios are used as benchmarks of system performance and dynamics. Scotland’s agriculture has modernised since 1940 and become more efficient in conversion of resources, with a consequent increase in delivery of provisioning goods and services. Although the energy ratio, and flow of goods per unit hectare and per unit labour have increased, the inputs necessary to maintain those flows of ecosystem goods are also increasing, even as their relative economic costs decrease. Increases in use of fertiliser suggests that production from the soil, as a natural capital fund, is not being conserved without a large, and increasing, input. Analysis of the complexity of the coupled agricultural land system also suggests that land management rather than biodiversity is a necessary subject for evaluation of provisioning services from agriculture. Understanding of ecosystem services based on accounts that integrate inputs, outputs and flows from funds of natural, human, social, financial and physical capitals, provides a process-based foundation for improved understanding of ecosystem services and human–environmental relationships. Adopting an accounting approach for understanding the role of agricultural land use for supply of provisioning services, and particularly examining a long time-series of accounts, enables understanding of land changes and underlying drivers, as well as the contribution of cultural and other aspects of human systems coupled with environment systems. Accounting for ecosystem services using costs as well as benefits, and use of metrics beyond financial benefit, supports debate and evaluation of trade-offs between services and has direct relevance for decision- and policy-making.

Agricultural land-use dynamics: Assessing the relative importance of socioeconomic and biophysical drivers for more targeted policy

A detailed understanding of multiple human and environmental factors influencing land allocations among agricultural uses can facilitate more efficient and targeted land policy. To show this, we used a comprehensive dataset of socioeconomic, physiographic, and climatic indicators to investigate potential determinants of land-use in Australia’s intensive agricultural region during the period 1992–2010. We applied a seemingly unrelated regressions land-use shares spatial error model with random effects coupled with variance decomposition analysis to identify the statistical significance, direction and magnitude of observed associations between land-use and its drivers.Population: density, rainfall, equity ratio, and access to markets were the most influential policy-relevant land-use factors. Land allocations to cereals and livestock production were significantly influenced by spatiotemporal rainfall and temperature variability. Improved pastures, cereals, annual and perennial crops plantations were larger in regions with better access to markets. Increases in equity ratio (i.e., better financial position) were associated with larger land allocations to improved pastures and annual crops and smaller extensive grazing area. Marginal associations were detected between land-use and output prices, and higher population density was associated with lower shares for all high value agricultural land-uses. The results suggest that improved transportation infrastructure, zoning regulations, and mechanisms to reduce farm debt exposure and risks from climate variability could have significant impact on the configuration of the Australian agricultural landscape.

A Dynamic Regional Model of Irrigated Perennial Crop Production

Economic analyses of regional irrigated agricultural production typically make little distinction between perennial and annual crops despite the distinctive characteristics of perennials. Such factors include high planting costs, lags in production, long lifespan, and potentially long-lasting impacts of input use and weather shocks. This study establishes a fully dynamic model of irrigated perennial crop production in a regional context where annuals are also grown. Perennial crop area is modeled as a vintage capital stock with age-dependent yields. The model is applied to the Riverland region of South Australia to examine the possible effects of both temporary and permanent changes in water supplies and the establishment of water prices, which is then used to estimate agricultural water demand. The model demonstrates that annuals are fallowed during drought so that perennial crops may be preserved and how, due to the fixed costs of re-planting perennials, annual crops are used to smooth profits while recovering from a severe shock. In all scenarios, a very slow rate of convergence to the steady state is found, highlighting the need for models that capture the transitional dynamics of agricultural land use in areas with significant perennial production.

Multitemporale und kantenbasierte Analyseverfahren zur Detektion agrarischer Landnutzungsdynamiken auf Teneriffa

Multitemporale und kantenbasierte Analyseverfahren zur Detektion agrarischer Landnutzungsdynamiken auf Teneriffa

Mining dense Landsat time series for separating cropland and pasture in a heterogeneous Brazilian savanna landscape

Better spatial information on the global distribution of croplands and pastures is urgently needed. Without reliable cropland–pasture separation it will be impossible to retrieve high-quality information on agricultural expansion or land use intensification, and on related ecosystem service provision. In this context, the savanna biome is critically important, but information on land use and land cover (LULC) is notoriously inaccurate in these areas. This is due to pronounced spatial–temporal dynamics of agricultural land use and spectral similarities between cropland, pasture, and natural savanna vegetation. In this study, we investigated the potential to reliably separate cropland, pasture, natural savanna vegetation, and other relevant land cover classes employing Landsat-derived spectral–temporal variability metrics for a savanna landscape in the Brazilian Cerrado. In order to better understand the surplus value and limitations of spectral–temporal variability metrics for classification purposes, we analyzed four datasets of different temporal depth, using 344 Landsat scenes across four footprints between 2009 and 2012. Our results showed a reliable separation between cropland, pasture, and natural savanna vegetation achieving an adjusted overall accuracy of 93%. A similar accuracy and spatial consistency of LULC classification could not be achieved based on spectral information alone, indicating the high additional value of temporal information for identifying LULC classes in the complex land use systems of savanna landscapes. There is great potential for transferring our approach to other savanna systems which still suffer from inaccurate LULC information.

Simulating land-use degradation in West Africa with the ALADYN model

West Africa faces a rapid growth in population and a subsequent demand for food production. Despite increasing demand, local farmers still follow traditional practices and try to overcome low productivity by continuously expanding cultivated areas. To assess the consequences of this expansion and to describe, based on anticipated population growth rates and land accessibility, the dynamics of agricultural land-use, we developed a spatially explicit agent-based ALADYN model of agricultural land use in the savannah around Kita, Mali. The model is based on remote sensing data on agricultural land use and data from field surveys. The ALADYN simulations clearly demonstrate that traditional agriculture is not sustainable. Even under the optimistic scenario of a declining rate of population growth, the current agricultural practices will result in the cultivation of all available agricultural lands by 2015. Under current practices, every farm will experience a period of 1–3 years every 15–20 years, during which field fertility will be too low to allow cultivation. Thus, to avoid severe food shortage, emigration or alternative sources of food are necessary. Comparison of the model and remote sensing data reveals that already in 2003 the cultivated part of the study area is lower than projected. That is, farmers anticipate overexploitation and, most probably, emigrate from the area. The model highlights the great need for new agricultural practices in West Africa.

The Dynamics of Agricultural Land use Using Multi-Spectral Imageries in Southern Part of Nigeria

The Dynamics of Agricultural Land use Using Multi-Spectral Imageries in Southern Part of Nigeria land use change and modeled the factors for detecting change in agricultural land use in the Southern part of Nigeria. This was with a view to assessing the dynamics of agricultural land use using multispectral imageries in the study area. The study integrated fieldwork and satellites imageries from Land sat Thematic Mapper(TM)1987.

Coupling participatory mapping and GIS to inform village-level agricultural zoning in the Democratic Republic of the Congo

Participatory methods that consider the land use of local communities are critical to resource planning. In the Maringa-Lopori-Wamba (MLW) Landscape located in northern Democratic Republic of the Congo (DRC), the African Wildlife Foundation (AWF) is leading efforts to engage the DRC Government in developing strategies for sustainable forest management and micro-zoning. In developing these strategies, we used participatory mapping to determine collaboratively with the local communities the delimitation of village-level agricultural zones for 16 villages located in easterncentral MLW Landscape. For this, we developed methods that combined participatory mapping, satellite image interpretation and GPS data collection. To inform the dynamics of agricultural land use, we collected human population data at the village level and calculated the maximum distance from each agricultural area to the nearest road. The surface area of the delineated village-level agricultural boundaries ranged from 241ha to 2616ha. We found a significant and positive relationship between the villages’ human population and the surface area of their agricultural limits (r2=0.43, p=0.005). There was also a significant and positive relationship between the villages’ population and the maximum distance that their agricultural limits extended from the road. We highlight observations from our participatory mapping experiences and explain the applicability of our findings to participative micro-zoning and understanding agricultural land use in the DRC.

The use of gravity concepts for agricultural land-use dynamics: a case study on the Algarve

Recent research emphasises the role of small towns as main players in the spatial dynamics of rural areas. However, it is still largely unknown how much such urban areas generate planned development or growth in nearby surroundings, and how such small urban agglomerations attract and diffuse multiple activities in rural areas. This paper discusses the issue of socio-economic feasibility of rural areas from a long-term strategic perspective and proposes a new spatial land-cover accounting methodology to assess the impacts and changes occurring in the rural world, with a particular view to the pulling forces in small towns. Methodologically, this paper applies gravity concepts in combination with GIS to identify the compactness of agricultural areas within different regions of the Algarve and to detect the direction of significance of land-use variations. By means of time series and comparative analyses of different agricultural land, important land use changes can be identified.

A spatial high-resolution model of the dynamics of agricultural land use

An area model is presented for agricultural land use, based on a generalized linear mixed model. This model is spatially explicit and dynamic and, although it uses aggregated data, allows for heterogeneity of behavior among individual farmers. The parameters of the fixed component of the model are obtained using an estimation equations approach, and the structure of spatiotemporal correlation is assessed using empirical semivariograms. The model is illustrated using as an example the dynamics of agricultural land use in the Lower Guadalquivir area in Spain. A simulation study indicates that the model gives poor results if the heterogeneity of individual behavior and spatiotemporal autocorrelation are ignored.

A spatial high‐resolution model of the dynamics of agricultural land use

AbstractAn area model is presented for agricultural land use, based on a generalized linear mixed model. This model is spatially explicit and dynamic and, although it uses aggregated data, allows for heterogeneity of behavior among individual farmers. The parameters of the fixed component of the model are obtained using an estimation equations approach, and the structure of spatiotemporal correlation is assessed using empirical semivariograms. The model is illustrated using as an example the dynamics of agricultural land use in the Lower Guadalquivir area in Spain. A simulation study indicates that the model gives poor results if the heterogeneity of individual behavior and spatiotemporal autocorrelation are ignored.

Nutrient management in tropical agroecosystems

West Africa faces a rapid growth in population and a subsequent demand for food production. Despite increasing demand, local farmers still follow traditional practices and try to overcome low productivity by continuously expanding cultivated areas. To assess the consequences of this expansion and to describe, based on anticipated population growth rates and land accessibility, the dynamics of agricultural land-use, we developed a spatially explicit agent-based ALADYN model of agricultural land use in the savannah around Kita, Mali. The model is based on remote sensing data on agricultural land use and data from field surveys. The ALADYN simulations clearly demonstrate that traditional agriculture is not sustainable. Even under the optimistic scenario of a declining rate of population growth, the current agricultural practices will result in the cultivation of all available agricultural lands by 2015. Under current practices, every farm will experience a period of 1–3 years every 15–20 years, during which field fertility will be too low to allow cultivation. Thus, to avoid severe food shortage, emigration or alternative sources of food are necessary. Comparison of the model and remote sensing data reveals that already in 2003 the cultivated part of the study area is lower than projected. That is, farmers anticipate overexploitation and, most probably, emigrate from the area. The model highlights the great need for new agricultural practices in West Africa.