Large-scale Agricultural Land Research Articles

Past and future land use change dynamics: assessing the impact of urban development on agricultural land in the Pantura Jabar region, Indonesia.

The conversion of large-scale agricultural land into urban areas poses a significant challenge to achieving national and global food security targets, as outlined in Sustainable Development Goal number 2, which aims to eradicate hunger. Indonesia has experienced a significant decline in rice field areas, with a reduction of approximately 650 thousand hectares within a year (2017-2018), the largest being in Java. Hence, this study aims to examine the impact of urban expansion on agricultural land in the north coast region of West Java Province from 2013 to 2020 and develop a predictive model for 2030 to support sustainable land use planning. The primary methods employed were random forest (RF) analysis using Google Earth Engine, intensity analysis, multilayer perceptron-neural network (MLP-NN), Markov chains-cellular automata (Markov-CA), and stakeholder interviews. The model also evaluated the influence of "distance to tollgates" as a previously unexplored driving factor in existing land use modeling studies. Landsat image classification results using the RF algorithm showed 87-88% accuracy. Cropland has historically been and is projected to remain the primary target for the expansion of built-up areas. Spatial planning irregularities were found in the growth of these areas that adversely affected farmers' socioeconomic and environmental conditions. Evaluation of land use models using MLP-NN and Markov-CA demonstrated an accuracy rate of 86.29-86.23%. The distance to tollgates factor significantly impacts the models, albeit less than population density. The 2030 intervention scenario, which implements a firm policy for sustainable agricultural land use, offers the potential to maintain the predicted cropland loss compared to business as usual.

Effects of Large-Scale Agricultural Land Investment on Local Communities Livelihoods: Evidence from Bambasi Woreda, Western Ethiopia

Most of the African countries including Ethiopia are often known by problem of large scale agricultural land investment effects on the local community where land is being transferred to investment. Even though several efforts made so far to solve the overall effects of large scale agricultural land investment situation, the challenge is still widespread problem in Ethiopia. Hence the study project's purpose was to find out how large agricultural investments in Bambasi Woreda, Western Ethiopia impact the livelihoods of the surrounding populations and examine the local communities' participation in the large scale agricultural investment. In order to attain these objectives, data were collected from 330 randomly selected households in four purposively selected kebeles of the district for both control groups and treatment groups. The sample size was chosen using a multistage stratified random sampling technique. Both qualitative and quantitative data gathering techniques and instruments were employed in the study. Besides, the instruments utilized to collect the data were observations, focus groups, interviews, household surveys, and document reviews. Data was analyzed using both descriptive statistics and econometric methods. The study shows that out of the total sampled respondents 30 of the treatment group and 13 percent of control group reveal that the project provided opportunity in terms of employment opportunity, technology transfer, utilization of agricultural inputs, changing the working culture of the community and productivity. The chie square value shows there is statistical significance among treated and control group on opportunity investment provided for the household and community. A binary logit regression model was used to describe how large-scale agriculture land investment affected the local community's standard of living. The findings indicated that only six variables were found to be significant out of the characteristics that were expected to influence local community employment in large-scale agricultural land investment projects. These includes household's educational accomplishment, size of HH, occupation of HH, Loss of useful land due to investment Project and technology transfer significantly and positively affected the employment opportunity in large scale agricultural investment projects, whereas the distance of a household's home from an investment project has a negative impact. Large-scale agricultural investments have a detrimental influence on household wealth accumulation and income, according to the estimation results of the average treatment effects on the treated. The management and implementation of land transfer for large-scale agricultural investment projects is inadequate, lack of openness, absence of community consultation, natural forest degradation, socio-economic and ecological effects must be carefully considered before transferring the land for large-scale agricultural investment.

Sustainable grain production growth of farmland–A role of agricultural socialized services

The main aim of this study is to examine the evolving landscape of agricultural socialized services and their impact on the consistent growth of grain production in China. Using panel data from 2007 to 2020, we employ the Entropy Method to gauge the dynamic changes in agricultural socialized services that have contributed to the steady increase in grain production. The research methods include static panel, mediator, and threshold regression models to investigate the effects and mechanisms underpinning the improvement of agricultural socialized services on grain production growth. The empirical findings demonstrate a significantly positive correlation between enhanced agricultural socialized services, such as means of production services, sci-tech information services, and social public services, and increased grain production. This positive impact persists even with limited grain production resources. A mediating effect was identified, whereby agricultural socialized services indirectly stimulate grain production growth by encouraging large-scale agricultural land management. Furthermore, threshold analysis indicates the presence of a single threshold effect linked to the level of agricultural socialization services. This threshold effect plays a pivotal role in the relationship between large-scale agricultural management and steady grain production growth. The study suggests an enhancement of agricultural socialized services can promote sustained growth in grain production.

Spatial Pattern of Large-Scale Agricultural Land and Spatial Heterogeneity of Influencing Factors in the Mountainous Areas of Western China—Wuling Mountains as an Example

The scaling of agricultural land is a trend in land use transformation and is important for modernizing agriculture. Therefore, the reasons for large-scale agricultural land formation should be explored. The spatial distribution of large-scale agricultural land and the factors for its formation vary between different regions. Currently, the exploration of the formation mechanism of large-scale agricultural land from the perspective of heterogeneity is not yet sufficient. Therefore, the main objectives of this article are as follows: first, analyze the spatial pattern characteristics of large-scale agricultural land; second, explore the spatial heterogeneity characteristics of influencing factors from both global and local perspectives; third, explore the mechanism of the formation of large-scale agricultural land from the perspective of heterogeneity. The results indicate the following: (1) The large-scale agricultural land distribution pattern in the Wuling Mountains area was high in the east and low in the west. (2) Natural conditions, production factors, and location conditions all significantly impacted large-scale agricultural land, but with differences in their degree of influence. From a local perspective, the influences of various factors in different regions also exhibited spatial heterogeneity. These two types of heterogeneity can be attributed to the differences in regional development stages. (3) Natural conditions, location conditions, and production factors had negative, positive, and positive effects on the agricultural land scale, respectively, but the influence of the first two decreased with the improvement in the regional development stages. The influence of different factors on production factors was related to the regional development stage, and production factors that were suitable for the regional development stage had a greater impact. The conclusion can provide differentiated policy support for regional land use in practice.

Comparative analysis of soil organic matter fractions, lability, stability ratios, and carbon management index in various land use types within bharatpur catchment, Chitwan District, Nepal

BackgroundLand use and land cover changes have a significant impact on the dynamics of soil organic matter (SOM) and its fractions, as well as on overall soil health. This study conducted in Bharatpur Catchment, Chitwan District, Nepal, aimed to assess and quantify variations in total soil organic matter (TSOMC), labile organic matter fraction (CL), stable organic matter fraction (CS), stability ratio (SR), and carbon management index (CMI) across seven land use types: pastureland, forestland, fruit orchards, small-scale conventional agricultural land, large-scale conventional agricultural land, large-scale alternative fallow and conventional agricultural land, and organic farming agricultural land. The study also explored the potential use of the Carbon Management Index (CMI) and stability ratio (SR) as indicators of soil degradation or improvement in response to land use changes.ResultsThe findings revealed significant differences in mean values of TSOMC, CL, and CS among the different land use types. Forestland and organic farming exhibited significantly higher TSOMC (3.24%, 3.12%) compared to fruit orchard lands (2.62%), small scale conventional farming (2.22%), alternative fallow and conventional farming (2.06%), large scale conventional farming (1.84%) and pastureland (1.20%). Organic farming and Forestland also had significantly higher CL (1.85%, 1.84%) and CS (1.27%, 1.39%) compared to all other land use types. Forest and organic farming lands showed higher CMI values, while pastures and forests exhibited higher SR values compared to the rest of the land use types.ConclusionsThis study highlights the influence of various land use types on soil organic matter pools and demonstrates the potential of CMI and SR as indicators for assessing soil degradation or improvement in response to land use and land cover changes.

Integration of multimodal data for large-scale rapid agricultural land evaluation using machine learning and deep learning approaches

Rapid and accurate agricultural land evaluation provides essential guidance for the supervision and allocation of agricultural land resources; it also helps to ensure food security. Previous work has mainly evaluated the land quality at the county level by using field sampling data and based on a factor approach. However, it is difficult to achieve uniform, large-scale agricultural land evaluation via conventional approaches because of its spatial heterogeneity, as well as the large temporal and economic costs associated with data acquisition. In this study, we integrated publicly available multimodal data (i.e., satellite remote sensing, environmental, and socioeconomic data) into the Google Earth Engine (GEE) platform, selected the best indicators from each modality using the geodetector, on the basis of which different combinations of input models were designed. And then we developed machine learning (random forest, RF) and deep learning (deep neural network, DNN) models to evaluate the land quality in paddy field and dry land systems in 2013 throughout Guangdong Province, China. The results showed that the performance of our combination of variables decreased in the following order: multimodal > bimodal > unimodal. With the best input combination, the RF model (R2 = 0.91, RMSE = 97.56, and CCC = 0.95) outperformed the DNN model (R2 = 0.89, RMSE = 108.72, and CCC = 0.94) in terms of predicting the quality of paddy field. The RF model (R2 = 0.90, RMSE = 104.27, and CCC = 0.95) also outperformed the DNN model (R2 = 0.86, RMSE = 124.38, and CCC = 0.93) in terms of predicting the quality of dry land. The agricultural land quality estimates obtained using the RF and DNN models were more accurate for paddy field than for dry land systems because of greater land quality homogeneity in paddy fields. This research proposed a simple, low-cost for rapid and accurate agricultural land evaluation at the provincial scale using publicly available multimodal data, which can help to achieve control of the agricultural land grade at multiple spatial and temporal scales.

Large-scale agricultural land investments and food security in Nigeria

The study examines how large-scale agricultural land investments (LIs) affect household food security in Nigeria. It is one of the few studies in Nigeria that offers new empirical insights into household food security. Precisely, it unravels how LIs affect the livelihood outcomes of households in communities where LIs operate compared to households in communities without such LIs. This study engages the mixed methods of analysis involving quantitative and qualitative techniques. The quantitative data is drawn from the Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA) utilising the Logistic regression technique. The qualitative aspect of this study involved fieldwork conducted in two distinct Nigerian states: Kwara State, situated in the North-Central region, and Ogun State, located in the South-West region. The qualitative findings further enrich the quantitative findings. It also helped to investigate the nature of jobs households engaged in and also the employment density of the investors’ farms which could not be done using the national representative data (LSMS). The results indicate that households in communities without land investments have 0.2% higher likelihood of being more food secure than households in communities with land investments and it is significant at 5%. The qualitative analysis reveals several key insights, including the observation that female headed households in communities with LIs are less likely to be food secure, receive less wages, and spend more time in communities with LIs. Government should put in place policies (for example, compensation policies) that will ensure that land deals are properly monitored and structured to benefit and protect the host communities. If the government fails to properly regulate land deals, rural people will always suffer. Also, they should monitor and control the activities, and the type of crops grown in investment farms so that land investments can bring about the desired benefits such as improvement in household livelihood and enhance food security in the communities where they exist.

Abandoned Land Mapping Based on Spatiotemporal Features from PolSAR Data via Deep Learning Methods

Monitoring agricultural abandonment is essential in understanding the effects on the environment and food security. Polarimetric synthetic aperture radar (PolSAR) is an efficient approach for the monitoring of large-scale agricultural land cover in cloudy and rainy areas. However, previous studies have not taken advantage of the valuable phase information and not fully utilized the spatiotemporal features of farmland parcels, which has seriously limited the abandoned land identification accuracy. In this study, we developed a new method for the mapping of abandoned land based on the spatiotemporal features from PolSAR Single Look Complex (SLC) images via deep learning methods. First, backscattering coefficients (σ0VV, σ0VH) were derived, and the polarimetric parameters (entropy, anisotropy and mean alpha angle) were obtained based on Cloude–Pottier polarimetric decomposition. Then, the VGG16 deep convolutional network was innovatively used to extract spatial features from both the backscattering coefficients and polarimetric parameters. Next, the separability index was calculated to select the most effective spatial features. Finally, LSTM classifications were conducted based on the time series of backscattering features, the polarimetric parameters, the extracted spatial features and their combinations. The results showed that the introduction of multitemporal polarimetric parameters and spatial features both led to an improvement in the abandoned land identification accuracy. The combination of backscattering features, polarimetric parameters and spatial features yielded the best performance in identifying abandoned land, with producer’s accuracy of 88.29% and user’s accuracy of 84.03%. This study demonstrated the potential of polarimetric parameters and validated the effectiveness of spatiotemporal features in abandoned land identification. It provided a practical method for the production of a highly reliable abandoned land mapping in cloudy and rainy areas.

The Impact of Large-scale Agricultural Investments in Low-income Economies

Recent years have witnessed an increasing interest in large-scale agricultural land acquisitions in developing countries. The accompanying socio-economic implications have been areas of debate among politicians, policymakers and development agents. This paper argues that the traditional way of simulating the impacts of these investments in developing countries is misleading as the approach implies that the new investments are identical to the semi-subsistence way of farming that dominates agricultural practices in the host countries. In this study, we incorporate the peculiarity of large-scale agro-investments into an existing database for economy-wide models, i.e., social accounting matrix (SAM), and capture welfare and distributional outcomes properly. SAM-based multiplier models applied to Ethiopian data justify the need to account for the peculiarity of the investments in terms of production technology and their geographic distribution.

HyperSFormer: A Transformer-Based End-to-End Hyperspectral Image Classification Method for Crop Classification

Crop classification of large-scale agricultural land is crucial for crop monitoring and yield estimation. Hyperspectral image classification has proven to be an effective method for this task. Most current popular hyperspectral image classification methods are based on image classification, specifically on convolutional neural networks (CNNs) and recurrent neural networks (RNNs). In contrast, this paper focuses on methods based on semantic segmentation and proposes a new transformer-based approach called HyperSFormer for crop hyperspectral image classification. The key enhancement of the proposed method is the replacement of the encoder in SegFormer with an improved Swin Transformer while keeping the SegFormer decoder. The entire model adopts a simple and uniform transformer architecture. Additionally, the paper introduces the hyper patch embedding (HPE) module to extract spectral and local spatial information from the hyperspectral images, which enhances the effectiveness of the features used as input for the model. To ensure detailed model processing and achieve end-to-end hyperspectral image classification, the transpose padding upsample (TPU) module is proposed for the model’s output. In order to address the problem of insufficient and imbalanced samples in hyperspectral image classification, the paper designs an adaptive min log sampling (AMLS) strategy and a loss function that incorporates dice loss and focal loss to assist model training. Experimental results using three public hyperspectral image datasets demonstrate the strong performance of HyperSFormer, particularly in the presence of imbalanced sample data, complex negative samples, and mixed sample classes. HyperSFormer outperforms state-of-the-art methods, including fast patch-free global learning (FPGA), a spectral–spatial-dependent global learning framework (SSDGL), and SegFormer, by at least 2.7% in the mean intersection over union (mIoU). It also improves the overall accuracy and average accuracy values by at least 0.9% and 0.3%, respectively, and the kappa coefficient by at least 0.011. Furthermore, ablation experiments were conducted to determine the optimal hyperparameter and loss function settings for the proposed method, validating the rationality of these settings and the fusion loss function.

Towards solar extractivism? A political ecology understanding of the solar energy and agriculture boom in rural China

Contemporary global climate change imperative has stimulated innovative technological systems to combine renewable energy production and other socioeconomic functions, which offer promising solutions to materialize both climate change mitigation and developmental targets. Agrivoltaics—an array of colocation models of solar photovoltaic (PV) infrastructure and agriculture—are becoming increasingly popular across the world and are widely considered a laudable green development model. Since circa 2013, agrivoltaics have started to diffuse across China, with promises of multiple socioeconomic and ecological benefits. Different from existent studies that mostly took apolitical innovation diffusion frameworks, this article draws on an in-depth case study and multiple secondary sources including information on 421 agrivoltaic projects to approach the proliferation of agrivoltaics in China through a political ecology lens. It finds that the diffusion of agrivoltaics in China has been driven neither by technological superiority nor by market-business viability but by a salient logic of solar extractivism in the name of green development. The solar extractivism is featured by: large-scale (agricultural) land acquisition, prioritization of solar electricity production, domination of renewable industries, deep engagement from local state, discourses of green transition and inclusive development, exclusionary enforcement techniques, and neglect of local socioecological systems. The stark contrast between discourses and realities reveals that the deployment of agrivoltaics embodies solar extractivism and the implicated “green agenda” served rather as a legitimation strategy for resource extraction. The solar extractivism perspective cautions against the fetishism of renewables and relevant green infrastructures in popular green development discourses and calls for critical examination of renewable integrative technical models through social justice lenses.

The Influence of Large-Scale Agricultural Land Management on the Modernization of Agricultural Product Circulation: Based on Field Investigation and Empirical Study

Large-scale agricultural land management has become the obvious development trend of China’s rural land management. This paper focuses on large-scale agricultural land management in China and analyzes the influence mechanism of large-scale agricultural land management on the circulation of agricultural products. We use the methods of field investigation and empirical research, put forward the theoretical hypothesis through field investigation, and empirically test it. It is found that the impact of large-scale agricultural land management on the circulation efficiency of the agricultural products under the “input-output” index has a lag and shows a U-shaped characteristic of decreasing first and then increasing. For the modernization of agricultural product circulation under the comprehensive index system, large-scale agricultural land management has a significant positive promoting effect. This reflects the potential of large-scale agricultural land management in promoting the development of rural agriculture and agricultural product circulation. This suggests that in the process of promoting the modernization of agricultural product circulation, the government should pay special attention to the modernization of upstream agricultural production, promote large-scale agricultural land management in a standardized and orderly way, and realize the coordinated reform of agriculture and the agricultural product circulation industry. In addition, the Chinese government also needs to make up for the shortcomings in the upstream organization, the construction of wholesale markets for the agricultural products, and rural logistics infrastructure.

Impact of Transfer of Agricultural Land Functions on Socio-Economic and Socio-Ecological Conditions in West Lombok Regency

Land conversion is basically a natural thing to happen, but in reality it becomes a problem, because it occurs on agricultural land that is still productive and its availability is still limited. The negative impact of shifting the function of paddy fields is not only a decrease in agricultural production, but also has an impact on the socio-economic conditions of the community and the environment of the area concerned. In certain cases, the conversion of paddy fields is unavoidable. This study aims to, 1) Identify the types of land transfer in West Lombok Regency, 2). Analyzing the impact of land conversion on socio-economic conditions in West Lombok Regency and 3) Analyzing the impact of land conversion on socio-ecological conditions in West Lombok Regency. The results showed that the conversion of agricultural land is a change in the designation of agricultural land into non-agricultural land use. The potential land that has been widely converted in Labuapi, Gunungsari and Gerung sub-districts during the last five years (2010-2020) is paddy field and dry land which has changed its land use into housing, restaurants, shops and so on. There are different types of land conversion between the sample sub-districts/villages. Labuapi sub-district experienced large-scale agricultural land conversion, while Gunungsari and Gerung sub-districts experienced small agricultural land conversion. In general, the conversion of agricultural land in the sample sub-districts/villages has a negative impact on socio-economic aspects such as changes in land tenure, employment opportunities, changes in work patterns.

The consequences of expropriation of agricultural land and loss of livelihoods on those households who lost land in Da Nang, Vietnam

Abstract Acquisition of large-scale agricultural land for urbanization and industrialization is a widespread phenomenon in Vietnam. This acquisition has impacted those households whose land was expropriated in many ways, such as economic, cultural, and social aspects. In this research, the author surveyed 100 households who lost their land for Da Nang Hi-Tech Park project to collect data about the change in their livelihoods and the satisfaction level with their quality of life. This study aimed to answer three questions relating to employment, compensation expenses, and life. The results show that these householders still have many difficulties adapting to a new life after nearly ten years. The unemployment rate increases, but it depends on the gender and age of the worker. Compared with the five years ago, the households’ incomes also decreased by 190 USD/household. The misuse of compensation money paid for their has also had negative impacts on their livelihoods. Some other problems such as environmental pollution and social evils have put pressure on households who lost their land. Hence, most of these households want to return to their previous agricultural life.

Protection of local land use rights in the process of large-scale agricultural land acquisition in Ethiopia

ABSTRACT Land use rights are the basis for multidimensional aspects of the local people. In rural Ethiopia, the livelihood of the local people is strongly attached to land use rights. On the other hand, the government has been transferring or allocating huge tracts of land for large-scale agricultural investments through expropriation and the notion of free land doctrine. The role of rural land laws in the protection of local land use rights in the case of agricultural land acquisition has given little attention in research studies. The purpose of this article is to examine the contents of rural land laws in Ethiopia regarding the protection of local land use rights in the process of land acquisition for investment in agriculture. A doctrinal legal research method is employed to examine the protection of rural land use rights in the process of agricultural land acquisition. The author argues that rural land laws are weak in the protection of local land use rights in the process of land acquisition for investment in agriculture. The article recommends the need to have comprehensive and specific governing law that gives strong protection and guarantee for local land use rights in the process of agricultural investment land acquisition.

Assessment of the Local Perceptions on the Drivers of Deforestation and Forest Degradation, Agents of Drivers, and Appropriate Activities in Cambodia

Understanding the drivers of deforestation and forest degradation and the agents of such drivers is important for introducing appropriate policy interventions. Here, we identified drivers and agents of drivers through the analysis of local perceptions using questionnaire surveys, focus group discussions, and field observations. The Likert scale technique was employed for designing the questionnaire with scores ranging from 1 (strongly disagree) to 5 (strongly agree). We found nine direct drivers of forest deforestation and forest degradation, namely illegal logging (4.53 ± 0.60, ± is for standard deviation), commercial wood production (4.20 ± 0.71), land clearing for commercial agriculture (4.19 ± 1.15), charcoal production (3.60 ± 1.12), land clearing for subsistence agriculture (3.54 ± 0.75), new settlement and land migration (3.43 ± 0.81), natural disasters (3.31 ± 0.96), human-induced forest fires (3.25 ± 0.96), and fuelwood for domestic consumption (3.21 ± 0.77). We also found four main indirect drivers, namely lack of law enforcement, demand for timber, land tenure right, and population growth. Our analysis indicates that wood furniture makers, medium and large-scale agricultural investors, charcoal makers, land migrants, firewood collectors, and subsistent farmers were the agents of these drivers. Through focus group discussions, 12 activities were agreed upon and could be introduced to reduce these drivers. In addition to enforcing the laws, creating income-generating opportunities for locals along with the provision of environmental education could ensure long-term reduction of these drivers. The REDD+ project could be an option for creating local income opportunities, while reducing deforestation and forest degradation.

Plantations, women, and food security in Africa: Interrogating the investment pathway towards zero hunger in Cameroon and Ghana

In this paper, we critically engage with SDG-2- Target 2.3 and SDG-5 to examine how and why large-scale agricultural land acquisitions modify the social relations of women’s food access. Adopting qualitative methods and from agrarian political economy perspectives, we assess the implications of plantation investments on food security and gender equality in Cameroon and Ghana. The study draws on the impacts of various plantation schemes in Cameroon and Ghana to argue that the current all-encompassing framing of the SDG-2, whereby support for small-scale production appears to co-exist with the promotion of corporate-led agricultural investments, tends to create a vague interpretation of food security, even when capitalist motives override the interests of rural working men and women. In both countries, plantations have been characterised by displacement, reduced food production and competition over land resources in rural contexts where women bear the burden of social reproduction, particularly in subsistence and food provisioning for their households. Restricted access to farmlands hampers petty commodity production, and provokes rural exodus among farming populations in Cameroon who compete with pastoralists for land; and in the Ghana case, where settlers and migrants compete with dispossessed indigenes for arable lands. The study shows the inherent contradictions and tensions within the narratives of sustainable development and projects which are considered as potential pathways to the SDGs. One of such tension areas is between local food security- which is a gendered role for women in West/Central Africa – and the accumulation imperatives of capitalist investors. We iterate that achieving zero hunger requires gender-inclusive land and labour policies that recognise, protect and empower small farmers and women, and create opportunities for local and national food self-sufficiency.

Operational Large-Area Land-Cover Mapping: An Ethiopia Case Study

Knowledge of land cover and land use nationally is a prerequisite of many studies on drivers of land change, impacts on climate, carbon storage and other ecosystem services, and allows for sufficient planning and management. Despite this, many regions globally do not have accurate and consistent coverage at the national scale. This is certainly true for Ethiopia. Large-area land-cover characterization (LALCC), at a national scale is thus an essential first step in many studies of land-cover change, and yet is itself problematic. Such LALCC based on remote-sensing image classification is associated with a spectrum of technical challenges such as data availability, radiometric inconsistencies within/between images, and big data processing. Radiometric inconsistencies could be exacerbated for areas, such as Ethiopia, with a high frequency of cloud cover, diverse ecosystem and climate patterns, and large variations in elevation and topography. Obtaining explanatory variables that are more robust can improve classification accuracy. To create a base map for the future study of large-scale agricultural land transactions, we produced a recent land-cover map of Ethiopia. Of key importance was the creation of a methodology that was accurate and repeatable and, as such, could be used to create earlier, comparable land-cover classifications in the future for the same region. We examined the effects of band normalization and different time-series image compositing methods on classification accuracy. Both top of atmosphere and surface reflectance products from the Landsat 8 Operational Land Imager (OLI) were tested for single-time classification independently, where the latter resulted in 1.1% greater classification overall accuracy. Substitution of the original spectral bands with normalized difference spectral indices resulted in an additional improvement of 1.0% in overall accuracy. Three approaches for multi-temporal image compositing, using Landsat 8 OLI and Moderate Resolution Imaging Spectroradiometer (MODIS) data, were tested including sequential compositing, i.e., per-pixel summary measures based on predefined periods, probability density function compositing, i.e., per-pixel characterization of distribution of spectral values, and per-pixel sinusoidal models. Multi-temporal composites improved classification overall accuracy up to 4.1%, with respect to single-time classification with an advantage of the Landsat OLI-driven composites over MODIS-driven composites. Additionally, night-time light and elevation data were used to improve the classification. The elevation data and its derivatives improved classification accuracy by 1.7%. The night-time light data improve producer’s accuracy of the Urban/Built class with the cost of decreasing its user’s accuracy. Results from this research can aid map producers with decisions related to operational large-area land-cover mapping, especially with selecting input explanatory variables and multi-temporal image compositing, to allow for the creation of accurate and repeatable national-level land-cover products in a timely fashion.

Female labor outcomes and large-scale agricultural land investments: Macro-micro evidencefrom Tanzania

This paper examined the extent to which Large-scale Agricultural Land Investments (LALIs) has delivered on its promises (e.g. increased productivity, job creation, and rural development, particularly for rural women). We conducted empirical analyses using the Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA) dataset (macro evidence), which was complemented with two case studies of LALIs in Kilombero district, Morogoro region, Tanzania (micro evidence). The findings from the study revealed that the LALIs have limited effect on agricultural wage. However, the results show that LALIs have a negative association with the welfare of female-headed households located in communities with LALIs. On the micro evidence, we found that female-headed households working in the LALIs earned slightly lower agricultural wage compared to those not working in the LALIs. This implies that the use of LALIs in Tanzania to drive agricultural transformation requires specific targeting of potential beneficiaries.

Large-scale agricultural land acquisitions and Ethiopia's ethnic minorities: A test for the rule of law

Large-scale agricultural land acquisitions and Ethiopia's ethnic minorities: A test for the rule of law