Classification of rare land cover types: Distinguishing annual and perennial crops in an agricultural catchment in South Korea

Many environmental data are inherently imbalanced, with some majority land use and land cover types dominating over rare ones. In cultivated ecosystems minority classes are often the target as they might indicate a beginning land use change. Most standard classifiers perform best on a balanced distribution of classes, and fail to detect minority classes. We used the synthetic minority oversampling technique (smote) with Random Forest to classify land cover classes in a small agricultural catchment in South Korea using modis time series. This area faces a major soil erosion problem and policy measures encourage farmers to replace annual by perennial crops to mitigate this issue. Our major goal was therefore to improve the classification performance on annual and perennial crops. We compared four different classification scenarios on original imbalanced and synthetically oversampled balanced data to quantify the effect of smote on classification performance. smote substantially increased the true positive rate of all oversampled minority classes. However, the performance on minor classes remained lower than on the majority class. We attribute this result to a class overlap already present in the original data set that is not resolved by smote. Our results show that resampling algorithms could help to derive more accurate land use and land cover maps from freely available data. These maps can be used to provide information on the distribution of land use classes in heterogeneous agricultural areas and could potentially benefit decision making

Crop diversity and stability of revenue on farms in Central Europe : an analysis of big data from a comprehensive agricultural census in Bavaria

<div><p>Diversity of agricultural landscapes is important to maintain the provision of ecosystem services. In face of decreasing support measures for agricultural markets in the European Union, diversified crop portfolios could also offer a possibility to stabilize revenue at farm level (portfolio effect). We hypothesize that (i) diversity of crop portfolios changes along spatial gradients in the study area (Bavaria, Germany), (ii) the composition of portfolios depends on farm parameters, and (iii) more diverse portfolios on arable land provide higher revenue stability. We analysed agricultural census data comprising all farms (<i>N</i> = 105 314) in the study area and identified 26 typical crop portfolios. We show that portfolio composition is related to farm characteristics (whole farm revenue, farm type, farm size) and location. Currently, diversification of crop portfolios fails to promote stability of portfolio revenue in the study area, where policy still indirectly influences market prices of energy crops. We conclude that the portfolio effect as a natural insurance was less important in recent years due to high market prices for specific crops. This low need for natural insurances probably favoured simplified portfolios leading to decreased agricultural diversity.</p></div

Is Ridge Cultivation Sustainable? A Case Study from the Haean Catchment, South Korea

Non-sustainable agricultural practices can alter the quality of soil and water. A sustainable soil management requires detailed understanding of how tillage affects soil quality, erosion, and leaching processes. Agricultural soils in the Haean catchment (South Korea) are susceptible to erosion by water during the monsoon. For years, erosion-induced losses have been compensated by spreading allochthonous sandy material on the fields. These anthropogenically modified soils are used for vegetable production, and crops are cultivated in ridges using plastic mulches. To evaluate whether the current practice of ridge cultivation is sustainable with regard to soil quality and soil and water conservation, we (i) analysed soil properties of topsoils and (ii) carried out dye tracer experiments. Our results show that the sandy topsoils have a very low soil organic matter content and a poor structure and lack soil burrowers. The artificial layering induced by spreading sandy material supported lateral downhill water flow. Ridge tillage and plastic mulching strongly increased surface runoff and soil erosion. We conclude that for this region a comprehensive management plan, which aims at long-term sustainable agriculture by protecting topsoils, increasing soil organic matter, and minimizing runoff and soil erosion, is mandatory for the future

Identification and quantification of macro- and microplastics on an agricultural farmland

Abstract Microplastic contamination of aquatic ecosystems is a high priority research topic, whereas the issue on terrestrial ecosystems has been widely neglected. At the same time, terrestrial ecosystems under human influence, such as agroecosystems, are likely to be contaminated by plastic debris. However, the extent of this contamination has not been determined at present. Via Fourier transform infrared (FTIR) analysis, we quantified for the first time the macro- and microplastic contamination on an agricultural farmland in southeast Germany. We found 206 macroplastic pieces per hectare and 0.34 ± 0.36 microplastic particles per kilogram dry weight of soil. In general, polyethylene was the most common polymer type, followed by polystyrene and polypropylene. Films and fragments were the dominating categories found for microplastics, whereas predominantly films were found for macroplastics. Since we intentionally chose a study site where microplastic-containing fertilizers and agricultural plastic applications were never used, our findings report on plastic contamination on a site which only receives conventional agricultural treatment. However, the contamination is probably higher in areas where agricultural plastic applications, like greenhouses, mulch, or silage films, or plastic-containing fertilizers (sewage sludge, biowaste composts) are applied. Hence, further research on the extent of this contamination is needed with special regard to different cultivation practices

Crop selection under price and yield fluctuation : Analysis of agro-economic time series from South Korea

Temporal fluctuations of crop price and yield can have a strong inuence on farmers\u27 revenue. Under uncertainty, farmers\u27 crop selection on { what to cultivate and how much of their land to allocate to different crops{ is of crucial importance to secure their revenue as well as related ecosystem services. Multi-crop farming can be seen as a strategy to mitigate uncertainties that farmers face. In this study, we used Singular Spectrum Analysis (SSA) to quantify the fluuctuations of crop price and yield for single and multiple crop selections in South Korea. Furthermore, risk adjusted revenue of each crop selection was analysed using the Sharpe ratio. We constructed three empirical crop portfolios containing one, three and five crops. For the single crop farming, six main crops in South Korea were analysed, and household data were used to build empirical crop portfolios. Our results showed that revenue from rice farming was the most stable, whereas it fluctuated strongly for pepper. However, growing rice provided the lowest revenue and farmers who cultivate multiple crops might as much as double their revenue compared to rice farming. Diversified crop farming can be a means of enhancing revenue. The biggest part of fluctuations in portfolios with several crops was seasonal, which might be mitigated by planning in advance. The artificial stability of rice price was due to policy intervention. However, it should be noted that the rice policy has been reformed and a high protection for domestic rice farming would no longer last in the future in South Korea. These results might have practical consequences for farmers\u27 decision making on crop selection as well as for agricultural policy

Classification of rare land cover types : Distinguishing annual and perennial crops in an agricultural catchment in South Korea

Many environmental data are inherently imbalanced, with some majority land use and land cover types dominating over rare ones. In cultivated ecosystems minority classes are often the target as they might indicate a beginning land use change. Most standard classifiers perform best on a balanced distribution of classes, and fail to detect minority classes. We used the synthetic minority oversampling technique (smote) with Random Forest to classify land cover classes in a small agricultural catchment in South Korea using modis time series. This area faces a major soil erosion problem and policy measures encourage farmers to replace annual by perennial crops to mitigate this issue. Our major goal was therefore to improve the classification performance on annual and perennial crops. We compared four different classification scenarios on original imbalanced and synthetically oversampled balanced data to quantify the effect of smote on classification performance. smote substantially increased the true positive rate of all oversampled minority classes. However, the performance on minor classes remained lower than on the majority class. We attribute this result to a class overlap already present in the original data set that is not resolved by smote. Our results show that resampling algorithms could help to derive more accurate land use and land cover maps from freely available data. These maps can be used to provide information on the distribution of land use classes in heterogeneous agricultural areas and could potentially benefit decision making

Image analysis of dye stained patterns in soils

Quality of surface water and groundwater is directly affected by flow processes in the unsaturated zone. In general, it is difficult to measure or model water flow. Indeed, parametrization of hydrological models is problematic and often no unique solution exists. To visualise flow patterns in soils directly dye tracer studies can be done. These experiments provide images of stained soil profiles and their evaluation demands knowledge in hydrology as well as in image analysis and statistics. First, these photographs are converted to binary images classifying the pixels in dye stained and non-stained ones. Then, some feature extraction is necessary to discern relevant hydrological information. In our study we propose to use several index functions to extract different (ideally complementary) features. We associate each image row with a feature vector (i.e. a certain number of image function values) and use these features to cluster the image rows to identify similar image areas. Because images of stained profiles might have different reasonable clusterings, we calculate multiple consensus clusterings. An expert can explore these different solutions and base his/her interpretation of predominant flow mechanisms on quantitative (objective) criteria. The complete workflow from reading-in binary images to final clusterings has been implemented in the free R system, a language and environment for statistical computing. The calculation of image indices is part of our own package Indigo, manipulation of binary images, clustering and visualization of results are done using either build-in facilities in R, additional R packages or the LATEX system