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An understanding of changes in forest cover and the drivers of forest transition (FT) contributes to the sustainable management of global forests. In this paper, we used the latest global land cover data published by the European Space Agency (ESA) to investigate spatiotemporal variation characteristics of forest cover in developing countries from 1992 to 2015, and then analyzed causal factors of this variation using a binary logistic regression model. Existing studies on FT are mostly based on data from the Food and Agriculture Organization (FAO); this study improves our understanding of FT mechanisms through the use of a new dataset. The results indicate that the forest area in developing countries decreased from 21.8 to 21.3 million km2 from 1992–2015, and the rate of decline slowed after 2004. South America suffered the largest reduction in forest area (505,100 km2), whereas forest area in Africa increased slightly. By 2015, more than 80% of African countries had experienced FT, whereas only half of developing countries experienced forest expansion in South America. The variables affecting FT occurrence differed among continents. On the global scale, the remaining forest coverage and the proportion of forest exports negatively affected the likelihood of FT occurrence, whereas urbanization level had a positive effect.

An understanding of changes in forest cover and the drivers of forest transition (FT) contributes to the sustainable management of global forests. In this paper, we used the latest global land cover data published by the European Space Agency (ESA) to investigate spatiotemporal variation characteristics of forest cover in developing countries from 1992 to 2015, and then analyzed causal factors of this variation using a binary logistic regression model. Existing studies on FT are mostly based on data from the Food and Agriculture Organization (FAO); this study improves our understanding of FT mechanisms through the use of a new dataset. The results indicate that the forest area in developing countries decreased from 21.8 to 21.3 million km2 from 1992–2015, and the rate of decline slowed after 2004. South America suffered the largest reduction in forest area (505,100 km2), whereas forest area in Africa increased slightly. By 2015, more than 80% of African countries had experienced FT, whereas only half of developing countries experienced forest expansion in South America. The variables affecting FT occurrence differed among continents. On the global scale, the remaining forest coverage and the proportion of forest exports negatively affected the likelihood of FT occurrence, whereas urbanization level had a positive effect.

The effect of soil amendment with biochar has been widely evaluated for its effects in mitigating greenhouse gas emissions (GHG) and remediating polluted soils with metals; however, a synergic understanding of the system, including biochar, soil, and microbial activity, is lacking. In this study, a meta-analysis of 854 paired data from 73 studies demonstrate that biochar application in soil affects GHG emissions and soil metal availability. First, several properties of biochar, soil, and microbial activity were considered as parameters in the meta-analysis. Then, the size effect was evaluated using the percentage of change (Pc) as obtained by the meta-analyzed data. Several parameters were related as influencer factors in GHG emissions and soil metal availability. Notably, biochar addition in soil resulted in a significant CO2 increase in emissions, whereas N2O emissions decreased; these results were directly correlated with microbial activity. Although this trend, demonstrated by the data analysis, differs from results of other studies found in the literature, it also emphasized the need for a deep understanding of the effect of biochar addition to soil (properties, nutrients, gas exchange, etc.) and to microorganisms (activity, diversity, etc.). Furthermore, it was also proved, that soil metal concentration decreases significantly when biochar was added (Cd > Zn > Pb > Cu > Fe). According to the results, biochar addition in soils contaminated with Cd and Cu was related to an increase in the microbial activity; while, soils amended with biochar but polluted with Pb, Zn, and Fe presented a higher inhibition effect on microorganisms. To improve the interpretation of soil amendment with biochar, it would be necessary to standardize the form for reporting results, particularly of the microbial activity and GHG emissions, in order to be used for future comparative studies.

The effect of soil amendment with biochar has been widely evaluated for its effects in mitigating greenhouse gas emissions (GHG) and remediating polluted soils with metals; however, a synergic understanding of the system, including biochar, soil, and microbial activity, is lacking. In this study, a meta-analysis of 854 paired data from 73 studies demonstrate that biochar application in soil affects GHG emissions and soil metal availability. First, several properties of biochar, soil, and microbial activity were considered as parameters in the meta-analysis. Then, the size effect was evaluated using the percentage of change (Pc) as obtained by the meta-analyzed data. Several parameters were related as influencer factors in GHG emissions and soil metal availability. Notably, biochar addition in soil resulted in a significant CO2 increase in emissions, whereas N2O emissions decreased; these results were directly correlated with microbial activity. Although this trend, demonstrated by the data analysis, differs from results of other studies found in the literature, it also emphasized the need for a deep understanding of the effect of biochar addition to soil (properties, nutrients, gas exchange, etc.) and to microorganisms (activity, diversity, etc.). Furthermore, it was also proved, that soil metal concentration decreases significantly when biochar was added (Cd > Zn > Pb > Cu > Fe). According to the results, biochar addition in soils contaminated with Cd and Cu was related to an increase in the microbial activity; while, soils amended with biochar but polluted with Pb, Zn, and Fe presented a higher inhibition effect on microorganisms. To improve the interpretation of soil amendment with biochar, it would be necessary to standardize the form for reporting results, particularly of the microbial activity and GHG emissions, in order to be used for future comparative studies.

Accurate detection of weeds in farmland can help reduce pesticide use and protect the agricultural environment. To develop intelligent equipment for weed detection, this study used an imaging spectrometer system, which supports micro-scale plant feature analysis by acquiring high-resolution hyper spectral images of corn and a number of weed species in the laboratory. For the analysis, the object-oriented classification system with segmentation and decision tree algorithms was utilized on the hyper spectral images to extract shape and texture features of eight species of plant leaves, and then, the spectral identification characteristics of different species were determined through sensitive waveband selection and using vegetation indices calculated from the sensitive band data of the images. On the basis of the comparison and analysis of the combined characteristics of spectra, shape, and texture, it was determined that the spectral characteristics of the ratio vegetation index of R677/R710 and the normalized difference vegetation index, shape features of shape index, area, and length, as well as the texture feature of the entropy index could be used to build a discrimination model for corn and weed species. Results of the model evaluation showed that the Global Accuracy and the Kappa coefficient of the model were both over 95%. In addition, spectral and shape features can be regarded as the preferred characteristics to develop a device of weed identification from the view of accessibility to crop/weeds discriminant features, according to different roles of various features in classifying plants. Therefore, the results of this study provide valuable information for the portable device development of intelligent weed detection.

Accurate detection of weeds in farmland can help reduce pesticide use and protect the agricultural environment. To develop intelligent equipment for weed detection, this study used an imaging spectrometer system, which supports micro-scale plant feature analysis by acquiring high-resolution hyper spectral images of corn and a number of weed species in the laboratory. For the analysis, the object-oriented classification system with segmentation and decision tree algorithms was utilized on the hyper spectral images to extract shape and texture features of eight species of plant leaves, and then, the spectral identification characteristics of different species were determined through sensitive waveband selection and using vegetation indices calculated from the sensitive band data of the images. On the basis of the comparison and analysis of the combined characteristics of spectra, shape, and texture, it was determined that the spectral characteristics of the ratio vegetation index of R677/R710 and the normalized difference vegetation index, shape features of shape index, area, and length, as well as the texture feature of the entropy index could be used to build a discrimination model for corn and weed species. Results of the model evaluation showed that the Global Accuracy and the Kappa coefficient of the model were both over 95%. In addition, spectral and shape features can be regarded as the preferred characteristics to develop a device of weed identification from the view of accessibility to crop/weeds discriminant features, according to different roles of various features in classifying plants. Therefore, the results of this study provide valuable information for the portable device development of intelligent weed detection.

Interest in organic foods is increasing at a moment when humanity is facing a range of health challenges including the concern that some conventionally produced foods may pose possible adverse effects on human and livestock health. With the increasing human population, intensive production is increasingly trending towards high-input systems that aim to close yield gaps, increase crop yields, and develop new crop varieties with higher yield potential and tolerance to biotic and abiotic stresses, all within the context of incorporating specific traits to satisfy consumer demand. Potato (Solanum tuberosum L.) is one of the most consumed foods under different cultural diets, however its production faces some challenges related to soilborne diseases, marketable yield and quality, sugars and dry matter content of the produced tubers, tuber content in terms of nitrate, minerals, vitamins, bioactive compounds and antioxidants, and consumer appreciation regarding the sensory characteristics of tubers and processed products. Different studies have been investigating some of these challenges, with sometimes straightforward and sometimes connflicting results. This variability in research results indicates the general non-transferability of the results from one location to another under the same management practices in addition to differences in plant material. This review compares some characteristics of raw or boiled potato and processed products from potato tubers grown organically and conventionally. Ideally, such information may be of benefit in decision making by consumers in their dietary choices, by potato growers in their selection of crop management practices, and by scientists looking at potential areas for future research on potatoes.