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Protecting the environment and enhancing food security are among the world's Sustainable Development Goals and greatest challenges. International food trade is an important mechanism to enhance food security worldwide. Nonetheless, it is widely concluded that in international food trade importing countries gain environmental benefits, while exporting countries suffer environmental problems by using land and other resources to produce food for exports. Our study shows that international food trade can also lead to environmental pollution in importing countries. At the global level, our metaanalysis indicates that there was increased nitrogen (N) pollution after much farmland for domestically cultivated N-fixing soybeans in importing countries was converted to grow high N-demanding crops (wheat, corn, rice, and vegetables). The findings were further verified by an intensive study at the regional level in China, the largest soybean-importing country, where the conversion of soybean lands to corn fields and rice paddies has also led to N pollution. Our study provides a sharp contrast to the conventional wisdom that only exports contribute substantially to environmental woes. Our results suggest the need to evaluate environmental consequences of international trade of all other major goods and products in all importing countries, which have significant implications for fundamental rethinking in global policy-making and debates on environmental responsibilities among consumers, producers, and traders across the world., Competing Interests: The authors declare no conflict of interest.

The pairwise separability index (SI) has been demonstrated as an effective indicator for capturing crucial phenological differences between two plant species. However, its application to crop types, which have more obvious phenological characteristics than natural vegetation, has received less attention, and extending the pairwise SI to multiple crops for feature selection still remains a challenge. This paper presented two SI extension approaches (\textSI\mathrm{ave} and \textSI\min) to select the optimal spectro-temporal features for multiple crops, and investigated their classification performance using Heilongjiang Province, China, as a study area. Feature interpretability and classification accuracy of different crops were evaluated for the two approaches. The results showed that the \textSI\mathrm{ave} approach generally has relatively high feature interpretability due to its better description of crucial phenological characteristics of different crops. Those crops with high separability are insensitive to the extension approach and have similar classification accuracy for the two approaches, whereas those crops with poor separability show good performance with the \textSI\min method. Due to the higher temporal autocorrelation, the optimal features for crop classification that are selected by the \textSI\mathrm{ave} approach exhibit greater information redundancy across the time domain than those that are selected by the \textSI\min approach, which largely explains the relatively low classification accuracy achieved using the \textSI\mathrm{ave} approach. These comparison results between \textSI\min and \textSI\mathrm{ave} approaches also indicate that time-series images with high temporal resolution do not necessarily produce high classification accuracy, regardless of their ability to describe the seasonal characteristics of crops. [ABSTRACT FROM PUBLISHER]

Aim Agricultural practices have dramatically altered the land cover of the earth, but the spatial extent and intensity of these practices is often difficult to catalogue. Information on the distribution and performance of specific crops is often only available through national or subnational statistics. Recently, however, there have been multiple independent efforts to incorporate the detailed information available from statistical surveys with supplemental spatial information to produce a spatially explicit global dataset specific to individual crops. While these datasets provide decision makers with improved information on global cropping systems, the final global cropping maps differ substantially from one another. This study aims to explore and quantify systematic similarities and differences between four major global cropping systems products and the subsequent implications for analyses dependent on those models. Location This study was conducted at a global scale. Methods Each global cropping systems model was assessed by latitude as a measure of biophysical plausibility and each pair of models was compared using a Gaussian filter to remove trivial spatial discrepancies. Model disagreement was explored in relation to the interdependent input data of each model pair with a particular focus on cropland extent. The influence of the observed model differences on subsequent analyses was demonstrated using model-dependent estimates of the yield gap as an example. Results The results of our analysis indicate that the choice of cropping systems model is non-trivial: considerable differences exist between model-specific estimates of the yield gap across nearly all climate zones and the average model difference exceeds the average estimated yield gap in certain regions. The differences in crop-specific harvested area and yield products of each model are significant, and mostly result from differences in the input datasets and downscaling methodologies. In particular, the choice of dataset on cropland extent proved to be influential regardless of the downscaling process employed. Main conclusions Discrepancies in the final products of cropping systems models are currently poorly understood, but have implications for basic policy decisions relating to agricultural production and food security. The considerable disagreement between models serves as a reminder of the ongoing challenges to the creation of spatially explicit estimates of harvested area and yield based on crop statistics. Our analysis helps shed light on the importance of model choice by demonstrating the implications for further analyses that depend on cropping systems models, and works to overcome these challenges by characterizing model-dependent differences in harvested area and yield. [ABSTRACT FROM AUTHOR]

The impact of climate change on the crop water footprint (WF) is highly uncertain, hindering effective agricultural water use in response to climate change. This study proposes a factor sensitivity analysis method, which can screen out statistically significant climatic factors and interactions on crop WF under various uncertainties and determine water resource management measures to mitigate drought for different crops. A special case study was conducted in Heilongjiang Province, China. The results showed that (1) the annual change in crop WF showed a downward trend from 1988 to 2018. The crop WF was dominated by green water footprint (WF green), and the average occupancy rate of WF green in crops was 58.7%–74.1 %; the spatial distribution of WF has latitude zonality. (2) Drought has different effects on WF of different crops, and WF of soybean is susceptible to drought. Wind speed, sunshine hours, and humidity have a greater impact on crop WF in most growth stages. (3) The effect of climatic factors on crop WF varies in different months. The rice WF is mainly affected by the climate in May, and there is an interaction between May humidity and May rain. The WF of maize and soybeans are affected primarily by the climate in July, especially sunshine hours. The proposed approach attempts to analyze that crop WF is affected by not only an individual climatic factor but also their interactions. Crop water management practices should be adjusted based on the results to mitigate the adverse impact of climatic conditions on crop WF during different growing months. [ABSTRACT FROM AUTHOR]