Yield gap analysis could provide management suggestions to increase crop yields, while the understandings of resource utilization efficiency could help judge the rationality of the management. Based on more than 110 published papers and data from Food and Agriculture Organization (FAO, www.fao.org/faostat) and the Global Yield Gap and Water Productivity Atlas (www.yieldgap.org), this study summarized the concept, quantitative method of yield gap, yield-limiting factors, and resource utilization efficiency of the three major food crops (wheat, maize and rice). Currently, global potential yields of wheat, maize and rice were 7.7, 10.4 and 8.5 t ha^–1, respectively. However, actual yields of wheat, maize and rice were just 4.1, 5.5 and 4.0 t ha^–1, respectively. Climate, nutrients, moisture, crop varieties, planting dates, and socioeconomic conditions are the most mentioned yield-limiting factors. In terms of resource utilization, nitrogen utilization, water utilization, and radiation utilization efficiencies are still not optimal, and this review has summarized the main improvement measures. The current research focuses on quantitative potential yield and yield gap, with a rough explanation of yield-limiting factors. Subsequent research should use remote sensing data to improve the accuracy of the regional scale and use machine learning to quantify the role of yield-limiting factors in yield gaps and the impact of change crop management on resource utilization efficiency, so as to propose reasonable and effective measures to close yield gaps.

How to fully use spectral and temporal information for efficient identification of crops becomes a crucial issue since each crop has its specific seasonal dynamics.  A thorough understanding on the relative usefulness of spectral and temporal features is thus essential for better organization of crop classification information.  This study, taking Heilongjiang Province as the study area, aims to use time-series moderate resolution imaging spectroradiometer (MODIS) surface reflectance product (MOD09A1) data to evaluate the importance of spectral and temporal features for crop classification.  In doing so, a feature selection strategy based on separability index (SI) was first used to rank the most important spectro-temporal features for crop classification.  Ten feature scenarios with different spectral and temporal variable combinations were then devised, which were used for crop classification using the support vector machine and their accuracies were finally assessed with the same crop samples.  The results show that the normalized difference tillage index (NDTI), land surface water index (LSWI) and enhanced vegetation index (EVI) are the most informative spectral features and late August to early September is the most informative temporal window for identifying crops in Heilongjiang for the observed year 2011.  Spectral diversity and time variety are both vital for crop classification, and their combined use can improve the accuracy by about 30% in comparison with single image.  The feature selection technique based on SI analysis is superior for achieving high crop classification accuracy (producers’ accuracy of 94.03% and users’ accuracy of 93.77%) with a small number of features.  Increasing temporal resolution is not necessarily important for improving the classification accuracies for crops, and a relatively high classification accuracy can be achieved as long as the images associated with key phenological phrases are retained.

High-resolution satellite data have been playing an important role in agricultural remote sensing monitoring.  However, the major data sources of high-resolution images are not owned by China.  The cost of large scale use of high resolution imagery data becomes prohibitive.  In pace of the launch of the Chinese “High Resolution Earth Observation Systems”, China is able to receive superb high-resolution remotely sensed images (GF series) that equalizes or even surpasses foreign similar satellites in respect of spatial resolution, scanning width and revisit period.  This paper provides a perspective of using high resolution remote sensing data from satellite GF-1 for agriculture monitoring.  It also assesses the applicability of GF-1 data for agricultural monitoring, and identifies potential applications from regional to national scales.  GF-1’s high resolution (i.e., 2 m/8 m), high revisit cycle (i.e., 4 days), and its visible and near-infrared (VNIR) spectral bands enable a continuous, efficient and effective agricultural dynamics monitoring.  Thus, it has gradually substituted the foreign data sources for mapping crop planting areas, monitoring crop growth, estimating crop yield, monitoring natural disasters, and supporting precision and facility agriculture in China agricultural remote sensing monitoring system (CHARMS).  However, it is still at the initial stage of GF-1 data application in agricultural remote sensing monitoring.  Advanced algorithms for estimating agronomic parameters and soil quality with GF-1 data need to be further investigated, especially for improving the performance of remote sensing monitoring in the fragmented landscapes.  In addition, the thematic product series in terms of land cover, crop allocation, crop growth and production are required to be developed in association with other data sources at multiple spatial scales.  Despite the advantages, the issues such as low spectrum resolution and image distortion associated with high spatial resolution and wide swath width, might pose challenges for GF-1 data applications and need to be addressed in future agricultural monitoring.

Although climate change impacts and agricultural adaptations have been studied extensively, how smallholder farmers perceive climate change and adapt their agricultural activities is poorly understood. Survey-based data (presents farmers’ personal perceptions and adaptations to climate change) associated with external biophysical-socioeconomic data (presents real-world climate change) were used to develop a farmer-centered framework to explore climate change impacts and agricultural adaptations at a local level. A case study at Bin County (1980s-2010s), Northeast China, suggested that increased annual average temperature (0.6°C per decade) and decreased annual precipitation (46 mm per decade, both from meteorological datasets) were correctly perceived by 76 and 66.9%, respectively, of farmers from the survey, and that a longer growing season was confirmed by 70% of them. These reasonably correct perceptions enabled local farmers to make appropriate adaptations to cope with climate change: Longer season alternative varieties were found for maize and rice, which led to a significant yield increase for both crops. The longer season also affected crop choice: More farmers selected maize instead of soybean, as implicated from survey results by a large increase in the maize growing area. Comparing warming-related factors, we found that precipitation and agricultural disasters were the least likely causes for farmers’ agricultural decisions. As a result, crop and variety selection, rather than disaster prevention and infrastructure improvement, was the most common ways for farmers to adapt to the notable warming trend in the study region.

Rice is one of the most important grain crops in Northeast China (NEC) and its cultivation is sensitive to climate change. This study aimed to explore the spatio-temporal changes in the NEC rice planting area over the period of 1980-2010 and to analyze their relationship to climate change. To do so, the CLUE-S (conversion of land use and its effects at small region extent) model was first updated and used to simulate dynamic changes in the rice planting area in NEC to understand spatio-temporal change trends during three periods: 1980-1990, 1990-2000 and 2000-2010. The changing results in individual periods were then linked to climatic variables to investigate the climatic drivers of these changes. Results showed that the NEC rice planting area expanded quickly and increased by nearly 4.5 times during 1980-2010. The concentration of newly planted rice areas in NEC constantly moved northward and the changes were strongly dependent on latitude. This confirmed that climate change, increases in temperature in particular, greatly influenced the shift in the rice planting area. The shift in the north limit of the NEC rice planting area generally followed a 1°C isoline migration pattern, but with an obvious time-lag effect. These findings can help policy makers and crop producers take proper adaptation measures even when exposed to the global warming situation in NEC.

To feed the increasing world population, more food needs to be produced from agricultural land systems. Solutions to produce more food with fewer resources while minimizing adverse environmental and ecological consequences require sustainable agricultural land use practices as supplementary to advanced biotechnology and agronomy. This review paper, from a land system perspective, systematically proposed and analyzed three interactive strategies that could possibly raise future food production under global change. By reviewing the current literatures, we suggest that cropland expansion is less possible amid fierce land competition, and it is likely to do less in increasing food production. Moreover, properly allocating crops in space and time is a practical way to ensure food production. Climate change, dietary shifts, and other socio-economic drivers, which would shape the demand and supply side of food systems, should be taken into consideration during the decision-making on rational land management in respect of sustainable crop choice and allocation. And finally, crop-specific agricultural intensification would play a bigger role in raising future food production either by increasing the yield per unit area of individual crops or by increasing the number of crops sown on a particular area of land. Yet, only when it is done sustainably is this a much more effective strategy to maximize food production by closing yield and harvest gaps.