Calcium (Ca²⁺) plays an important role in determining plant growth and development because it maintains cell wall and
membrane integrity. Therefore, understanding the role of Ca2+ in carbon and lipid metabolism could provide insights
into the dynamic changes in cell membranes and cell walls during the rapid elongation of cotton fibers. In the present
study, we found that the lack of Ca²⁺ promoted fiber elongation and rapid ovule expansion, but it also caused tissue
browning in the ovule culture system. RNA-sequencing revealed that Ca²⁺ deficiency induced cells to be highly oxidized,
and the expression of genes related to carbon metabolism and lipid metabolism was activated significantly. All gene
members of nine key enzymes involved in glycolysis were up-regulated, and glucose was significantly reduced in Ca²⁺
deficiency-treated tissues. Ca²⁺ deficiency adjusted the flowing of glycolysis metabolic. However, low K⁺ recovered
the expression levels of glycolysis genes and glucose content caused by Ca2+ deficiency. Electrospray ionizationtandem
mass spectrometry technology was applied to uncover the dynamic profile of lipidome under Ca²⁺ and K⁺
interacted conditions. Ca²⁺ deficiency led to the decrease of fatty acid (FA), diacylglycerol (DAG), glycolipid and the
significant increase of triacylglycerol (TAG), phospholipid phosphatidylethanolamine (PE), phosphatidylglycerol (PG),
and PC (phosphatidylcholine). Low K⁺ restored the contents of FA, phospholipids, and glycolipids, effectively relieved
the symptoms caused by Ca²⁺ deficiency, and recovered the development of fiber cells. This study revealed dynamic
changes in transcript and metabolic levels and uncovered the signaling interaction of Ca²⁺ deficiency and low K⁺ in
glycolysis and lipid metabolism during fiber development.

Crop planting patterns are an important component of agricultural land systems.  These patterns have been significantly changed due to the combined impacts of climatic changes and socioeconomic developments.  However, the extent of these changes and their possible impacts on the environment, terrestrial landscapes and rural livelihoods are largely unknown due to the lack of spatially explicit datasets including crop planting patterns.  To fill this gap, this study proposes a new method for spatializing statistical data to generate multitemporal crop planting pattern datasets.  This method features a two-level model that combines a land-use simulation and a crop pattern simulation.  The output of the first level is the spatial distribution of the cropland, which is then used as the input for the second level, which allocates crop censuses to individual gridded cells according to certain rules.  The method was tested using data from 2000 to 2019 from Heilongjiang Province, China, and was validated using remote sensing images.  The results show that this method has high accuracy for crop area spatialization.  Spatial crop pattern datasets over a given time period can be important supplementary information for remote sensing and thus support a wide range of application in agricultural land systems.

Information on crop acreage is important for formulating national food polices and economic planning.  Spatial sampling, a combination of traditional sampling methods and remote sensing and geographic information system (GIS) technology, provides an efficient way to estimate crop acreage at the regional scale.  Traditional sampling methods require that the sampling units should be independent of each other, but in practice there is often spatial autocorrelation among crop acreage contained in the sampling units.  In this study, using Dehui County in Jilin Province, China, as the study area, we used a thematic crop map derived from Systeme Probatoire d’Observation de la Terre (SPOT-5) imagery, cultivated land plots and digital elevation model data to explore the spatial autocorrelation characteristics among maize and rice acreage included in sampling units of different sizes, and analyzed the effects of different stratification criteria on the level of spatial autocorrelation of the two crop acreages within the sampling units.  Moran’s I, a global spatial autocorrelation index, was used to evaluate the spatial autocorrelation among the two crop acreages in this study.  The results showed that although the spatial autocorrelation level among maize and rice acreages within the sampling units generally decreased with increasing sampling unit size, there was still a significant spatial autocorrelation among the two crop acreages included in the sampling units (Moran’s I varied from 0.49 to 0.89), irrespective of the sampling unit size.  When the sampling unit size was less than 3 000 m, the stratification design that used crop planting intensity (CPI) as the stratification criterion, with a stratum number of 5 and a stratum interval of 20% decreased the spatial autocorrelation level to almost zero for the maize and rice area included in sampling units within each stratum.  Therefore, the traditional sampling methods can be used to estimate the two crop acreages.  Compared with CPI, there was still a strong spatial correlation among the two crop acreages included in the sampling units belonging to each stratum when cultivated land fragmentation and ground slope were used as stratification criterion.  As far as the selection of stratification criteria and sampling unit size is concerned, this study provides a basis for formulating a reasonable spatial sampling scheme to estimate crop acreage.

Here, the ozone-treated domestic sludge was diluted up to four different multiples and utilized as a nutritional source for hydroponic lettuce growth.  Additionally, lettuce was cultured using the modified Hoagland nutrient solution as a control.  The effects of ozone-treated domestic sludge on lettuce growth and nutrition were studied.  Results showed that the lettuce treated with modified Hoagland inorganic nutrient solution had increased leaf number, plant height, fresh weight and dry weight compared to those treated with the ozone-treated domestic sludge dilution (P<0.05).  However, the lettuce cultivated with the 2-fold ozone-treated sludge dilution showed significantly higher (P<0.05) contents of chlorophyll, soluble sugar and ascorbic acid (Vc) compared to that treated with modified Hoagland nutrient solution.  And the nitrate concentration in the lettuce cultured with the 2-fold ozone-treated sludge dilution was 53.93% less than that cultured with the modified Hoagland nutrient solution, which was a significant improvement (P<0.05).  This study suggested that the 2-fold ozone-treated sludge dilution is optimal for lettuce hydroponic nutrient requirements.

Rice area has been expanding rapidly during the past 30 years under the influence of global change in northeastern China, which is the northernmost region of rice cultivation in China.  However, the spatio-temporal dynamic changes in rice area are still unclear, although they may have important policy implications for environmental protection and adaptation to climate change.  In this study, we aimed to identify the dynamic changes of the rice area in Heilongjiang Province of northeastern China by extracting data from multiple Landsat images.  The study used ground quadrats selected from Google Earth and the extraction of a confusion matrix to verify the accuracy of extraction.  The overall accuracy of the extracted rice area was higher than 95% as a result of using the artificial neural network (ANN) classification method.  The results showed that the rice area increased by approximately 2.4×10⁶ ha during the past 30 years at an annual rate of 8.0×10⁴ ha, and most of the increase occurred after 2000.  The central latitude of the rice area shifted northwards from 46 to 47°N during the study period, and moved eastwards from 130 to 133°E.  The rice expansion area accounted for 98% of the total change in rice area, and rice loss was notably rare.  The rice expansion was primarily from dryland.  In addition, rice cultivation in marshland and grassland played a minor role in the rice expansion in this region.

A total of 479 bacterial strains were isolated from brine (Bohai, Qinhuangdao City, Hebei Province, China). Bioassay results indicated that 4 strains named Ha1, Ha17, Ha38, and Ha384 had herbicidal activity. And strain Ha1 had the highest effective herbicidal activity. As a result, this study aims to identify strain Ha1, characterize its physiological and biological activities, evaluate the herbicidal activity of its metabolites, and develop a ‘pesta’ formulation and assess its effectiveness on Digitaria sanguinalis. Ha1 was identified as Serratia marcescens based on 16S rDNA sequencing. This strain has a flagellum, a diameter of 0.5 to 0.8 μm, and a length of 0.9 to 2.0 μm. The indole test shows positive results, and the catalase enzyme exhibits strong positive reactions. Results further showed that the inhibitory concentration (IC50) of the crude extracts to D. sanguinalis radicula and coleoptile were 3.332 and 2.828 mg mL–1, respectively. Both the suppression of D. sanguinalis and the cell viability of the Ha1 formulation in ‘pesta’ were higher when stored at 4°C than at (25±2)°C. These results indicated that S. marcescens Ha1 can potentially be used as a biocontrol agent against D. sanguinalis.

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.

For the scientific management of farmland, it is significant to understand the spatio-temporal variability of soil organic matter and to study the influences of related factors. Using geostatistical theory, GIS spatial analysis, trend analysis and a Geographically Weighted Regression (GWR) model, this study analyzed the response of soil organic matter to climate and socio-economic factors in central Heilongjiang Province during the past 25 years. Second soil survey data of China for 1979-1985, 2005 field sampling data, climate observations and socio-economic data for 1980-2005 were analyzed. First, soil organic matter in 2005 was spatially interpolated using the Co-Kriging method along with auxiliary data sets of soil type and pH. The spatio-temporal variability was then studied by comparison with the 1980s second soil census data. Next, the temporal trends in climate and socio-economic factors over the past 25 years were investigated. Finally, we examined the variation of the response of soil organic matter to climate and socio-economic factors using the GWR model spatially and temporally. The model showed that 53.82% area of the organic matter content remained constant and 29.39% has decreased during the past 25 years. The impact of precipitation on organic matter content is mainly negative, with increasing absolute values of the regression coefficient. The absolute value of regression coefficient of annual average temperature has decreased, and more areas are now under its negative effects. In addition, the areas of positive regression coefficient of annual sunshine hours have northward shifted, with the increasing absolute value of positive coefficient and decreasing absolute value of negative coefficient. The areas of positive regression coefficient of mechanized farming as a socio-economic factor have westward shifted, with the increasing absolute value of negative coefficient and decreasing absolute value of positive coefficient. The area of regions with the positive regression coefficient of irrigation has expanded. The regions with positive regression coefficient of fertilizer use have shrinked. The positive regression coefficient of mulch film consumption has significantly increased. The regression coefficient of pesticide consumption was mainly positive in the west of the study area, while it was negative to the east. Generally, GWR model is capable to investigate the influence of both climatic and socio-economic factors, avoided the insufficiency of other research based on the single perspective of climatic or socio-economic factors. Therefore, we can conclude that GWR model could provide methodological support for global change research and serve as basic reference for cultivated land quality improvement and agricultural decision making.

Remote sensing, in particular satellite imagery, has been widely used to map cropland, analyze cropping systems, monitor crop changes, and estimate yield and production. However, although satellite imagery is useful within large scale agriculture applications (such as on a national or provincial scale), it may not supply sufficient information with adequate resolution, accurate geo-referencing, and specialized biological parameters for use in relation to the rapid developments being made in modern agriculture. Information that is more sophisticated and accurate is required to support reliable decision-making, thereby guaranteeing agricultural sustainability and national food security. To achieve this, strong integration of information is needed from multi-sources, multi-sensors, and multi-scales. In this paper, we propose a new framework of satellite, aerial, and groundintegrated (SAGI) agricultural remote sensing for use in comprehensive agricultural monitoring, modeling, and management. The prototypes of SAGI agriculture remote sensing are first described, followed by a discussion of the key techniques used in joint data processing, image sequence registration and data assimilation. Finally, the possible applications of the SAGI system in supporting national food security are discussed.

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.