• Red huajiao distribution under different periods was predicted using Maxent. • Suitable habitats were located mainly in the Qinba Mountains and the semi-humid and semi-arid areas of the Loess Plateau. • Mean temperature of the coldest quarter and min temperature of the coldest month were the important environmental factors. • The amides content in red huajiao peels varied significantly across suitable habitats. • Temperature, wind speed and precipitation were the key climate factors affecting amide accumulation.

Since the reform and opening up, regional imbalances in the development of market economy and urbanization have significantly changed the spatial agglomeration pattern of grain production (GP) in China. To characterize GP by the yield, we used the gravity center (GC) and standard deviation ellipse (SDE) to investigate the evolution characteristics of the spatial agglomeration pattern of GP and its three major crops with wheat, maize and rice in China, then establish a spatial econometric model to explore the natural and socioeconomic drivers of GP at the national level, at different crops and at different regions. The research results indicate that the GC of GP gradually shifts and expands to the northeast and presents a spatial distribution pattern in a “northeast–southwest” direction. The GC of wheat production mainly expands upwards in the east–west direction. The GC of maize production shifts in a direction that is more consistent with grain production, and there is an expansion trend in the east–west direction, while the GC of rice production has the largest north–south span, and continues to expand upward to the north by the east. The status of grain production in Northeast China and Northwest China is rising and the importance of grain production in the Southeast coastal areas are decreasing. The importance of wheat and corn production in North China continues to strengthen, and Northeast China is becoming more important for rice production, but the middle and lower reaches of the Yangtze River are still important rice producing regions. Changes in GP with significant spatial dependence are jointly affected by many factors, such as natural and socio-economic factors, and there are obvious differences among different food crops and different division regions, with the most prominent positive effects being the multiple crop index (MCI), arable land per capita (AL) and agricultural mechanization (MECH), while economic growth and urbanization are significantly negative.

Improving agricultural water use efficiency (AWUE) is an important way to solve the shortage of water resources in arid and semi-arid regions. This study used the Super-DEA (data envelopment analysis) to measure the AWUE of 52 cities in Northwest China from 2000 to 2018. Based on spatial and temporal perspectives, it applied Exploratory Spatial Data Analysis (ESDA) to explore the dynamic evolution and regional differences of AWUE. A spatial econometric model was then used to analyze the main factors that influence the AWUE in Northwest China. The results showed firstly that the overall AWUE in Northwest China from 2000 to 2018 presented a steady upward trend. However, only a few cities achieved effective agricultural water usage by 2018, and the differences among cities were obvious. Secondly, AWUE showed an obvious spatial autocorrelation in Northwest China and showed significant high–high and low–low agglomeration characteristics. Thirdly, economic growth, urbanization development, and effective irrigation have significant, positive effects on AWUE, while per capita water resource has a significant, negative influence. Finally, when improving the AWUE in arid and semi-arid regions, plans should be formulated according to local conditions. The results of this study can provide new ideas on the study of AWUE in arid and semi-arid regions and provide references for the formulation of regional agricultural water resource utilization policies as well.

Implementation of the Grain for Green program (GGP) intensifies land use/cover change (LUCC) in the loess hilly-gully region. Clarifying the response of LUCC to the GGP and its driving forces are basic premises to implement the GGP more effectively for alleviating soil erosion in this region. This study analyzed the spatio-temporal characteristics of conversion of cultivated land to forest land and grassland in two study periods of 2000–2010 and 2010–2018. The transition matrix model and the dynamic degree model were utilized to explore changes among cultivated land, forest land, and grassland based on the remote sensing (RS) and monitoring data of land use in 2000, 2010, and 2018. Secondly, further detection on driving forces of increase of forest land and grassland was conducted through the logistic regression model. Fourteen driving factors were selected: the GGP, elevation, slope, population density, GDP per land area, distance to city, distance to residential area, etc. The results revealed that: (1) Area of cultivated land was mainly transferred to forest land and grassland during two study periods. The conversion of cultivated land to forest land and grassland occupied 21.48% and 68.01% of outward-transferring area of cultivated land from 2000 to 2010, and accounted for 13.26% and 74.3% from 2010 to 2018; (2) From the results of the logistic regression model, elevation, the GGP, annual mean temperature, slope III (6–15°), and GDP per land area were the main driving forces from 2000 to 2010. Moreover, the most prominent driving forces were the GGP, elevation, rural population density, slope III (6–15°), and soil pH from 2010 to 2018. The findings of this study can help us better understand the conversion of cultivated land to forest land and grassland under the GGP and provide a scientific basis to facilitate sustainable development of land resources in the study area.