Observed Changes in Agroclimate Metrics Relevant for Specialty Crop Production in California

Every decade, a suite of standardized climatological metrics known as climate normals are updated, providing averages of temperature and precipitation data over the previous 30-year period. Although some of these climate normals are directly applicable to agricultural producers, there are additional agroclimate metrics calculated from meteorological data that provide physiologically relevant information for on-farm management decisions. In this study, we identified a suite of energy-based agroclimate metrics and calculated changes over the two most recent normal periods (1981–2010 and 1991–2020), focusing on specialty crop production regions in California. Observed changes in agroclimate metrics were largely consistent with broader global warming trends. While most metrics showed small changes between the two periods, during the 1991–2020 period, the last spring freeze occurred ~5 days earlier as compared to the 1981-2010 period, contributing to a >6 day longer frost-free period in the Sacramento and Salinas Valleys; likewise an additional 6.4 tropical nights (Tn > 20 °C) occurred in the Coachella Valley during the 1991-2020 period. A complementary trend analysis of the agroclimate metrics over the 1981–2020 period showed significant increases in growing degree days across all agricultural regions, while significant increases in heat exposure were found for the Salinas and Imperial Valleys and over the Central Coast region. Moreover, summer reference evapotranspiration increased approximately 40 mm in California’s Central Valley during 1981–2020, with implications for agricultural water resources. Quantifying the shifts in these agroclimate metrics between the two most recent 30-year normal periods and the accompanying 40-year trends provides context for understanding and communicating around changing climatic baselines and underscores the need for adaptation to meet the challenge that climate change poses to agriculture both in the future and in the present.

Evaluating regional agricultural water resources system resilience and analyzing driving factors in the Hongxinglong Administration

A Projection Pursuit Classification model optimized by the Cat Swarm Optimization algorithm (CSO-PPC) was proposed to evaluate system resilience in Hongxinglong Administration of Heilongjiang Province, China. Meanwhile, the driving forces behind resilience were analyzed using Principal Component Analysis (PCA). CSO-PPC was used to evaluate resilience for the 12 farms in Hongxinglong Administration, and PCA was applied to select the key factors driving their resilience. Results showed that the key factors were per capita water, unit area grain yield, application of fertilizer per unit cultivated area and the proportion of cultivated land, which were closely related to human production and planting area. Overall water resources system resilience improved by 2011 compared to 2005. Specifically, water resources system resilience grades for the 12 farms were divided into five levels from inferior to superior, i.e. I to V. After six years of development, the resilience of eight farms had improved. Farm Youyi and Farm 853 were upgraded from inferior level II to the best level V. However, according to the data, four farms still had low resilience that had not improved in recent years. Further results showed that the driving forces decreased from 1998 to 2003 and increased from 2003 to 2011.

A Review of Irrigation Information Retrievals from Space and Their Utility for Users

Irrigation represents one of the most impactful human interventions in the terrestrial water cycle. Knowing the distribution and extent of irrigated areas as well as the amount of water used for irrigation plays a central role in modeling irrigation water requirements and quantifying the impact of irrigation on regional climate, river discharge, and groundwater depletion. Obtaining high-quality global information about irrigation is challenging, especially in terms of quantification of the water actually used for irrigation. Here, we review existing Earth observation datasets, models, and algorithms used for irrigation mapping and quantification from the field to the global scale. The current observation capacities are confronted with the results of a survey on user requirements on satellite-observed irrigation for agricultural water resources’ management. Based on this information, we identify current shortcomings of irrigation monitoring capabilities from space and phrase guidelines for potential future satellite missions and observation strategies.

Land Use Optimization in a Resource-Exhausted City Based on Simulation of the F-E-W Nexus

Limited land resources are used to meet the growing economic, social, and ecological needs of people in China. Food, energy, and water (F-E-W) are the basic resources for supporting human survival and the transformation of different land uses. This paper tries to construct a theoretical framework of land use and the F-E-W nexus and uses system dynamics to simulate the optimal allocation of land use in Shizuishan City, China, by comparing different scenarios that have different parameters related to F-E-W. The final results follow: (1) according to the relationship between land use and the F-E-W nexus, a three-layer nested theoretical framework is constructed. (2) Future land use under different scenarios differs. Under the scenarios of a less dependence on coal energy, a higher utilization efficiency of energy and agricultural water resources, and a lower grain self-sufficiency rate, there are less crop and urban lands but more ecological land. However, generally speaking, crop and rural construction lands tend to decrease, while urban and ecological lands tend to increase. (3) Combined with different objectives, the rapid transformation scenario is considered a better option in which to achieve a balance among the economy, society, and ecology. This paper also discusses the application of land use optimization in the delineation of three control lines in territory-space planning in China.

Are China’s Water Resources for Agriculture Sustainable? Evidence from Hubei Province

We assessed the sustainability of agricultural water resources in Hubei Province, a typical agricultural province in central China, for a decade (2008–2018). Since traditional evaluation models often consider only the distance between the evaluation point and the positive or negative ideal solution, we introduce gray correlation analysis and construct a new sustainability evaluation model. Our research results show that only one city had excellent sustainable development capacity of agricultural water resources, and the evaluation value of eight cities fluctuated by around 0.5 (the median of the evaluation result), while the sustainable development capacity of agricultural water resources in other cities was relatively poor. Our findings not only reflect the differences in the natural conditions of water resources among various cities in Hubei, but also the impact of the cities’ policies to ensure efficient agricultural water use for sustainable development. The indicators and methods in this research are not difficult to obtain in most countries and regions of the world. Therefore, the indicator system we have established by this research could be used to study the sustainability of agricultural water resources in other countries, regions, or cities.