Agricultural Water-Land-Plant System Engineering—Updated Achievements to Improve Crop Growth

1. Introduction to the Special Issue

To meet the needs of an estimated 9.7 billion people, global food production must increase by 50–60% by 2050 [1]. However, international agricultural production is becoming increasingly vulnerable due to diminishing water resources and the degradation of soil quality caused by climate change [2,3]. On a global scale, the overall effects of climate change on crop yields are negative; median per-decade yield impacts—without adaptation—are as follows: −2.1% for maize, −1.2% for soybean, −0.7% for rice, and −1.2% for wheat [4]. Approximately 1 °C of warming and more frequent extreme heat and rainfall during 2000–2009 in West Africa resulted in regional average yield reductions of 10–20% for millet and 5–15% for sorghum [5]. Water shortages in central United States have led to over-exploitation of groundwater for agricultural irrigation; if the current rate of ground water depletion continues, it will decrease regional corn and wheat production by 6.75% and 1.08%, respectively, by 2050 [6]. Various climate change scenarios in China from the 2010s to the 2090s will increase total crop water use of maize, rice, and wheat by 41.4–43.4%, 27.8–29.3%, and 11.7–12.5%, respectively. Meanwhile, the projected decreases in precipitation in wheat and rice growth seasons will lead to proportional reductions in crop yield [7]. Water-saving irrigation methods, optimal agronomic practices, and advanced soil management could greatly improve crop growth environments and enhance crop production. However, the effects of these technologies will vary with crop and growth conditions. Therefore, optimal and smart technologies targeted at agricultural water-land-plant systems in specific regions and for specific crops should be further researched, helping to mitigate the negative effects of water shortages and climate change on sustainable high crop production.

Data are the basis for determining optimal water and nutrient management in the agricultural water-land-plant system. Timely and accurate data acquisition is a key activity for precisely evaluating and managing the performance of agricultural technologies. Traditional methods of acquiring data for soil and plants are manual and time-intensive. For example, soil samples are mostly taken from the field to measure soil water, nutrients, and salt contents. Crop samples are manually collected to obtain growth indicators, including total biomass and leaf area; nutrient content of total nitrogen, phosphorus, and potassium in plant tissues and fruits; as well as crop yields and harvest indicators. Traditional methods can accurately provide crop and soil data; however, they have a long processing time which negates quick decisions in smart agricultural management. Remote sensing technology provides an alternative approach to quickly obtain soil and crop data on a large scale. Images from satellites and unmanned aerial vehicles (UAV) have been widely used to acquire data on crop planting area, crop height and LAI, crop evapotranspiration, as well as crop biomass and yield for cases where appropriate algorithms have been developed [8,9]. Crop water status—a key parameter for irrigation scheduling and evaluating field water balance [10,11], especially on a large scale—is increasingly being estimated using remote sensing and UAV technologies [12].

Advanced technologies are vital for achieving sustainable crop production under water shortage and climate change conditions. Therefore, this Special Issue focuses on the latest research on agricultural engineering in the agricultural water-land-plant system, including mechanisms for the regulation of crop water, fertilizer, salt, heat and microclimate; water-saving irrigation; agronomic technologies and modes; comprehensive observation and simulation technology for farmland ecosystems; and agricultural production management.

2. Main Contributions of This Special Issue

This Special Issue includes eight research articles that focus on advances in improvement of soil and water system quality, crop growth response to water quality and agronomic practices, climate change and food production, and crop data retrieval using UAV and remote sensing technologies.

2.1. Agricultural Environment and Plant Responses

Soil water and quality are key factors for crop growth and yield. Both soil water stress and water logging can limit root water uptake and negatively influence crop root development and growth. Prolonged water logging significantly suppressed the root growth of two representative herbaceous species in the lower region (145–155 m elevations zone) of the Three Gorges Reservoir in China; root length density and root tips were markedly reduced (p < 0.05) by 1–5 times and 1–7 times, respectively, compared to those in the high elevation region (175 m elevation zone), where water logging duration is shorter [contribution 1]. Nutrient ion storage and cycling in the soil determines soil quality. Plant litter is an important contributor of nutrients to soil and water. Zheng et al. reported that the average annual concentration and storage of sodium in plant litter was 538 mg/kg and 2957 mg/m² in a forest ecosystem, respectively, with significantly higher values during the rainy season compared to the dry season [contribution 2]. They also reported that litter sodium storage is mainly distributed in leaf, twig, and fine wood debris, and that storage varies with plant litter type—higher storage in broadleaf riparian forests compared to mixed forests [contribution 2]. Soil and water carbon concentrations are key factors not only for soil and water quality, but also for carbon emission and sink management. Wang et al. reported that emergent plants have the highest carbon storage capacities and higher carbon densities compared with floating plants [contribution 3], which provide measures for enhancing the carbon sequestration potential in artificial wetland construction in urban ecosystems.

2.2. Crop Growth Response to Water Quality and Agronomic Practices

Irrigation is critical to support global food security. Irrigated croplands produce 48% of global crops in terms of value [13]. In China, irrigated land accounts for approximately 56% of the total arable land, and produces 77% of China’s total grains and 90% of its cash crops [14]. In some regions with water shortage, polluted water was used to irrigate crops to obtain high yield, which influenced crop germination and fruit quality. In Turkey, water resources with high concentrations of heavy metals (copper (Cu), iron (Fe), lead (Pb), chromium (Cr), arsenic (As), nickel (Ni), and cadmium (Cd)) from streams have been used for irrigation. The study results show that high heavy metal concentrations in irrigation water (4–20 times higher than the permissible limits) from the Karasu Creek, which had toxic Cu and Fe levels, led to a decline in seed germination rates (approximately 20% lower) and adversely impacted early seedling growth. Ryegrass seeds were most affected by these irrigation waters (approximately 40% lower germination rate) [contribution 4]. In a rain-fed region, conservation tillage technology was confirmed to enhance agricultural production efficiency and mitigate wind erosion and land degradation. Cong et al. reported that combining no-tillage and straw mulching technology in a semi-arid region of Northeast China enhanced the soil water retention of seeding (40–50 mm) and curtailed soil sediment transport and wind erosion. This resulted in notably higher soil moisture (20–30%), nitrate nitrogen concentration (14–49%), and soil carbon content (10–19%) during the crucial growth season of maize, causing an increase in maize yield (14.5% to 16.6%) and WUE (18.3% to 21.7%) compared to conventional tillage [contribution 5]. Therefore, adopting integrated no-tillage and straw mulching technology together with good quality water is critical for enhancing crop growth and producing high quality yield.

2.3. Climate Change and Food Production

Climate change—characterized by increasing temperatures and frequent heavy rainfall and drought events—has complicated effects on the water–crop–carbon nexus. Ye et al. developed an integrated optimization and prediction agricultural model, based on the water–energy–food–carbon nexus theory, to systematically analyze the interactions of climate change on agricultural systems. The model was used to evaluate future trends in grain yield and water use in the Pearl River Basin of Southeast China. They found that rising temperatures significantly reduce rice and maize crop yields. Moreover, increased CO₂ concentrations, for example, from 600 ppm to 1135 ppm (predicted for the end of the century by the SSP585 scenario), will shift the crop yield trends from negative to positive, further reducing water, energy, and carbon footprints by 12.82%, 10.62%, and 10.59%, respectively. Based on the above-mentioned results, water management in agricultural systems under climate change should be regulated to ensure sustainable agricultural production [contribution 6].

2.4. Crop Data Retrieval Using UAV and Remote Sensing Technology

Smart agriculture management based on timely big data can significantly enhance agricultural productivity, reduce costs, and promote environmental sustainability. At present, unmanned aerial vehicles (UAV), together with remote sensing, are commonly used to quickly acquire plant and irrigation water information on a large scale. Canopy water interception, which is difficult to measure in field conditions because of the influence of microclimate and canopy structure, is a key component in evaluating the water budget of sprinkler irrigation and rainfall. Zhou et al. (contribution 7) developed an algorithm using light band data from UAV images to estimate canopy interception under sprinkler irrigation. They reported that the developed method performed well with root mean square errors of 0.18–0.27 mm and relative errors of 21–27%. They noted that the estimated canopy interception is underestimated by 18–32% when interception is higher than 1.4 mm, mainly due to the saturation of the normalized difference vegetation index (NDVI) when leaf area index is higher than 4.0 [contribution 7].

High-resolution remote sensing data (for example Landsat 4/5/7/8) has been increasingly used to estimate actual land surface evapotranspiration (ETa). The GeeSEBAL model is widely used to estimate ETa based on remote sensing data, but the accuracy of the model is uncertain for heterogeneous land surfaces. In contribution 8, Hu et al. optimized the quantile values of the end element components in the GeeSEBAL model, and found that the improved GeeSEBAL model increased estimation accuracy. They also reported that the GeeSEBAL algorithm has the highest sensitivity to the parameter of vegetation index for adjusting soil brightness (SAVI_hot) for all biological communities, which is not considered in the original model. However, the detailed sensitivity parameters may change with different biological communities, and it is necessary to optimize for each vegetation type separately [contribution 8]. In contribution 3, Wang et al. used remote sensing technology to assess the vegetation distribution characteristics and carbon density variations in two artificial wetlands, and found that there was a significant increase in vegetation coverage and carbon sequestration from 2018 to 2023—particularly in lake shore areas—indicating positive outcomes from wetland ecological restoration and management measures [contribution 3].

The contributions presented in this Special Issue provide novel insights into responses to climate change, irrigation water quality, flood duration, wetland ecosystem restoration, and conservation tillage on plant growth, soil water dynamics, nutrients and carbon. These insights highlight the need to develop optimal algorithms and enhance model parameters to efficiently acquire crop and land surface information using UAV and remote sensing. The technologies reported in this Special Issue could help improve the management of soil environments, which would enhance crop growth and resource-use efficiency under climate change and variations in water quality and quantity.