Winter wheat and cotton are the main crops grown on the North China Plain (NCP). Cotton is often transplanted after the winter wheat harvest to solve the competition for cultivated land between winter wheat and cotton, and to ensure that both crops can be harvested on the NCP. However, the root system of transplanted cotton is distorted due to the restrictions of the seedling aperture disk before transplanting. Therefore, the investigation of the deformed root distribution and water uptake in transplanted cotton is essential for simulating soil water movement under different irrigation methods. Thus, a field experiment and a simulation study were conducted during 2013–2015 to explore the deformed roots of transplanted cotton and soil water movement using border irrigation (BI) and surface drip irrigation (SDI). The results showed that SDI was conducive to root growth in the shallow root zone (0–30 cm), and that BI was conducive to root growth in the deeper root zone (below 30 cm). SDI is well suited for producing the optimal soil water distribution pattern for the deformed root system of transplanted cotton, and the root system was more developed under SDI than under BI. Comparisons between experimental data and model simulations showed that the HYDRUS-2D model described the soil water content (SWC) under different irrigation methods well, with root mean square errors (RMSEs) of 0.023 and 0.029 cm³ cm⁻³ and model efficiencies (EFs) of 0.68 and 0.59 for BI and SDI, respectively. Our findings will be very useful for designing an optimal irrigation plan for BI and SDI in transplanted cotton fields, and for promoting the wider use of this planting pattern for cotton transplantation.

Flat (F), mini-ditch (MD), and ridge-furrow (RF) are three conventional cotton planting patterns that are usually adopted around the world, yet soil and crop responses to these three patterns are poorly studied, as is their suitability for increasing yield for coastal areas in Eastern China. The effects of three planting methods on water and salt dynamics as well as on growth and lint yield of cotton (Gossypium hirsutum L.) were investigated in a saline field in Bohai Rim, China, to select the best planting pattern for cultivating coastal saline fields of Eastern China. Soil moisture in the root zone with RF was 11.9% and 12.1% higher than with F and MD, whereas the electrical conductivity of a saturated soil extract (EC_e) in the root zone with RF was 18.0% and 13.8% lower than with MD and F, respectively, during the growth period, which indicated that RF could efficiently collect rainfall and leach salt in the root zone. After drip irrigation, the infiltration and salt-leaching depth with RF were both deeper than that with F and MD. The stand establishment of MD was the highest (80.3%) due to the greenhouse effect from film mulching, and was 12.8% and 4.6% higher than that with F and RF, respectively. Growth indicators and lint yield demonstrated that RF was superior to F and MD because of the higher soil moisture and lower EC_e. The lint yield was significantly higher in RF, suggesting that RF can be an optimal planting pattern for agricultural reclamation in similar saline-alkaline areas around the world.

Collecting accurate real-time soil moisture data in crop root zones is the foundation of automated precision irrigation systems. Soil moisture sensors (SMSs) have been used to monitor soil water content (SWC) in crop fields for a long time; however, there is no generally accepted guideline for determining optimal number and placement of soil moisture sensors in the soil profile. In order to study adequate positioning for the installation of soil moisture sensors in the soil profile, six years of field experiments were carried out in North China Plain (NCP). Soil water content was measured using the gravimetric method every 7 to 10 days during six growing seasons of winter wheat (Triticum aestivum L), and root distribution was measured using a soil core method during the key periods of winter wheat growth. The results from the experimental data analysis show that SWC at different depths had a high linear correlation. In addition, the values of correlation coefficients decreased with increasing soil depth; the coefficient of variation (CV) of SWC was higher in the surface layers than in the deeper layers (depths were 0–40 cm, 0–60 cm, and 0–100 cm during the early, middle, and last stages of winter wheat, respectively); wheat roots were mainly distributed in the surface layer. According to an analysis of CV for SWC and root distribution, the depths of planned wetted layers were determined to be 0–40 cm, 0–60 cm, and 0–100 cm during the sowing to reviving stages (the early stage of winter wheat), returning green and jointing stages (the middle stage of winter wheat), and heading to maturity stage (the last stage of winter wheat), respectively. The correlation and R-cluster analyses of SWC at different layers in the soil profile showed that SMSs should be installed 10 and 30 cm below the soil surface during the winter wheat growing season. The linear regression model can be built using SWC at depths of 10 and 30 cm to predict total average SWC in the soil profile. The results of validation showed that the developed model provided reliable estimates of total average SWC in the planned wetted layer. In brief, this study suggests that suitable positioning of soil moisture sensors is at depths of 10 and 30 cm below the soil surface.