Understanding the Evaluation Abilities of External Cluster Validity Indices to Internal Ones

Evaluating internal Cluster Validity Index (CVI) is a critical task in clustering research. Existing studies mainly employ the number of clusters (NC-based method) or external CVIs (external CVIs-based method) to evaluate internal CVIs, which are not always reasonable in all scenarios. Additionally, there is no guideline of choosing appropriate methods to evaluate internal CVIs in different cases. In this paper, we focus on the evaluation abilities of external CVIs to internal CVIs, and propose a novel approach, named external CVI\u27s evaluation Ability MEasurement approach through Ranking consistency (CAMER), to measure the evaluation abilities of external CVIs quantitatively, for assisting in selecting appropriate external CVIs to evaluate internal CVIs. Specifically, we formulate the evaluation ability measurement problem as a ranking consistency task, by measuring the consistency between the evaluation results of external CVIs to internal CVIs and the ground truth performance of internal CVIs. Then, the superiority of CAMER is validated through a real-world case. Moreover, the evaluation abilities of seven popular external CVIs to internal CVIs in six different scenarios are explored by CAMER. Finally, these explored evaluation abilities are validated on four real-world datasets, demonstrating the effectiveness of CAMER

Low N Fertilizer Application and Intercropping Increases N Concentration in Pea (Pisum sativum L.) Grains

Sustainable intensification of pulses needs reduced input of nitrogen (N) fertilizer with enhanced crop nutritional quality and yield. Therefore, increasing N harvest in grains (sink organs) by improving N remobilization is of key importance. Previous research has shown that a lower dose of N fertilizer effectively increases the rate of N remobilization, while intercropping improves the grain N concentration in pea (Pisum sativum L.). However, it is unknown whether intercropping can facilitate this N fertilizer effect to increase N remobilization, and thereby enhance the N harvest index (NHI). In this study, we determined N allocation among different organs of pea plants, N translocation from leaf and stem tissues to pods, N2 fixation, N utilization efficiency, and NHI of pea plants grown alone or intercropped with maize (Zea mays L.) with different N fertilization treatments in a field experiment in northwestern China from 2012 to 2014. A base application of 90 kg N ha−1 at sowing and top-dress application of 45 kg N ha−1 at flowering integrated with maize–pea intercropping increased N allocation to pod tissues, N translocation to grains, and NHI of pea plants. Compared with the application of 90 kg N ha−1 at sowing and 135 kg N ha−1 top-dressed at flowering, reducing the top-dress application of N fertilizer to 45 kg N ha−1 increased N allocation to intercropped pea plants by 8%. Similarly, N translocation to grains from leaf and stem tissues was increased by 37.9 and 43.2%, respectively, enhancing the NHI by 40.1%. A positive correlation between N2 fixation and NHI was observed, implying that N2 fixation improves N concentration in grain sinks. Thus, our data show that growing pulses in an intercropping system with reduced N fertilization are essential for maximizing N translocation, improving nutritional quality, and preventing the loss of N through leaching, thereby avoiding potential groundwater contamination

Wheat-Maize Intercropping With Reduced Tillage and Straw Retention: A Step Towards Enhancing Economic and Environmental Benefits in Arid Areas

Intercropping is considered a promising system for boosting crop productivity. However, intercropping usually requires higher inputs of resources that emit more CO2. It is unclear whether an improved agricultural pattern could relieve this issue and enhance agricultural sustainability in an arid irrigation area. A field experiment using a well-designed agricultural practice was carried out in northwest China; reduced tillage, coupled with wheat straw residue retention measures, was integrated with a strip intercropping pattern. We determined the crop productivity, water use, economic benefits, and carbon emissions (CEs). The wheat-maize intercropping coupled with straw covering (i.e., NTSI treatment), boosted grain yield by 27–38% and 153–160% more than the conventional monoculture of maize and wheat, respectively, and it also increased by 9.9–11.9% over the conventional intercropping treatment. Similarly, this pattern also improved the water use efficiency by 15.4–22.4% in comparison with the conventional monoculture of maize by 45.7–48.3% in comparison with the conventional monoculture of wheat and by 14.7–15.9% in comparison with the conventional intercropping treatment. Meanwhile, NTSI treatment caused 7.4–13.7% and 37.0–47.7% greater solar energy use efficiency than the conventional monoculture of maize and wheat, respectively. Furthermore, the NTSI treatment had a higher net return (NR) by 54–71% and 281–338% and a higher benefit per cubic meter of water (BPW) by 35–51% and 119–147% more than the conventional monoculture of maize and wheat, respectively. Similarly, it increased the NR and BPW by 8–14% and 14–16% in comparison with the conventional intercropping treatment, respectively. An additional feature of the NTSI treatment is that it reduced CEs by 13.4–23.8% and 7.3–17.5% while improving CE efficiency by 62.6–66.9% and 23.2–33.2% more than the conventional monoculture maize and intercropping treatments, respectively. We can draw a conclusion that intercropping maize and wheat, with a straw covering soil surface, can be used to enhance crop production and NRs while effectively lowering CO2 emissions in arid oasis irrigation region

Optimized Nitrogen Rate, Plant Density, and Regulated Irrigation Improved Grain, Biomass Yields, and Water Use Efficiency of Maize at the Oasis Irrigation Region of China

Nitrogen is a key factor in maize (Zea mays L.) grain and biomass production. Inappropriate application with sub-optimum plant density and irrigation can lead to low productivity and inefficient use. A two-year field experiment was conducted to determine which nitrogen rate, plant density, and irrigation level optimize grain, biomass yield, and water use efficiency. Three nitrogen rates of urea (46–0–0 of N–P2O5–K2O) (N0 = 0 kg N ha−1, N1 = 270 kg N ha−1, and N2 = 360 kg N ha−1), with three maize densities (D1 = 75,000 plants ha−1, D2 = 97,500 plants ha−1, and D3 = 120,000 plants ha−1), and two irrigation levels (W1 = 5250 m3/hm2 and W2 = 4740 m3/hm2) were investigated. The results show that both grain and biomass yields were affected by the main factors. The interaction between nitrogen rate and irrigation level significantly (p < 0.001) affected grain yield but not biomass. It was observed that the grain yield increased correspondingly with nitrogen rate and plant density, while it decreased as the irrigation level increased. Water use efficiency was significantly (p < 0.001) affected by the main factors and their interactions. Nevertheless, water use efficiency was highest at (5250 m3/hm2) × 270 kg N ha−1; × 360 kg N ha−1 × 120,000 plants ha−1 and increased from 62% to 68%. In addition, the highest biomass yield was recorded at 5250 m3/hm2 × 270 kg N ha−1; × 360 kg N ha−1 × 120,000 plants ha−1 while the interaction of either irrigation level with 0 and 270 kg ha−1 or 97,500 and 120,000 plants ha−1 yielded the lowest water use efficiency. Thus, optimized nitrogen rates, plant density, and alternate irrigation levels can support optimum grain and biomass yields. It can also improve nitrogen and water use efficiency in maize production

Optimized Nitrogen Rate, Plant Density, and Regulated Irrigation Improved Grain, Biomass Yields, and Water Use Efficiency of Maize at the Oasis Irrigation Region of China

Nitrogen is a key factor in maize (Zea mays L.) grain and biomass production. Inappropriate application with sub-optimum plant density and irrigation can lead to low productivity and inefficient use. A two-year field experiment was conducted to determine which nitrogen rate, plant density, and irrigation level optimize grain, biomass yield, and water use efficiency. Three nitrogen rates of urea (46–0–0 of N–P2O5–K2O) (N0 = 0 kg N ha−1, N1 = 270 kg N ha−1, and N2 = 360 kg N ha−1), with three maize densities (D1 = 75,000 plants ha−1, D2 = 97,500 plants ha−1, and D3 = 120,000 plants ha−1), and two irrigation levels (W1 = 5250 m3/hm2 and W2 = 4740 m3/hm2) were investigated. The results show that both grain and biomass yields were affected by the main factors. The interaction between nitrogen rate and irrigation level significantly (p p 3/hm2) × 270 kg N ha−1; × 360 kg N ha−1 × 120,000 plants ha−1 and increased from 62% to 68%. In addition, the highest biomass yield was recorded at 5250 m3/hm2 × 270 kg N ha−1; × 360 kg N ha−1 × 120,000 plants ha−1 while the interaction of either irrigation level with 0 and 270 kg ha−1 or 97,500 and 120,000 plants ha−1 yielded the lowest water use efficiency. Thus, optimized nitrogen rates, plant density, and alternate irrigation levels can support optimum grain and biomass yields. It can also improve nitrogen and water use efficiency in maize production

Microbial Community Shifts with Soil Properties and Enzyme Activities in Inter-/Mono-Cropping Systems in Response to Tillage

No-till and cereal–legume intercropping have been recognized as favorable cropping practices to increase crop yields while maintaining soil quality in arid and semiarid environments, but the biological mechanisms are poorly understood. The present study aimed to determine the response of yields, soil properties, enzyme activities, and microbial community diversity and composition in mono- and inter-cropping under conventional and no-tillage conditions. We initiated a field experiment in Wuwei, a typical arid area of China, in 2014. Soil was sampled in August 2022 and, yields, soil properties, enzyme activities, and the microbial community diversity and composition were determined in the maize and pea strips in inter- and mono-cropping systems. Results revealed that the maize and pea strips in the no-till intercropping significantly increased yields, total and organic carbon stocks, decreased NO3−-N, and obtained the highest total and organic P in the soil. No-tillage significantly enhanced the Shannon index and Pielou evenness of the bacterial community and total microbial community over conventional tillage, with the α-diversity of the bacterial community and total microbial community distinctly higher in the NTIM treatment than in the CTIM treatment. The α-diversity of the total microbial community was significantly related to yield, soil IC and OC, and the α-diversity of the archaea community was significantly related to soil TC, TC/TP, TN/TP, and BX. Meanwhile, the α-diversity of the eukaryote community was significantly related to soil yield, soil TC/TP. Both no-tillage and intercropped maize significantly increased the abundance of archaea phylum Thaumarchaeota and bacterial phylum Nitrospirae, and were significantly positively associated with soil OC and NH4+-N, benefiting nitrogen fixation of intercropped pea from the atmosphere under the no-tillage cereal/legume intercropping. No-till intercropping was conducive to the accumulation of organic carbon, while decreasing the abundance of Proteobacteria, Acidobacteria, and Verrucomicrobia. Limited soil enzyme activities (ACP, ALP, DP, NAG, BG, AG, CB) led to decreases in organic carbon turnover and utilization. Intercropping altered soil microbial community diversity and composition due to changes in soil properties and enzyme activities. These findings suggest that no-tilled cereal–legume intercropping is a sustainable cropping practice for improving soil properties and enhancing microbial (archaea, bacterial, eukaryota) diversity, but the persistence is not conducive to rapid turnover of soil nutrients due to limited enzyme activities

Photophysiological Mechanism of Dense Planting to Increase the Grain Yield of Intercropped Maize with Nitrogen-Reduction Application in Arid Conditions

Leaf photophysiological characteristics are the main indexes that determine crop yield formation. However, it remains unclear whether photosynthesis is systematically regulated via the cropping pattern and nitrogen supply when maize crops are planted with a high density. So, a field experiment that had a three-factor split-plot arrangement of treatments was conducted from 2020 to 2021. The main plot was two cropping patterns that included the sole cropping of maize and wheat–maize intercropping. The split plot had two nitrogen application rates: a traditional nitrogen application rate (N2, 360 kg ha−1) and one reduced by 25% (N1, 270 kg ha−1) for maize. The split–split plot had three planting densities: a traditional density (M1, 78,000 plant ha−1), a medium density (M2, 10,400 plant ha−1), and a high density (M3, 129,000 plant ha−1) for sole maize; the corresponding densities of intercropped maize were 45,000, 60,000, and 75,000 plant ha−1, respectively. The grain yield, the photosynthetic traits, and chlorophyll a fluorescence of the maize were assessed. The results showed that a 25% nitrogen reduction and dense planting had a negative impact on the individual maize’s photosynthesis. However, intercropping could alleviate these drawbacks. When the maize was grown in the intercropping system at a lower nitrogen level and a medium planting density (IN1M2), the photosynthetic traits were better or similar to those of the traditional treatment (SN2M1) at the reproductive growth stage. Moreover, IN1M2 improved the light energy distribution among photochemistry, photo-protective and heat dissipation process of maize compared with SN2M1. A grey relation analysis demonstrated that the Pn and Tr of the individual maize played the most significant role in the group’s productivity. Thus, the IN1M2 treatment achieved the highest grain yield and can be recommended as a feasible agronomic practice in oasis-irrigated regions

Decoupled Adaptive Motion Control for Unmanned Tracked Vehicles in the Leader-Following Task

As a specific task for unmanned tracked vehicles, leader-following imposes high-precision requirements on the vehicle’s motion control, especially the steering control. However, due to characteristics such as the frequent changes in off-road terrain and steering resistance coefficients, controlling tracked vehicles poses significant challenges, making it difficult to achieve stable and precise leader-following. This paper decouples the leader-following control into speed and curvature control to address such issues. It utilizes model reference adaptive control to establish reference models for the speed and curvature subsystems and designs corresponding parameter adaptive control laws. This control method enables the actual vehicle speed and curvature to effectively track the response of the reference model, thereby addressing the impact of frequent changes in the steering resistance coefficient. Furthermore, this paper demonstrates significant improvements in leader-following performance through a series of simulations and experiments. Compared with the traditional PID control method, the results shows that the maximum following distance has been reduced by at least approximately 12% (ensuring the ability to keep up with the leader), the braking distance has effectively decreased by 22% (ensuring a safe distance in an emergency braking scenario and improving energy recovery), the curvature tracking accuracy has improved by at least 11% (improving steering performance), and the speed tracking accuracy has increased by at least 3.5% (improving following performance)