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Predictive weather-based models are widely used to schedule irrigation through the estimation of crop evapotranspiration. However, perceiving real-time crop water requirements remains a challenge. This research aims at field validating and exploiting a low-cost IoT soil moisture tensiometer prototype to consequently compare weather-based irrigation to soil water moisture-based irrigation in terms of yield and crop water productivity. The prototype is based on the ESP32 microcontroller and BMP180 barometric sensor. When compared to a mechanical tensiometer, the IoT prototype proved its accuracy, registering an average R2 equal to 0.8 and an RMSE range of 4.25–7.1 kPa. In a second step, the irrigation of a Romaine lettuce field (Lactuca sativa L.) cultivated under a drip system was managed according to two different scenarios: (1) using the data feed from the IoT tensiometers, irrigation was performed to keep the soil water potential between −15 and −25 kPa; (2) using the data provided by the in-situ weather station to estimate the crop water requirements. When comparing the yield, no significant difference was registered between the two scenarios. However, the water productivity was significantly higher, registering a 36.44% increment in scenario 1. The experiment highlights the water-saving potential achievable through real-time monitoring of soil moisture conditions. Since it is a low-cost device (82.20 USD), the introduced prototype facilitates deploying and managing a fleet of sensors for soil water potential live mapping.

High-pressure sodium (HPS) lighting is increasingly replaced by LED lighting in lettuce greenhouse cultivation. In contrast to HPS lighting, LEDs do not heat radiation. Therefore, the leaf temperature is significantly lower under LEDs. This raises the question of whether LED lighting has a positive impact on the reduction in water consumption during lettuce production. In this paper, we investigated this question and found that the water consumption of lettuce produced under LEDs was significantly lower (−15%) than under HPS without loss of yield. We also found that supplementary lighting increases the concentrations of caffeoylquinic acid, dicaffeoyltartaric acid, dicaffeoylquinic acid and that of the total phenolic compounds in lettuce leaves by 61%, 39%, 163% and 38%, respectively. Only the LED fixture was also efficient enough to increase the concentration of caffeoyltartaric acid (+24%). Most of the phenolic compounds showed a very strong positive correlation with the chlorophyll concentration in lettuce, which predominated in the leaves exposed to the LED lighting. Based on these facts, we conclude that by optimizing the light composition, more sustainable plant production, higher concentrations of chlorophyll and some phenolic compounds are possible.

Bacterial wilt is a destructive soilborne disease caused by Ralstonia solanacearum, posing a severe threat to plants in the Solanaceae family. It impacts on tobacco productivity worldwide. This study was conducted to analyze the changes in the soil’s physical and chemical properties, the number of microbes, and the bacterial diversity of the rhizosphere soil before and after the wilt disease. The rhizosphere soil of healthy and diseased tobacco plants was collected from Pucheng, Nanping, Fujian Province, Southern China. The results revealed significant differences in the trends of physical and chemical properties of the soil of healthy and diseased plants. The soil pH, available potassium (K), available phosphorous (P), and organic matter contents (SOM) were lower in the rhizosphere soil for healthy plants than for pre-diseased plants (HW). Only the available P, among all physical and chemical properties in the rhizosphere of diseased plants (HS), was significantly lower than those for pre-diseased plants (HW), changing from 149.59 mg/kg to 59.19 mg/kg. The order of numbers of the three main microbes in the rhizosphere soil for healthy plants (HC) and pre-diseased plants was the following: bacteria > actinomycetes > fungi. The number of actinomycetes in the soil of the diseased tobacco plants increased significantly. A comparison of the rhizosphere soil of diseased and healthy tobacco plants showed that the relative abundance of the bacterial community in the rhizosphere soil of the pathogenic tobacco plants changed significantly. The community diversity was increased, and the Pseudomonadaceae, to which the bacterial pathogen of bacterial wilt belonged, rose to a certain extent. Both pre-diseased and healthy plants showed changes in the physical and chemical properties, microbial quantity, and microbial diversity, thus proving that tobacco disease was closely related to the soil’s ecological environment.

Sichuan, a hilly area in southwestern China, is recommended as a bioethanol production base because of its abundant crop residue resources. However, removing the crop straw for bioethanol may negatively affect soil fertility and productivity due to the local purple soil vulnerability. To explore the impact of crop residue removal on soil fertility and productivity and meet the needs of sustainable agriculture, we conducted a crop residue removal experiment by measuring the soil organic carbon (SOC), total nitrogen (TN), and total phosphorus (TP) contents, and crop yield in the purple soil region in southwest China. Soil erosion was also simulated by Revised Universal Soil Loss Equation version 2 (RUSLE 2). The results showed that soil erosion increased with the increase of the straw removal rate. Compared with 0% removal treatment, the SOC content reduced at other removal rate treatments, especially for long-term residue removal. The effect of residue removal on soil TN and TP was not consistent within one year. After two years, residue removal greater than 25% caused a decrease in TN by 1.6–3.7%, and straw removal greater than 50% caused a TP decrease by 8.5–9.3%. More than 25% of the residue removed reduced maize and canola yields, and TN and TP content. However, all crop residue removal treatments resulted in SOC content reduction and soil erosion deterioration. In conclusion, crop residue removal was not recommended due to agricultural sustainability in Sichuan, China.

Apricot fruits are a favorite for consumption; however, their kernels are a rich source of nutritionally interesting substances, too. Nevertheless, in processing of apricots, the kernels remain often unused. In this study, 32 cultivars of different origin were analyzed for their protein content and content of secondary metabolites (phenolics and flavonoids). The weight and taste of kernels were assessed and these data were summarized for an evaluation of the attractiveness of the studied apricot kernels. Results showed that the protein content of kernels ranged from 14.56% to 28.77% and did not depend on the origin or weight of kernel, or taste. In addition, total phenolic (63.5–1277.3 mg GAE/100 g DW) and total flavonoid (0–153.1 mg CE/100 g DW) contents and antioxidant capacity (483.4–2348.4 mg TE/100 g DW) were measured in kernels. In conclusion, the Czech hybrids LE-5959, LE-5500 and French cultivar Koolgat are prospective for kernel processing and consumption because of their high protein content and sweet taste. Hybrid LI-3-6, originating in China, showed high protein content as well but because of bitter taste could be useful rather in medicine.

Vegetation plays an important role in atmospheric, hydrologic and biochemical cycles and is an important indicator of the impact of climate and human factors on the environment. In this paper, a method, which combines the empirical orthogonal function (EOF) and temporal unmixing analysis (TUA) methods, is applied to monitor the phenological characteristcs and spatial distribution of vegetation phenology in the middle part of the Huai River region. Based on the variance and EOF curves, the EOF provides the number of phenology modes, information which is the basis for an accurate temporal unmixing model. The TUA describes the temporal vegetation phenological details and spatial distribution. Importantly, this approach does not require assumptions, prior information or pre-defined thresholds. The vegetation phenology curves derived from the MODIS EVI data using the combined EOF and TUA methods display much more detail than the curves from Landsat TM using spectral mixture analysis (SMA). Additionally, the vegetation phenology spatial distribution from MODIS EVI is consistent with the field survey data. The combination method of EOF and TUA can be used to monitor vegetation phenology spatiotemporal change in a large area from time series of MODIS EVI data.

A family of Sigmoidal non-linear models is commonly used to determine the critical period of weed control (CPWC) and acceptable yield loss (AYL) in annual crops. We tried to prove another non-linear model to determine CPWC and AYL in a soybean agroforestry system with kayu putih. The three-year experiment (from 2019–2021) was conducted using a randomised complete block design factorial with five blocks as replications. The treatments comprised weedy and weed-free periods. Non-linear models comprised 45 functions. The results show that the Sigmoidal and Dose-Response Curve (DRC) families were the most suitable for estimating CPWC and AYL. The best fitted non-linear model for weedy and weed-free periods in the dry season used the Sigmoidal family consisting of the Weibull and Richards models, while in the wet season the best fit was obtained using the DRC and Sigmoidal families consisting of the DR-Hill and Richards models, respectively. The CPWC of soybean in the dry season for AYL was 5, 10, and 15%, beginning at 20, 22, and 24 days after emergence (DAE) and ended at 56, 54, and 52 DAE. The AYL in the wet season started at 20, 23, and 26 DAE and ended at 59, 53, and 49 DAE.

Mulch film contaminates the environment while increasing agricultural yield. As such, the factors that impact the residual film management capacity of farmers must be identified, which would also be helpful for the sustainable development and security of agriculture. We investigated 10 counties across nine cities (states) by surveying 1284 households. The improved entropy-weighted composite index and the grey correlation analysis model were employed to measure the residual film management capacity from five dimensions: human capital, use behavior, management skills, public policy, and willingness. The entropy-weighted composite index results showed that residual film being harmful to the environment had the highest overall rate of 0.0745, followed by the effectiveness of mulch film, which was 0.0715. The results of the grey correlation analysis model showed that willingness to use biodegradable mulch film had the highest correlation of 0.8960, followed by educational level of the laborers, which was 0.8824. We found that residual film being harmful to the environment, effectiveness of mulch film, willingness to use biodegradable mulch film, and educational level of the laborers were the major factors influencing the residual mulch film pollution knowledge of households. Based on this finding, the government should increase subsidies for promoting biodegradable mulch film and publicize the hazards of residual films using diversified means in multiple channels.