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Climate change, environmental degradation, and limited resources are motivations for sustainable forest management. Forests, the most abundant renewable resource on earth, used to make a wide variety of forest-based products for human consumption. To provide a scientific measure of a product’s sustainability and environmental performance, the life cycle assessment (LCA) method is used. This article provides a comprehensive review of environmental performances of forest-based products including traditional building products, emerging (mass-timber) building products and nanomaterials using attributional LCA. Across the supply chain, the product manufacturing life-cycle stage tends to have the largest environmental impacts. However, forest management activities and logistics tend to have the greatest economic impact. In addition, environmental trade-offs exist when regulating emissions as indicated by the latest traditional wood building product LCAs. Interpretation of these LCA results can guide new product development using biomaterials, future (mass) building systems and policy-making on mitigating climate change. Key challenges include handling of uncertainties in the supply chain and complex interactions of environment, material conversion, resource use for product production and quantifying the emissions released.

To meet the increasing demands of precision agricultural and environmental management, more abundant and accurate information is needed to describe soil organic carbon (SOC) vertical variation. Based on 923 soil profiles (collected at the depths of 0–15, 15–30, 30–60, 60–90, 90–120, and 120–150 cm) in the central area of Changhua County, Taiwan, the distribution curve of the SOC content of each profile was fitted by the equal-area spline model, and it was possible to obtain the SOC content at all depths. Taking the 0–5 cm (L1), 5–10 cm (L2), and 10–15 cm (L3) sub-layers as examples, their SOC contents and stocks were compared to the mean values of the average 5-cm-thick sub-layers (Lm) derived from the value of the 0–15 cm layer. The results indicated that the SOC contents and stocks both reduced with increasing soil depths. The mean SOC contents of L1, L2, and L3 were 22.1, 21.0, and 18.7 g·kg−1, respectively, with significant variation, and the values of L2 and L3 were 5.0% and 15.4% lower than that of L1. Similarly, the mean SOC stocks were 1.29, 1.25, and 1.16 kg·m−2 of the L1, L2, and L3 layers, also with significant variation, and the values of L2 and L3 were 4.0% and 10.1% lower than that of L1. Meanwhile, it was found that the SOC content and stock of Lm were both close to the corresponding values in L2, but were significantly different to that of L1 and L3. Furthermore, the interpolation contours of the SOC contents and stocks in L1, L2, and L3 by digital soil mapping also presented regular variation with increasing soil depths, while the contours of Lm had nearly identical patterns to that of L2. The results demonstrate that the typically used mean SOC contents with certain thicknesses calculated from the sampling layer can only approximately inflect the SOC situation at intermediate depths, but the SOC content in the upper and lower parts within the sampling layer varies greatly. Therefore, the actual distribution of SOC varies gradually depending on the soil depth. This study indicates that the combination of the equal-area spline model and digital soil mapping can greatly enrich the current soil SOC database and provide more abundant and accurate SOC content and stock information for precision agricultural and environmental management based on legacy soil database.

In this study, we aimed to determine the effects of tillage systems (TS) on wheat grain yield, yield components, and physicochemical properties under rain-fed conditions over 2 years (2018–2019) in the Kuhdasht region in southwestern Iran. The tillage treatments were a combination of conventional tillage (CT), reduced tillage (RT), and no tillage (NT) systems based on randomized complete blocks with three replications. Wheat grain yields and yield components, physical properties (geometrical dimensions, sphericity, thousand-grain mass (TGM), water absorption (WA), volume (V), and density), chemical composition (protein content (PC), water content (WC), fiber content (FC), ash content (AC)), surface color parameters (L*, a*, and b*), and correlations among measured parameters were assessed. The results showed that wheat grain yield and yield components were significantly affected by TS (p < 0.01). The wheat grain yield was higher under CT (2.72 t ha−1), with a significant difference between RT (1.76 t ha−1) and NT (1.20 t ha−1). The highest TGM (49.19 g) was achieved under CT, followed by RT (43.41 g), with the lowest (39.17 g) in the NT system. TS had significant effects on certain physical properties of wheat grains (p < 0.01). Wheat grain, size, shape, and mass values were higher under CT than RT and NT systems in all experimental years. CT resulted in the highest WA, while the lowest resulted from NT. TS had no significant influence on the density of the grains. TS had significant effects on PC, AC, and WC values of grains at the 1% probability level and on FC at the 5% level. Higher PC values were obtained under the CT system (13.07%, against 11.90% and 10.67% for RT and NT, respectively) in all growing seasons. Additionally, the AC was significantly lower in the sample grains under RT (2.38%) and NT (2.43%) than in those from CT (2.56). The FC was higher under NT (15.73%) than RT (13.73) and CT (13.71%). The grain WC was significantly higher under NT (7.57%) than RT (6.79%) and CT (7.0%). The TS significantly affected the surface color parameters of grains, while the L* (lightness) and b* (yellowness) values of grains under CT were higher than in RT and NT.

Lolium rigidum Gaud. is a cross-pollinated species characterized by high genetic diversity and it was detected as one of the most herbicide resistance-prone weeds, globally. Acetohydroxyacid synthase (AHAS) resistant populations cause significant problems in cereal production; therefore, monitoring the development of AHAS resistance is widely recommended. Using next-generation sequencing (NGS), a de novo transcriptome sequencing dataset was presented to identify the complete open reading frame (ORF) of AHAS enzyme in L. rigidum and design markers to amplify fragments consisting of all of the eight resistance-conferring amino acid mutation sites. Pro197Thr, Pro197Ala, Pro197Ser, Pro197Gln, and Trp574Leu amino acid substitutions have been observed in samples. Although the Pro197Thr amino acid substitution was already described in SU and IMI resistant populations, this is the first report to reveal that the Pro197Thr in AHAS enzyme confers a high level of resistance (ED50 3.569) to pyroxsulam herbicide (Triazolopyrimidine).

With the emergence of machine learning methods during the past decade, alternatives to conventional geostatistical methods for soil mapping are becoming increasingly more sophisticated. To provide a complete overview of their performance, this study performed cost–benefit analysis of four soil mapping methods based on five criteria: accuracy, processing time, robustness, scalability and applicability. The evaluated methods were ordinary kriging (OK), regression kriging (RK), random forest (RF) and ensemble machine learning (EML) for the prediction of total soil carbon and nitrogen. The results of these mechanisms were objectively standardized using the linear scaling method, and their relative importance was quantified using the analytic hierarchy process (AHP). EML resulted in the highest cost–benefit score of the tested methods, with maximum values of accuracy, robustness and scalability, achieving a 55.6% higher score than the second-ranked RF method. The two geostatistical methods ranked last in the cost–benefit analysis. Despite that, OK could retain its place as the most frequent method for soil mapping in recent studies due to its widespread, user-friendly implementation in GIS software and its univariate character. Further improvement of machine learning methods with regards to computational efficiency could additionally improve their cost–benefit advantage and establish them as the universal standard for soil mapping.

Saffron, also known as “the golden spice”, is one of the most expensive crops in the world. The expensiveness of saffron comes from its rarity, the tedious harvesting process, and its nutritional and medicinal value. Different countries of the world are making great economic growth due to saffron export. In India, it is cultivated mostly in regions of Kashmir owing to its climate and soil composition. The economic value generated by saffron export can be increased manyfold by studying the agronomical factors of saffron and developing a model for artificial cultivation of saffron in any season and anywhere by monitoring and controlling the conditions of its growth. This paper presents a detailed study of all the agronomical variables of saffron that have a direct or indirect impact on its growth. It was found that, out of all the agronomical variables, the important ones having an impact on growth include corm size, temperature, water availability, and minerals. It was also observed that the use of IoT for the sustainable cultivation of saffron in smart cities has been discussed only by very few research papers. An IoT-based framework has also been proposed, which can be used for controlling and monitoring all the important growth parameters of saffron for its cultivation.

Soil salinity and the use of saline groundwater are two major constraints in crop production, which covers a ~1.0 billion ha area of arid and semi-arid regions. The improved drainage function of soil can modify the salty growing environment for higher agricultural production. The present study evaluated the effectiveness of cut-soiler-constructed rice residue-filled preferential shallow subsurface drainage (PSSD) to improve the drainage function and its effect on the yield, quality and plant–water relations of mustard over 2019–2021. Cut-soiler-simulated drains were made in a semi-controlled lysimeter (2 × 2 × 3; L*W*H m) as the main plot treatment in a double replicated split–split experiment with two soil types (subplot) and three irrigation water salinities (4, 8 and 12 dS m−1) as the sub-sub-plot treatment. The drainage volume of variable salinity (EC), dependent on the total water input, was substantially higher in the rainy season (April to October), i.e., 16.6, 7.76 and 12.0% during 2018, 2019 and 2020, with 1.7, 0.32 and 0.77 kg salt removal per lysimeter, compared to the post-rainy season. The mustard seed, straw and biological yields were improved by 31.4, 14.41 and 18.08%, respectively, due to a positive effect on plant–water relations. The mustard seeds produced in the cut-soiler-treated plots recorded higher oil, crude fiber and protein contents and a lower erucic acid content. The increase in salt load, by higher-salinity irrigation water, was also efficiently managed by using cut-soiler PSSD. It was found that the saline irrigation water up to 12.0 dS m−1 can be used under such PSSD without any extra salt loading. The present study showed the potential of cut-soiler PSSD in root zone salinity management by improving drainage in salt-affected arid regions.

Remote sensing is an efficient method of monitoring experiments rapidly and by enabling the collection of significantly more detailed data, than using only field measurements, ensuring new possibilities in scientific research. A small plot field experiment was conducted in a randomized block design with winter oat (Avena sativa L.) varieties in Debrecen, Hungary in the 2020/2021 cropping year. Multiple field measurements and aerial surveys were carried out examining the response of oat on Silicon and Sulfur foliar fertilization treatments thereby monitoring their effects on the physiology, production and stress tolerance. Parallel application of in situ (elevation, soil pH, NDVI, SPAD, chlorophyll content) and aerial (NDVI, NDRE) surveys including unmanned aerial vehicles (UAVs) provided a diverse source of data for evaluation. Both the oat varieties (88.9%) and the foliar fertilization treatments (87.5%) were correctly classified and clearly separated with the discriminant analysis based on measured data. The Pearson correlation analysis showed a very strong positive connection (r = 0.895–1.00) between the NDVI values measured using a hand-held system and UAV-installed camera, except the third measurement time, where the correlation was weaker (r = 0.70). Our results indicate that field experiments can be effectively supported by UAVs.