Hierarchically porous magnetic biochar as an amendment for wheat (Triticum aestivum L.) cultivation in alkaline Cd-contaminated soils: Impacts on plant growth, soil properties and microbiota.

Hierarchically porous magnetic biochar (HMB) had been found to act as an effective amendment to remediate cadmium (Cd) in water and soil in a previous study, but the effects on wheat growth, Cd uptake and translocation mechanisms, and soil microorganisms were unknown. Therefore, soil Cd form transformation, soil enzyme activity, soil microbial diversity, wheat Cd uptake and migration, and wheat growth were explored by adding different amounts of HMB to alkaline Cd-contaminated soil under pot experiments. The results showed that application of HMB (0.5 %-2.0 %) raised soil pH, electrical conductivity (EC) and available Fe concentration, decreased soil available Cd concentration (35.11 %-50.91 %), and promoted Cd conversion to less bioavailable Cd forms. HMB treatments could reduce Cd enrichment in wheat, inhibit Cd migration from root to stem, rachis to glume, glume to grain, and promote Cd migration from stem to leaf and stem to rachis. HMB (0.5 %-1.0 %) boosted antioxidant enzyme activity, reduced oxidative stress, and enhanced photosynthesis in wheat seedlings. Application of 1.0 % HMB increased wheat grain biomass by 40.32 %. Besides, the addition of HMB (0.5 %-1.0 %) could reduce soil Cd bioavailability, increase soil enzyme activity, and increase the abundance and diversity of soil bacteria. Higher soil EC brought forth by HMB (2.0 %) made the wheat plants and soil bacteria poisonous. This study suggests that applying the right amount of HMB to alkaline Cd-contaminated soil could be a potential remediation strategy to decrease Cd in plants' edible parts and enhance soil quality.

Competition for Light Interception in Different Plant Canopy Characteristics of Diverse Cotton Cultivars.

Identifying the ideal plant nature and canopy structure is of great importance for improving photosynthetic production and the potential action of plants. To address this challenge, an investigation was accomplished in 2018 and 2019 at the Institute of Cotton Research (ICR) of the Chinese Academy of Agricultural Science (CAAS), Henan Province, China. Six cotton varieties with diverse maturities and plant canopy structures were used to evaluate the light interception (LI) in cotton, the leaf area index (LAI), the biomass, and the yield throughout the two years of study. The light spatial distribution in the plant canopy was evaluated using a geographic statistical method, following the increasing quantity of radiation intercepted, which was determined using the rules of Simpson. Compared to the cotton plants with a compact structure, varieties with both a loose and tower design captured a comparatively higher amount of light (average 31.3%) and achieved a higher LAI (average 32.4%), eventually achieving a high yield (average 10.1%). Furthermore, the polynomial correlation revealed a positive relationship between the biomass accumulation in the reproductive parts and canopy-accrued light interception (LI), signifying that light interception is critical for the yield development of cotton. Furthermore, when the leaf area index (LAI) was peaked, radiation interception and biomass reached the highest during the boll-forming stage. These findings will provide guidance on the light distribution in cotton cultivars with an ideal plant structure for light capture development, providing an important foundation for researchers to better manage light and canopies.

Carbon footprint of cotton production in China: Composition, spatiotemporal changes and driving factors.

Analyzing the carbon footprint of crop production and proposing low-carbon emission reduction production strategies can help China develop sustainable agriculture under the goal of 'carbon peak and carbon neutrality'. Cotton is an economically important crop in China, but few reports have systematically quantified the carbon footprint of China's cotton production and analyzed its spatiotemporal changes and driving factors. This study used a life cycle approach to analyze the spatiotemporal changes and identify the main components and driving factors of the carbon footprint of cotton production in China between 2004 and 2018 based on statistical data. The results showed that the carbon footprint per unit area of cotton in Northwest China, the Yellow River Basin and the Yangtze River Basin reached 6220.13 kg CO2eq·ha-1, 3528.14 kg CO2eq·ha-1 and 2958.56 kg CO2eq·ha-1, respectively. From 2004 to 2018, the CFa in the Yellow River Basin and Northwest China increased annually, with average increases of 59.87 kg CO2eq·ha-1 and 260.70 kg CO2eq·ha-1, respectively, while the CFa in the Yangtze River Basin decreased by an average of 21.53 kg CO2eq·ha-1 per year. The ridge regression and Logarithmic Mean Divisia Index (LMDI) model showed that fertilizer, irrigation electricity and agricultural film were the main influences on carbon emission growth at the micro level and that the economic factor was the key factor at the macro level. Improving the efficiency of cotton fertilization and electricity use and ensuring the high-quality development of the cotton industry are effective strategies to reduce the carbon footprint of cotton cultivation in the future. This study comprehensively uses statistical data and mathematical modeling to provide theoretical support for accounting and in-depth analysis of cotton carbon emissions. The results are valuable for policy making related to sustainable development and the low-carbon development of the Chinese cotton industry.

Boll characteristics and yield of cotton in relation to the canopy microclimate under varying plant densities in an arid area.

Planting density affects crop microclimate and intra-plant competition, playing an important role on yield formation and resource use, especially in areas where the cotton is grown at relatively high plant densities in Xinjiang, China. However, more studies are needed to examine how the change in planting density affects the microclimate factors such as the fraction of light intercepted (FLI), air temperature(T) and relative humidity (RH) within different canopy layers, which in turn affect the boll number per plant (BNF), boll number per unit area (BNA), boll weight (BW), and boll-setting rate (BSR) at fruiting branch (FB) positions FB1-3, FB4-6, and FB≥7 in cotton. To quantify the relationships between boll characteristics, yield, and microclimate factors, we conducted a 2-year field experiment in 2019-2020 in Xinjiang with six plant densities: 9 (P1), 12 (P2), 15 (P3), 18 (P4), 21 (P5), and 24 (P6) plants m-2. With each three plants m-2 increase in density, the average FLI and RH across different canopy layers increased by 0.37 and 2.04%, respectively, whereas T decreased by 0.64 °C. The BNF at FB≥ 7, FB4-6, and FB1-3 decreased by 0.82, 0.33, and 0.5, respectively. The highest BNA was observed in the upper and middle layers in the P4 treatment and in the lowest canopy layer with the P5. The highest BW was measured in the middle canopy layer for P3, and the highest BSR was measured in the lower layer for P3. Plant density exhibited linear or quadratic relationships with FLI, T, and RH. Microclimate factors mainly affected the boll number in each layer, but had no significant effects on the BW in any layer or the BSR in the middle and lower layers. Cotton yield was non-linearly related to plant density. The 2-year maximum yield was achieved at a plant density of 21 plants m-2, but the yield increase compared to the yield with a density of 18 plants m-2was only 0.28%. Thus, we suggest that the optimal plant density for drip-irrigated cotton in Xinjiang is 18 plants m-2, which could help farmers grow machine-harvested cotton.

How do cotton light interception and carbohydrate partitioning respond to cropping systems including monoculture, intercropping with wheat, and direct-seeding after wheat?

Different cotton (Gossypium hirsutum L.)-wheat (Triticum aestivum) planting patterns are widely applied in the Yellow River Valley of China, and crop yield mainly depends on light interception. However, little information is available on how cotton canopy light capturing and yield distribution are affected by planting patterns. Hence, field experiments were conducted in 2016 and 2017 to study the response of cotton canopy light interception, square and boll distribution, the leaf area index (LAI) and biomass accumulation to three planting patterns: a cotton monoculture (CM, planted on 15 May) system, a cotton/wheat relay intercropping (CWI, planted on 15 May) system, in which three rows of wheat rows were intercropped with one row of cotton, and a system in which cotton was directly seeded after wheat (CWD, planted on 15 June). The following results were obtained: 1) greater light capture capacity was observed for cotton plants in the CM and CWI compared with the CWD, and the light interception of the CM was 22.4% and 51.4% greater than that of the CWI and CWD, respectively, at 30 days after sowing (DAS) in 2016; 2) more bolls occurred at the first sympodial position (SP) than at other SPs for plants in the CM; 3) based on the LAI and biomass accumulation, the cotton growth rate was the greatest in CWD, followed by CM and CWI; and 4) the CM produced significantly greater yields than did the other two treatments because it yielded more bolls and greater boll weight. Information on the characteristics of cotton growth and development in response to different planting patterns would be helpful for understanding the response of cotton yields to planting patterns and would facilitate the improvement of cotton productivity.