Biodegradable film mulching is one of the most critical technologies to overcome the dual challenges of agricultural plastic pollution and greenhouse gas (GHG) emissions. However, it is often required to optimize the key film properties—color and thickness—on the agronomic productivity and net environmental footprint. This study aimed to identify the optimal mulching strategies for high crop yield with a better environment during peanut cultivation. A field experiment was conducted with nine treatments: a no-mulch control (CK), conventional 0.010 mm polyethylene films (black and white), and biodegradable films of two colors (black, BDM; white, WDM) and three thicknesses (0.008, 0.010, and 0.015 mm). A systematic measurement was carried out to determine the peanut yield, water use efficiency (WUE), as well as the fluxes of N₂O, CH₄, and CO₂ using the static chamber-gas chromatography. Global warming potential (GWP) and greenhouse gas intensity (GHGI) were also calculated to assess the overall climate impacts. Results demonstrated that all mulching treatments significantly improved the peanut yield and WUE, compared with the CK. All mulches were also significantly reduced the cumulative N₂O emissions, GWP, and GHGI (P<0.05). Concurrently. There was a weak CH₄ sink capacity in the soil. Furthermore, the agronomic and environmental performance of the biodegradable films was comparable to that of conventional films in the short term, indicating their viability as a sustainable alternative. Crucially, the color and thickness were identified as the key drivers of performance. In color, the white films produced an average yield 10.6% higher than black films of the same thickness (P<0.05). Thinner films also exhibited superior potential for GHG mitigation. The 0.008 mm white biodegradable film (WDM8) reduced the GWP by 41.8% and 24.4%, compared with its 0.010 and 0.015 mm counterparts, respectively (P<0.05). Similarly, the 0.008 mm black biodegradable film (BDM8) lowered GHGI by 7.4% and 19.1%, respectively, compared with the thicker black ones (P<0.05). Two context-dependent optimal strategies were proposed. First, the 0.008 mm white biodegradable film (WDM8) was recommended for the synergistic high yields and low carbon emissions, as it recorded the highest yield and one of the lowest GHGI values. Second, the 0.008 mm black biodegradable film (BDM8) exhibited no significant differences in yield and WUE from WDM8, thereby serving as another excellent mulching option for sustainable agricultural production.This finding can provide a scientific basis to optimize the targeted biodegradable film in climate-smart and sustainable agriculture.

The harmless treatment of livestock and poultry reduces the risk of water and soil pollution caused by untreated discard of sick and dead livestock. Chemical fertilizer increases crop yield, while its over-application will lead to serious problems such as agricultural non-point source pollution as well as land acidification and soil compaction. It is of great significance to explore the utilization potential of bio-organic matter originating from harmless treatment of livestock to improve the soil environment and enhance agricultural productivity. This study investigated the effects of different application rates of bio-organic matter (0, 1285, 1928, 2571 kg/hm²) and biochar addition (0, 10 000 kg/hm²) on soil properties and crop yield under 20% reduction of chemical nitrogen fertilizer. The results indicated that the application of bio-organic matter combined with biochar improved soil physical structure under fertilizer reduction by decreasing soil bulk density and increasing soil porosity and soil aggregate stability. Compared to that under CK, the soil bulk density was reduced by 1.42%-6.38%, and the soil porosity was increased by 1.17%-7.05%. Compared to conventional fertilization, applying bio-organic matter (1 928 kg/hm²) ensured sufficient soil nutrients for crop growth under 20% of fertilizer reduction. The soil fertility was further boosted by the addition of biochar. The alkaline nitrogen content peaked under BM3 with 42.08 mg/kg, and the total nitrogen content and soil organic matter content reached their peak values under NM4 treatment, which were 0.97 g/kg and 21.23 g/kg, respectively. The higher the amount of bio-organic matter applied, the higher the grain yield and crop water productivity. The yield gained with bio-organic matter application alone at the rate of 2571 kg/hm² under fertilizer reduction (NM4) was 7504 kg/hm², which can reach equal yield level with CK, while medium to high addition of bio-organic matter combining biochar (BM3 and BM4 treatments) produced higher grain yield than that under CK. The correlation analysis showed significant positive correlations between total nitrogen and maize yield and between soil organic matter and maize yield. Overall, under 20% fertilizer reduction, applying bio-organic matter at the rate of 1928 kg/hm² and combining biochar at the rate of 10 000 kg/hm² would be an economical plan to enhance soil physicochemical properties and ensure stable maize yield, and would also supply a scientific way to reuse bio-organic matter originating from harmless treatment of livestock carcasses.

Accurate information about phenological stages is essential for canola field management practices such as irrigation, fertilization, and harvesting. Previous studies in canola phenology monitoring focused mainly on the flowering stage, using its apparent structure features and colors. Additional phenological stages have been largely overlooked. The objective of this study was to improve a shape-model method (SMM) for extracting winter canola phenological stages from time-series top-of-canopy reflectance images collected by an unmanned aerial vehicle (UAV). The transformation equation of the SMM was refined to account for the multi-peak features of the temporal dynamics of three vegetation indices (VIs) (NDVI, EVI, and CI_red-edge). An experiment with various seeding scenarios was conducted, including four different seeding dates and three seeding densities. Three mathematical functions: asymmetric Gaussian function (AGF), Fourier function, and double logistic function, were employed to fit time-series vegetation indices to extract information about phenological stages. The refined SMM effectively estimated the phenological stages of canola, with a minimum root mean square error (RMSE) of 3.7 days for all phenological stages. The AGF function provided the best fitting performance, as it captured multiple peaks in the growth dynamics characteristics for all seeding date scenarios using four scaling parameters. For the three selected VIs, CI_red-edge achieved the greatest accuracy in estimating the phenological stage dates. This study demonstrates the high potential of the refined SMM for estimating winter canola phenology.