Cropland soil organic carbon (SOC) is crucial for global food security and mitigating the greenhouse effect. Accurate SOC prediction using hyperspectral data is essential for dynamic monitoring of soil carbon pools in croplands. However, effective methods to reduce hyperspectral data dimensionality and integrate it with suitable regression algorithms for reliable prediction models are poorly understood. In this study, we analyzed 108 soil samples from Changting County, Fujian Province, China. Our objective was to evaluate the performance of various combinations of six feature selection methods and four regression algorithms for SOC prediction. Our findings are as follows: the combination of the Successive Projections Algorithm (SPA) and Partial Least Squares (PLS) yielded the most favorable results, with R² (0.61), RMSE (1.77 g/kg), and MAE (1.48 g/kg). Moreover, we determined the relative importance of variables, with the following ranking: 696 nm > 892 nm > 783 nm > 1641 nm > 1436 nm > 396 nm > 392 nm > 2239 nm > 2129 nm. Notably, 696 nm exhibited the highest importance in the SPA-PLS model, with the Variable Importance in Projection (VIP) value of 1.22. This study provides profound insights into feature selection methods and regression algorithms for SOC prediction, highlighting the superiority of SPA-PLS as the optimal combination.

Estimating the available potassium (AK) in soil can help improve field management and crop production. Fourier-transform infrared (FTIR) spectroscopy is one of the most promising techniques for the fast and real-time analysis of soil AK content. However, the successful estimation of soil AK content by FTIR depends on the proper selection of appropriate spectral dimensionality reduction techniques. To magnify the subtle spectral signals concerning AK content and improve the understanding of the characteristic FTIR wavelengths of AK content, a total of 145 soil samples were collected in an agricultural site located in the southwest part of Sichuan, China, and three typical spectral dimensionality reduction methods—the successive projections algorithm (SPA), simulated annealing algorithm (SA) and competitive adaptive reweighted sampling (CARS)—were adopted to select the appropriate spectral variable. Then, partial least squares regression (PLSR) was utilized to establish AK inversion models by incorporating the optimal set of spectral variables extracted by different dimensionality reduction algorithms. The accuracy of each inversion model was tested based on the coefficient of determination (R²), root mean square error (RMSE) and mean absolute value error (MAE), and the contribution of the inversion model variables was explored. The results show that: (1) The application of spectral dimensionality reduction is a useful technique for isolating specific components of multicomponent spectra, and as such is a powerful tool to improve and expand the predicted potential of the spectroscopy of soil AK content. Compared with the SA and CARS algorithms, the SPA was more suitable for soil AK content inversion. (2) The inversion model results showed that the characteristic wavelengths were mainly around 777 nm, 1315 nm, 1375 nm, 1635 nm, 1730 nm and 3568–3990 nm. (3) Comparing the performances of different inversion models, the SPA–PLSR model (R²= 0.49, RMSE = 22.80, MAE = 16.82) was superior to the SA–PLSR and CARS–PLSR models, which has certain guiding significance for the rapid detection of soil AK content.

The carbon balance of the agricultural ecosystem is of great significance for the assessment of the source and sink of the terrestrial ecosystem. In view of the particularity of the growth and management of flue-cured tobacco, the carbon budget of the field-tobacco ecosystem was studied in order to provide the basis for the improvement of tobacco field management and the estimation of agricultural carbon sink. In this study, the carbon budget was observed with single application of chemical fertilizer using the long-term positioning test station for three years from 2015 to 2017. The results showed that the average cumulative biomass of flue-cured tobacco, the average carbon content of tobacco plant, and the cumulative carbon sequestration were (5832.10 ± 537.32) kg hm^-2, (42.14 ± 0.05)%, and (2459.25 ± 233.78) kg hm^-2, respectively. The ratio of root carbon to plant carbon was higher, with an average of 24.94%. The wet deposition carbon and the dry deposition carbon were 115.32 kg hm^-2 and 6.54 kg hm^-2 respectively, both accounting for 20.01% of root carbon amount. The total carbon output in growing season was 2464.98 kg hm^-2, in which the CO₂ emission expenditure accounts for 98.99%, the runoff carbon expenditure 0.76%, and the leaching carbon accounts for 0.25%. The flue-cured tobacco ecosystem was a weak carbon sink to the atmosphere, with a carbon sink of 116.13 kg hm^-2. The total amount of carbon sequestration in flue-cured tobacco was relatively lower, whereas its root system had a relatively higher contribution to soil carbon.

Introduction: Sustainable food production in fragile karst landscapes requires moving beyond input-intensive agriculture. Methods: This study investigated how long-term organic amendments affected maize yield, using a 15-year field trial on karst yellow soil. Integrating soil analysis, metagenomics, and causal modeling, revealed that adding farmyard manure or bio-organic fertilizer to mineral NPK increased yield by 12.08% and 11.48%, respectively, and improved key soil properties, most notably available phosphorus. Results: Organic inputs shifted the soil microbiome toward copiotrophic taxa and enriched genes for organic matter decomposition and phosphorus mobilization. However, statistical modeling revealed that these biological changes did not directly drive yield. Instead, the primary pathway was hierarchical: amendments first enhanced the soil’s chemical habitat, which then directly boosted crop growth while simultaneously shaping the microbial community and its functional potential. The interaction of soil, microbes, and genes together explained 81% of the yield variation. Discussion: Our findings demonstrate that in phosphorus-limited karst soils, organic amendments act foremost as soil conditioners. Microbial processes, though crucial, are secondary mediators that translate improved soil conditions into efficient nutrient cycling. Therefore, sustainable intensification in these vulnerable agroecosystems should prioritize managing soil health over directly targeting microbial processes.