The matching status of agricultural water and land resources is a prerequisite for grain production. The influence of gray water footprint has not been paid attention to in the study of agricultural water and land resources matching based on water footprint. To measure the matching status of agricultural water and land resources more comprehensively, the total water footprint (including blue, green and gray water footprint) and the cultivated land area was taken as the characterization parameters of water and land resources, respectively. The Gini coefficient model, and the agricultural water and land resources matching coefficient model were constructed to calculate the matching degree of agricultural water and land resources in a cold region (Heilongjiang Province) of China. Based on the amount of agricultural water consumption, the equivalent coefficient model was used to evaluate the degree of agricultural water and land resources shortage or to be developed. The result of agricultural water and land resources matching coefficient model showed that the matching degree of agricultural water and land resources in Heilongjiang Province is getting better year by year, which is consistent with the calculations determined from the Gini coefficient. The result of the equivalent coefficient method based on agricultural water consumption was consistent with the result of the Gini coefficient method based on total water footprint, which is verified that it is scientific and reasonable to take the total water footprint as the characterization parameter of water resource. The findings may provide implications for the spatial optimal allocation of regional agricultural water and land resources.

To evaluate the state of an agricultural development more comprehensively, a vulnerability assessment is introduced into agricultural water and land resources system, and it is expected that the vulnerability assessment can provide a basis for improving system structure and function and realizing sustainable development. In the study, 27 evaluation indicators are selected from the agricultural water and land resources system (AWLRS), socio-economic system and ecological structure system to construct the evaluation index system for agricultural water and land resource system vulnerability (AWLRSV). Seagull optimization algorithm (SOA) is used to calibrate the parameters of the random forest (RF) model. SOA-RF model is applied to measure the AWLRSV of Heilongjiang Province in China. The results show that the SOA-RF model has higher accuracy and stronger stability than the traditional RF model and DA-RF model. The value of AWLRSV in Heilongjiang Province presents a downward–upward–downward trend from 2008 to 2018. The vulnerability levels are mainly level II and III, and level III is mainly distributed northwest and southeast of Heilongjiang Province. The novelty of this paper is to regard the agricultural water and land resources system as a compound system, put forward the vulnerability assessment framework. The findings may provide reference for regional sustainable development from a new research perspective.

The stump and coarse root biomass remaining after tree harvesting are often overlooked by researchers, which may lead to underestimation of their role in carbon cycling, so we constructed two sets of additive models for larch (Larix olgensis Henry) plantations in Northeast China. Due to the absence of tree diameter at breast height data after harvesting, only the sole predictor variable stump disc diameter could be used to predict stump and coarse root biomass, and the results showed that stump disc diameter predicted stump biomass with higher accuracy than coarse root biomass predictions. In addition, to investigate the effect of the site class of complex stands on the predictive capability of the model, the generic model in this study was employed with all site class data and a specific model was developed and employed with all the site class data. We found that the generic model had different degrees of error compared to the predicted results for each site class, overestimating the total biomass by 15% and underestimating it by 10%, especially for site class IV. In conclusion, to obtain a biomass prediction model with reliable results, the impact of more complex site class effects should be considered.

Site class is a quantitative indicator used to evaluate site quality. It reflects site conditions, mainly climate, the suitability of soil for tree species and soil fertility. Despite the economic and ecological importance of tree competition and site class in sustainable forest management, there has been little research on its impact on the stump and coarse root biomass allocation within plantations. The stump and coarse roots were divided into five components ((stump disc (SD), stump knot (SK), coarse roots (>10 cm in diameter) (CR1), medium coarse roots (5–10 cm) (CR2) and fine coarse roots (2–5 cm) (CR3)), and the biomass of each component was obtained via the weighing method. It was found that the biomass of SD, CR1, CR2 and CR3 was mainly affected by competition (p ≤ 0.01). In the three site classes, the biomass of CR3 increased significantly with the increase in the competition index (CI) (p < 0.01); the biomass of CR1 decreased gradually. In site V, the biomass of SK, sapwood and heartwood increased significantly with the increase in CI. The results show that competition affects the allocation of stump and coarse root biomass mainly by changing the coarse root biomass. The development of stump knots is greatly influenced by site class. This study provides a reference for solving the competition mechanism underlying larch wood forest development, which will in turn promote more effective utilization of larch wood forests. This study also provides a scientific basis for accurately estimating the belowground biomass and carbon storage of artificial plantation forests.

Stumps and coarse roots form an important C pool and nutrient pool in a Larix olgensis (Larix olgensis Henry) plantation ecosystem, and their decomposition processes would affect nutrient cycling dynamics of the overall Larix olgensis plantation. We studied the decomposition and release of nutrients from stumps and coarse roots that were cleared 0, 6, 16, 26 and 33 years ago in Northeast China. The stumps and coarse roots were divided into stump discs (SD), stump knots (SK), coarse roots (>10 cm in diameter) (CR1), medium-coarse roots (5–10 cm in diameter) (CR2) and fine-coarse roots (2–5 cm in diameter) (CR3). During the entire 33-year study period, SK, CR1, CR2 and CR3 lost 87.37%, 96.24%, 75.76% and 91.98% of their initial mass, respectively. The average annual decomposition rate (k) was 0.068 for SD, 0.052 for SK, 0.092 for CR1, 0.068 for CR2 and 0.066 for CR3. After 33 years of decomposition, CR3 lost 5% of its initial C, CR2 lost 2%, and SK accumulated 1%, indicating slow C release. The N residues in SK, CR1, CR2 and CR3 were 186%, 109%, 158% and 170%, respectively. Coarse roots released P significantly faster than SD and SK, with 13% of the initial P released in CR1. SD and SK release cellulose, hemicellulose and lignin faster than coarse roots. The results show that Larix olgensis stumps and coarse roots could contribute to soil fertility recovery and serve as a long-term nutrient reservoir for forest vegetation.