Aboveground biomass (AGB) is an essential assessment of plant development and guiding agricultural production management in the field. Therefore, efficient and accurate access to crop AGB information can provide a timely and precise yield estimation, which is strong evidence for securing food supply and trade. In this study, the spectral, texture, geometric, and frequency-domain variables were extracted through multispectral imagery of drones, and each variable importance for different dimensional parameter combinations was computed by three feature parameter selection methods. The selected variables from the different combinations were used to perform potato AGB estimation. The results showed that compared with no feature parameter selection, the accuracy and robustness of the AGB prediction models were significantly improved after parameter selection. The random forest based on out-of-bag (RF-OOB) method was proved to be the most effective feature selection method, and in combination with RF regression, the coefficient of determination (R²) of the AGB validation model could reach 0.90, with root mean square error (RMSE), mean absolute error (MAE), and normalized RMSE (nRMSE) of 71.68 g/m², 51.27 g/m², and 11.56%, respectively. Meanwhile, the regression models of the RF-OOB method provided a good solution to the problem that high AGB values were underestimated with the variables of four dimensions. Moreover, the precision of AGB estimates was improved as the dimensionality of parameters increased. This present work can contribute to a rapid, efficient, and non-destructive means of obtaining AGB information for crops as well as provide technical support for high-throughput plant phenotypes screening.

Background: The accurate estimation of potato yield at regional scales is crucial for food security, precision agriculture, and agricultural sustainable development. Methods: In this study, we developed a new method using multi-period relative vegetation indices (rVIs) and relative leaf area index (rLAI) data to improve the accuracy of potato yield estimation based on the weighted growth stage. Two experiments of field and greenhouse (water and nitrogen fertilizer experiments) in 2018 were performed to obtain the spectra and LAI data of the whole growth stage of potato. Then the weighted growth stage was determined by three weighting methods (improved analytic hierarchy process method, IAHP; entropy weight method, EW; and optimal combination weighting method, OCW) and the Slogistic model. A comparison of the estimation performance of rVI-based and rLAI-based models with a single and weighted stage was completed. Results: The results showed that among the six test rVIs, the relative red edge chlorophyll index (rCI_{red edge}) was the optimal index of the single-stage estimation models with the correlation with potato yield. The most suitable single stage for potato yield estimation was the tuber expansion stage. For weighted growth stage models, the OCW-LAI model was determined as the best one to accurately predict the potato yield with an adjusted R² value of 0.8333, and the estimation error about 8%. Conclusion: This study emphasizes the importance of inconsistent contributions of multi-period or different types of data to the results when they are used together, and the weights need to be considered.

Leaf area index (LAI) is one of the important variables for crop growth monitoring and yield estimating. In this article, the potato LAI was retrieved by several vegetation indices (VIs) and spectral parameters of the continuum removal method (SPCRM) to provide accurate estimates. A comparison of the two methods of retrieving precision was completed. The data source for computing VIs and SPCRM was hyperspectral reflectance data for the life cycle, derived from two potato cultivars, Favorite (early maturing variety) and Yanshu 4 (late maturing variety), through field experiments. Sensitive bands were identified to indicate seven VIs by correlation analysis. Additionally, seven SPCRM were computed. Based on these methods, the potato LAI was retrieved and tested. Meanwhile, a comparison of the retrieving precision was implemented. The results showed that compared with the filtered spectral reflectance and VIs, the correlation between the potato LAI and the continuum removal spectral reflectance and its retrieved SPCRM were higher. The determination coefficients (R²) of the retrieving models of four parameters, the total area (S), the left area (Sl), the right area (Sr) and the depth area ratio (W), derived from the continuum removal method were all above 0.801, and their fitting coefficients (r) were all above 0.868, with the mean relative errors (MRE) all <0.14. It was identified that W was the most suitable parameter for retrieving the potato LAI. Although the effectiveness of SPCRM requires further research, this study manifests that SPCRM have the potential to accurately retrieve the potato LAI.

Potato is the fourth staple crop in China after wheat, maize and rice. The agro-pastoral ecotone (APE) in North China is a main region for potato production. However, potato yield has been seriously constrained by water shortages because of low precipitation and highly variable precipitation patterns during the growing season in this area. In this study, the Agricultural Production Systems Simulator (APSIM) model was used to simulate potato water-limited yield and historical years were divided into different water-temperature year types to optimize the optimal planting period (OPP) and cultivar of potato. The results showed that the potato yield varied in different water-temperature year types. Fast-developing cultivar Favorita could obtain the highest yield in most places and water-temperature year types due to its relatively short length of tuber formation stage. In this study, we suggest changing the planting date according to the water-temperature year type, which offers a new way to adapt to a highly variable climate. However, our method should be adopted carefully because we only considered climate factors; other agronomic management practices (adjusting planting density, plastic film mulch, conservation tillage etc.) also have a great effect on planting date and cultivar selection, which should be further investigated in the future.

Differentiated nitrogen (N) application styles and dosages can directly affect crop yield and planting costs. To maximize the efficiency of N fertilizer utilization and solve the problem of increasing production without increasing income, the Decision Support Systems for Agrotechnology Transfer (DSSAT) model was used in this study to carry out experiments simulating different N application styles and dosages for potatoes at 15 meteorological observation stations in the northeast region of China during 2013–2017. The present study aims to analyze the relationship among yield, cost, and net gains under different N fertilizer management scenarios by combining the cost-benefit method. The main conclusions are as follows: (1) the staged and incremental application of N fertilizer during the potato growth period can promote a yield increase. The lowest yield was achieved by a one-time basal application of N fertilizer at sowing, and the highest yield was observed when N fertilizer was applied in a 1:1 ratio at sowing and the end of tuber formation, respectively. (2) The range for N fertilizer application to achieve the highest potato yield at each station is 275–330 kg·ha⁻¹. (3) The highest potato yield under sufficient N application treatments did not correspond to the highest net gains. Potato net gains at all stations showed an increasing-then-decreasing trend with the increase in N application. The highest gains were obtained when 220 kg·ha⁻¹, 220 kg·ha⁻¹, and 275 kg·ha⁻¹ of N fertilizer were applied in a 1:1 ratio as a basal fertilizer and supplementary fertilizer at sowing, and the end of tuber formation in Heilongjiang, Jilin, and Liaoning provinces, respectively. This study provides a reference program for potato planting with the highest benefits for regional governments, farmers, and enterprises and aims to improve the status quo of potato planting, increasing production without increasing income.