Corrigendum: Retrieving rice (Oryza sativa L.) net photosynthetic rate from UAV multispectral images based on machine learning methods.

[This corrects the article DOI: 10.3389/fpls.2022.1088499.].

Response of soil nutrients retention and rice growth to biochar in straw returning paddy fields.

Applying straw to agricultural production to improve soil productivity and crop yields is significant. However, the straw-only application is possibly not a practical choice for achieving environmental protection and high yield. This study evaluated the applicability of straw combined with biochar to the paddy field. Two-year pot experiments were conducted to examine the effect of straw combined with different proportions (0, 5, 20, 40 t ha-1) of biochar on soil nitrogen retention, phosphorous availability, rice yield, and physiological parameters. Five treatments were included: control (CK), 7 t ha-1 straw + 0 t ha-1 biochar (ST), 7 t ha-1 straw + 5 t ha-1 biochar (SC1), 7 t ha-1 straw + 20 t ha-1 biochar (SC2), 7 t ha-1 straw + 40 t ha-1 biochar (SC3). The results indicated that the biochar had an encouraging effect on paddy fields with straw returning: (1) SC3 treatment significantly increased ammonium nitrogen (NH4+-N) and nitrate nitrogen (NO3--N) content in soils compared to ST, increasing by 30.19% and 42.72%, while SC2 treatment increased by 25.84% and 30.40%, respectively; (2) Regarding soil phosphorus availability, ST treatment showed a negative effect, while proper biochar application rate (20 t ha-1) effectively increased Olsen-P content (18.24%); (3) No significant difference among these treatments was observed in the photosynthetic characteristics. Notably, 20 t ha-1 biochar application (SC2) effectively enhanced rice components (stem, ear) dry biomass, improved rice yield (10.14%), and Harvest index (HI: 4.99%). Hence, the appropriate rate (20 t ha-1) of biochar combined with straw (7 t ha-1) returning is a promising strategy for increasing nitrogen retention and phosphorous availability, alleviating N and P losses and promoting rice growth and yield. These findings are expected to provide a new perspective in that straw-returning with biochar achieves high efficiency, ecological, and sustainable development of agriculture.

High-resolution crop yield and water productivity dataset generated using random forest and remote sensing.

Accurate and high-resolution crop yield and crop water productivity (CWP) datasets are required to understand and predict spatiotemporal variation in agricultural production capacity; however, datasets for maize and wheat, two key staple dryland crops in China, are currently lacking. In this study, we generated and evaluated a long-term data series, at 1-km resolution of crop yield and CWP for maize and wheat across China, based on the multiple remotely sensed indicators and random forest algorithm. Results showed that MOD16 products are an accurate alternative to eddy covariance flux tower data to describe crop evapotranspiration (maize and wheat RMSE: 4.42 and 3.81 mm/8d, respectively) and the proposed yield estimation model showed accuracy at local (maize and wheat rRMSE: 26.81 and 21.80%, respectively) and regional (maize and wheat rRMSE: 15.36 and 17.17%, respectively) scales. Our analyses, which showed spatiotemporal patterns of maize and wheat yields and CWP across China, can be used to optimize agricultural production strategies in the context of maintaining food security.

Retrieving rice (Oryza sativa L.) net photosynthetic rate from UAV multispectral images based on machine learning methods.

Photosynthesis is the key physiological activity in the process of crop growth and plays an irreplaceable role in carbon assimilation and yield formation. This study extracted rice (Oryza sativa L.) canopy reflectance based on the UAV multispectral images and analyzed the correlation between 25 vegetation indices (VIs), three textural indices (TIs), and net photosynthetic rate (Pn) at different growth stages. Linear regression (LR), support vector regression (SVR), gradient boosting decision tree (GBDT), random forest (RF), and multilayer perceptron neural network (MLP) models were employed for Pn estimation, and the modeling accuracy was compared under the input condition of VIs, VIs combined with TIs, and fusion of VIs and TIs with plant height (PH) and SPAD. The results showed that VIs and TIs generally had the relatively best correlation with Pn at the jointing-booting stage and the number of VIs with significant correlation (p< 0.05) was the largest. Therefore, the employed models could achieve the highest overall accuracy [coefficient of determination (R 2) of 0.383-0.938]. However, as the growth stage progressed, the correlation gradually weakened and resulted in accuracy decrease (R 2 of 0.258-0.928 and 0.125-0.863 at the heading-flowering and ripening stages, respectively). Among the tested models, GBDT and RF models could attain the best performance based on only VIs input (with R 2 ranging from 0.863 to 0.938 and from 0.815 to 0.872, respectively). Furthermore, the fusion input of VIs, TIs with PH, and SPAD could more effectively improve the model accuracy (R 2 increased by 0.049-0.249, 0.063-0.470, and 0.113-0.471, respectively, for three growth stages) compared with the input combination of VIs and TIs (R 2 increased by 0.015-0.090, 0.001-0.139, and 0.023-0.114). Therefore, the GBDT and RF model with fused input could be highly recommended for rice Pn estimation and the methods could also provide reference for Pn monitoring and further yield prediction at field scale.

Comparison of five Boosting-based models for estimating daily reference evapotranspiration with limited meteorological variables.

Accurate ET0 estimation is of great significance in effective agricultural water management and realizing future intelligent irrigation. This study compares the performance of five Boosting-based models, including Adaptive Boosting(ADA), Gradient Boosting Decision Tree(GBDT), Extreme Gradient Boosting(XGB), Light Gradient Boosting Decision Machine(LGB) and Gradient boosting with categorical features support(CAT), for estimating daily ET0 across 10 stations in the eastern monsoon zone of China. Six different input combinations and 10-fold cross validation method were considered for fully evaluating model accuracy and stability under the condition of limited meteorological variables input. Meanwhile, path analysis was used to analyze the effect of meteorological variables on daily ET0 and their contribution to the estimation results. The results indicated that CAT models could achieve the highest accuracy (with global average RMSE of 0.5667 mm d-1, MAE of 4199 mm d-1and Adj_R2 of 0.8514) and best stability regardless of input combination and stations. Among the inputted meteorological variables, solar radiation(Rs) offers the largest contribution (with average value of 0.7703) to the R2 value of the estimation results and its direct effect on ET0 increases (ranging 0.8654 to 0.9090) as the station's latitude goes down, while maximum temperature (Tmax) showes the contrary trend (ranging from 0.8598 to 0.5268). These results could help to optimize and simplify the variables contained in input combinations. The comparison between models based on the number of the day in a year (J) and extraterrestrial radiation (Ra) manifested that both J and Ra could improve the modeling accuracy and the improvement increased with the station's latitudes. However, models with J could achieve better accuracy than those with Ra. In conclusion, CAT models can be most recommended for estimating ET0 and input variable J can be promoted to improve model performance with limited meteorological variables in the eastern monsoon zone of China.