Estimating soil organic carbon of sown biodiverse permanent pastures in Portugal using near infrared spectral data and artificial neural networks

Designing an energy efficient, economically feasible, and environmentally robust integrated farming system model for sustainable food production in the Indian Himalayas

“…70 The economic returns had a positive correlation with energy productivity but a negative correlation with GWP and specific energy. 23 Growers' livelihoods are largely determined by the per day economic gain. In the present study, the per day economic gain was measured from economic efficiency to assess the economic viability and growing prosperity.…”

“…Out of this, the agriculture sector accounts for ∼11.1 Gt CO 2eq per y GHG emission. [23][24][25][26] With this background of the signicant impact of agricultural activities on climate change, countries participated at the Conference of Parties-26 (COP26) on 5th November 2021, as part of the discussion on agriculture and agreed on a transition toward climate-resilient sustainable food systems. It was recognized by the parties that climate-resilient agricultural practices would be crucial for ensuring food security and ending global hunger besides fullling COP26's commitment to minimizing 45% of emissions by 2030.…”

Study on soil cracks in pneumatic subsoiling based on LSTM

“…In order to analyse the influence of working air pressure, working depth and working speed on subsoiling resistance and subsoiling disturbance surface, PCA is used to reduce the dimensions of the three dimensions. PCA, as a classical linear transformation formula (You & Cai, 2009), can effectively analyse the contribution rate of its three dimensions (Morais et al, 2021). Through the analysis of soil by different test methods in Table 2, it shows that working air pressure can have a great influence on subsoiling resistance and subsoiling disturbance surface.…”

“…It can be seen from Table 3 that the p-Value of its subsoiling-soil specific resistance is significant, and the mismatch term is not significant, which proves that the model can be applied. According to the test results, the second order regression equation between soil-specific resistance and variables A, B and C is established, and the equation is: Response surface variation is related to the R 2 regression equation, adjusted R 2 and predicted R 2 values; the R 2 value should be at least 0.8 (Nahemiah et al, 2015) to achieve a good fit of the regression model (Nahemiah et al, 2015). As can be seen in Table 3, its parameter is greater than 0.8.…”

Examining the Driving Factors of SOM Using a Multi-Scale GWR Model Augmented by Geo-Detector and GWPCA Analysis

“…This is the basis for the model construction of the GWR and MGWR to select highquality auxiliary variables. Currently, no-linear machine-learning techniques (such as boosted regression trees [20], random forests [21], cubist [22], support vector machine [4], neural network [23]) and linear methods (such as multiple linear regression and redundancy analysis [9,24,25]) have been implemented to investigate the relationship between SOM and auxiliary variables. These linear methods assume that a significant linear relationship exists between the driving factors and spatial variation of SOM across an entire time series; however, this is difficult to satisfy [26].…”