In this study, the utility of regression-kriging was investigated in building prediction models for soil properties using mid-infrared (7498 to 600 cm 1) spectral data for soil samples collected from Nyando, Nzoia and Yala catchment areas in Kenya, sampled at 0–20 cm and 20–50 cm depths. Using a systematic technique, 158 samples were selected for analysis of a number of soil properties of interest using wet chemistry methods. We randomly divided the dataset into two groups: 118 samples in the calibration and 40 samples in the holdout validation set. The calibration set was first used to develop partial least squares regression (PLS) models for all the soil properties. Residuals from these models were used to generate semivariograms, which revealed a strong spatial dependence as determined by the ratio of nugget to sill for nitrogen, 9%; Al, 12%; and B, 36%, but with weak spatial dependence for exchangeable Ca (ExCa), 100%; and carbon, 76%. The fitted theoretical semivariograms were used to fit regression-kriging models. Lastly, both the PLS and regression-kriging models were assessed with the validation set and their prediction performance evaluated by R2 and root mean square error (RMSE). The results showed that regression kriging method gave lower RMSE values for all the evaluated soil properties except for ExCa, B and exchangeable acidity, with the best predictions, compared with the PLS model, obtained for ExMg (R2, 0.93 vs 0.88; RMSE, 6.1 vs 8.4 cmolc kg 1) and total nitrogen (R2 = 0.92 vs R2 = 0.74; RMSE, 0.11%, RMSE = 0.2%). In this study, regression-kriging, which takes into account spatial variation normally ignored by other methods, improved use of infrared spectroscopy for predicting soil properties.
Tag: forecasting
Quo vadis global forest governance? A transdisciplinary delphi study
Deforestation and forest degradation remain huge global environmental challenges. Over the last decades, various forest governance initiatives and institutions have evolved in global response to interlinked topics such as climate change mitigation, biodiversity conservation, indigenous rights, and trade impacts – accompanied by various levels of academic attention. Using a Delphi methodology that draws on both policy and academic insights, we assess the currently perceived state of play in global forest governance and identify possible future directions. Results indicate that state actors are seen to be key in providing supportive regulatory frameworks, yet interviewees do not believe these will be established at the global scale. Rather, respondents point to issue-specific, regional and inter-regional coalitions of the willing, involving the private sector, to innovate global forest governance. Linking forest issues with high politics may hold promise, as demonstrated by initiatives regarding illegal logging and timber trade. Confident rule-setting in support of the public good as well as responsible investments are seen as further avenues. New forest governance “hypes”, if used strategically, can provide leverage points and resources to ensure sustainability effects on the ground. At the same time, informal markets are often crucial for governance outcomes and need consideration. As such, clarifying tenure in sovereignty-sensitive ways is important, as are innovative ways for inclusive “glocal” decision-making. Lastly, new technologies, big data and citizens’ capacities are identified as potent innovation opportunities, for making global dependencies between consumption, production and deforestation visible and holding players accountable across the value chains.