{"type": "FeatureCollection", "features": [{"id": "10.1016/j.catena.2021.105718", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-05-30T16:16:20Z", "type": "Journal Article", "created": "2021-09-11", "title": "Correlation of banana productivity levels and soil morphological properties using regularized optimal scaling regression", "description": "Soil morphological properties described in the field, such as texture, consistence or structure, provide a valuable tool for the evaluation of soil productivity potential. In this study, we developed a regression model between the soil morphological variables of banana plantations and a crop Productivity Index (PI) previously developed for the same areas in Venezuela. For this, we implemented categorical regression, an optimal scaling procedure in which the morphological variables are transformed into a numerical scale, and can thus be entered in a multiple regression analysis. The model was developed from data from six plantations growing \u201cGran Nain\u201d bananas, each with two productivity levels (high and low), in two 4-ha experimental plots, one for each productivity level. Sixty-three A horizons in thirty-six soils were described using 15 field morphological variables on a nominal scale for structure type, texture and hue, and an ordinal scale for the rest (structure grade, structure size, wet and dry consistence, stickiness, plasticity, moist value, chroma, root abundance, root size, biological activity and reaction to HCl). The optimum model selected included biological activity, texture, dry consistence, reaction to HCl and structure type variables. These variables explained the PI with an R2 of 0.599, an expected prediction error (EPE) of 0.645 and a standard error (SE) of 0.135 using bootstrapping, and EPE of 0.662 with a SE of 0.236 using 10-fold cross validation. Our study showed how soil quality is clearly related to productivity on commercial banana plantations, and developed a way to correlate soil quality indicators to yield by using indicators based on easily measured soil morphological parameters. The methodology used in this study might be further expanded to other banana-producing areas to help identify the soils most suitable for its cultivation, thereby enhancing its environmental sustainability and profitability.", "keywords": ["2. Zero hunger", "55 Geolog\u00eda y ciencias afines", "63 Agricultura.", "Biological activity", "Biological activities", "63 Agricultura", "04 agricultural and veterinary sciences", "15. 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An online questionnaire was used to provide definitions for 33 soil\uffe2\uff80\uff90related terms in both scientific and plain language, as well as indicators for seven SESs and 11 STs. Participants were asked to rate their agreement with the definitions and indicators on a seven\uffe2\uff80\uff90grade Likert scale. The level of agreement was calculated as the percentage of ratings above 4, the neutral position. The survey was available from June to September 2023 and was distributed by a snowball approach. More than 260 stakeholders assessed the survey; 70% of respondents were researchers, and 15% were practitioners. Mean agreement levels for the definitions and indicators were generally high, at 85% and 78% respectively. However, it was apparent that the lowest agreement was found for terms that are relatively new, such as Ecosystem Services and Bundle, or unfamiliar for certain subgroups, such as ecological terms for stakeholders working at the farm scale. 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In this study, we developed a regression model between the soil morphological variables of banana plantations and a crop Productivity Index (PI) previously developed for the same areas in Venezuela. For this, we implemented categorical regression, an optimal scaling procedure in which the morphological variables are transformed into a numerical scale, and can thus be entered in a multiple regression analysis. The model was developed from data from six plantations growing \u201cGran Nain\u201d bananas, each with two productivity levels (high and low), in two 4-ha experimental plots, one for each productivity level. Sixty-three A horizons in thirty-six soils were described using 15 field morphological variables on a nominal scale for structure type, texture and hue, and an ordinal scale for the rest (structure grade, structure size, wet and dry consistence, stickiness, plasticity, moist value, chroma, root abundance, root size, biological activity and reaction to HCl). The optimum model selected included biological activity, texture, dry consistence, reaction to HCl and structure type variables. These variables explained the PI with an R2 of 0.599, an expected prediction error (EPE) of 0.645 and a standard error (SE) of 0.135 using bootstrapping, and EPE of 0.662 with a SE of 0.236 using 10-fold cross validation. Our study showed how soil quality is clearly related to productivity on commercial banana plantations, and developed a way to correlate soil quality indicators to yield by using indicators based on easily measured soil morphological parameters. The methodology used in this study might be further expanded to other banana-producing areas to help identify the soils most suitable for its cultivation, thereby enhancing its environmental sustainability and profitability.", "keywords": ["2. 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As unclear communication is a key barrier that hinders the implementation of research results into practice, this study aimed to answer the question about whether the terminology of large\uffe2\uff80\uff90scale initiatives is adequately understood within the soil\uffe2\uff80\uff90science community and non\uffe2\uff80\uff90research stakeholders. An online questionnaire was used to provide definitions for 33 soil\uffe2\uff80\uff90related terms in both scientific and plain language, as well as indicators for seven SESs and 11 STs. Participants were asked to rate their agreement with the definitions and indicators on a seven\uffe2\uff80\uff90grade Likert scale. The level of agreement was calculated as the percentage of ratings above 4, the neutral position. The survey was available from June to September 2023 and was distributed by a snowball approach. More than 260 stakeholders assessed the survey; 70% of respondents were researchers, and 15% were practitioners. Mean agreement levels for the definitions and indicators were generally high, at 85% and 78% respectively. However, it was apparent that the lowest agreement was found for terms that are relatively new, such as                     Ecosystem Services                     and                     Bundle                     , or unfamiliar for certain subgroups, such as ecological terms for stakeholders working at the farm scale. Due to their distinct majority, the results of this study primarily reflect the opinions of scientists. Thus, broad conclusions can only be drawn by comparing scientists with non\uffe2\uff80\uff90scientists. In this regard, the agreement was surprisingly high across all types of questions. The combined outcomes indicate that there is still a need to facilitate communication between stakeholders and to improve knowledge distribution strategies. 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