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6 resultados, página 1 de 1

Generalized Neophobia: concept, theoretical model and measurement

Arturo Barraza Macías (2023, [Artículo, Artículo])

Objective: To build and validate the generalized neophobia scale and establish its prevalence and sociodemographic profile of the participants. Method: an instrumental and correlational study was carried out by applying the generalized neophobia scale to 517 people. Results: the scale has a reliability level that ranges between .79 and .88, the exploratory factor analysis reports a single factor that explains 69.29% of the total variance and the confirmatory factor analysis reports a perfect fit model; the prevalence in the surveyed population was 13.5% and it is women and younger people who report a higher level of neophobia. Conclusions: the theoretical-conceptual contributions of this research can be assessed as consistent and its empirical contribution can be considered as indicative since its main limitation is the selection of the sample determined by accessibility and availability.

fobia específica miedo situación novedosa emoción HUMANIDADES Y CIENCIAS DE LA CONDUCTA HUMANIDADES Y CIENCIAS DE LA CONDUCTA specific phobia afraid novel situation

Closing the yield gap of soybean (Glycine max (L.) Merril) in Southern Africa: a case of Malawi, Zambia, and Mozambique

Siyabusa Mkuhlani Isaiah Nyagumbo (2023, [Artículo])

Introduction: Smallholder farmers in Sub-Saharan Africa (SSA) are increasingly producing soybean for food, feed, cash, and soil fertility improvement. Yet, the difference between the smallholder farmers’ yield and either the attainable in research fields or the potential from crop models is wide. Reasons for the yield gap include low to nonapplication of appropriate fertilizers and inoculants, late planting, low plant populations, recycling seeds, etc. Methods: Here, we reviewed the literature on the yield gap and the technologies for narrowing it and modelled yields through the right sowing dates and suitable high-yielding varieties in APSIM. Results and Discussion: Results highlighted that between 2010 and 2020 in SSA, soybean production increased; however, it was through an expansion in the cropped area rather than a yield increase per hectare. Also, the actual smallholder farmers’ yield was 3.8, 2.2, and 2.3 times lower than the attainable yield in Malawi, Zambia, and Mozambique, respectively. Through inoculants, soybean yield increased by 23.8%. Coupling this with either 40 kg ha−1 of P or 60 kg ha−1 of K boosted the yields by 89.1% and 26.0%, respectively. Overall, application of 21–30 kg ha-1 of P to soybean in SSA could increase yields by about 48.2%. Furthermore, sowing at the right time increased soybean yield by 300%. Although these technologies enhance soybean yields, they are not fully embraced by smallholder farmers. Hence, refining and bundling them in a digital advisory tool will enhance the availability of the correct information to smallholder farmers at the right time and improve soybean yields per unit area.

Decision Support Tools Digital Tools Site-Specific Recommendations CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA DECISION SUPPORT SYSTEMS LEGUMES YIELDS SOYBEANS