Filling the agronomic data gap through a minimum data collection approach.

Context: Agronomic data such as applied inputs, management practices, and crop yields are needed for assessing productivity, nutrient balances, resource use efficiency, as well as other aspects of environmental and economic performance of cropping systems. In many instances, however, these data are only available at a coarse level of aggregation or simply do not exist. Objectives: Here we developed an approach that identifies sites for agronomic data collection for a given crop and country, seeking a balance between minimizing data collection efforts and proper representation of the main crop producing areas. Methods: The developed approach followed a stratified sampling method based on a spatial framework that delineates major climate zones and crop area distribution maps, which guides selection of sampling areas (SA) until half of the national harvested area is covered. We provided proof of concept about the robustness of the approach using three rich databases including data on fertilizer application rates for maize, wheat, and soybean in Argentina, soybean in the USA, and maize in Kenya, which were collected via local experts (Argentina) and field surveys (USA and Kenya). For validation purposes, fertilizer rates per crop and nutrient derived at (sub-) national level following our approach were compared against those derived using all data collected from the whole country. Results: Application of the approach in Argentina, USA, and Kenya resulted in selection of 12, 28, and 10 SAs, respectively. For each SA, three experts or 20 fields were sufficient to give a robust estimate of average fertilizer rates applied by farmers. Average rates at national level derived from our approach compared well with those derived using the whole database ( ± 10 kg N, ± 2 kg P, ± 1 kg S, and ± 5 kg K per ha) requiring less than one third of the observations. Conclusions: The developed minimum crop data collection approach can fill the agronomic data gaps in a cost-effective way for major crop systems both in large- and small-scale systems. Significance: The proposed approach is generic enough to be applied to any crop-country combination to guide collection of key agricultural data at national and subnational levels with modest investment especially for countries that do not currently collect data.

[Compliance with post-cesarean section thromboprophylaxis recommendations. A cross sectional study in 233 women]. / Adherencia a las recomendaciones de tromboprofilaxis post-cesárea: estudio de corte transversal.

BACKGROUND: Cesarean section increases four times the risk of venous thromboembolism compared to vaginal delivery. The Royal College of Obstetricians and Gynecologists guidelines are used at our service. A written alert was designed to stratify patients at high, intermediate or low risk making a suggestion for thromboprophylaxis. AIM: To assess the compliance with the guidelines and to evaluate the impact of a written alert in the thromboprophylaxis compliance in women subjected to caesarean section. PATIENTS AND METHODS: Review of medical records of 233 women aged 19 to 32 years, subjected to a caesarean section in a Gynecology Service, between 2016-2017. RESULTS: Compliance with recommendations was observed in 29% of patients (68/233), 86% in the low-risk group, 26% in the intermediate risk group and 100% in the high risk group. In 41/233 (18%) of patients, a written alert was included in the medical record. Compliance with recommendations in the presence of the written alert was 61% (25/41 women) compared to 22% (43/192) in those lacking the alert (p < 0.01). In women whose emergency caesarean section was the only risk factor, the compliance with the recommendation was 8%, compared with 30% among those who had at least one thrombotic risk factor associated with caesarean section (p < 0.01). CONCLUSIONS: In this cross-sectional study, we observed a low compliance with thromboprophylaxis guidelines in cesarean women. We observed that the use of a written alert improved the compliance with thromboprophylaxis.

Adopting yield-improving practices to meet maize demand in Sub-Saharan Africa without cropland expansion.

Maize demand in Sub-Saharan Africa is expected to increase 2.3 times during the next 30 years driven by demographic and dietary changes. Over the past two decades, the area cropped with maize has expanded by 17 million hectares in the region, with limited yield increase. Following this trend could potentially result in further maize cropland expansion and the need for imports to satisfy domestic demand. Here, we use data collected from 14,773 smallholder fields in the region to identify agronomic practices that can improve farm yield gains. We find that agronomic practices related to cultivar selection, and nutrient, pest, and crop management can double on-farm yields and provide an additional 82 million tons of maize within current cropped area. Research and development investments should be oriented towards agricultural practices with proven capacity to raise maize yields in the region.

Field and in-silico analysis of harvest index variability in maize silage.

Maize silage is a key component of feed rations in dairy systems due to its high forage and grain yield, water use efficiency, and energy content. However, maize silage nutritive value can be compromised by in-season changes during crop development due to changes in plant partitioning between grain and other biomass fractions. The partitioning to grain (harvest index, HI) is affected by the interactions between genotype (G) × environment (E) × management (M). Thus, modelling tools could assist in accurately predicting changes during the in-season crop partitioning and composition and, from these, the HI of maize silage. Our objectives were to (i) identify the main drivers of grain yield and HI variability, (ii) calibrate the Agricultural Production Systems Simulator (APSIM) to estimate crop growth, development, and plant partitioning using detailed experimental field data, and (iii) explore the main sources of HI variance in a wide range of G × E × M combinations. Nitrogen (N) rates, sowing date, harvest date, plant density, irrigation rates, and genotype data were used from four field experiments to assess the main drivers of HI variability and to calibrate the maize crop module in APSIM. Then, the model was run for a complete range of G × E × M combinations across 50 years. Experimental data demonstrated that the main drivers of observed HI variability were genotype and water status. The model accurately simulated phenology [leaf number and canopy green cover; Concordance Correlation Coefficient (CCC)=0.79-0.97, and Root Mean Square Percentage Error (RMSPE)=13%] and crop growth (total aboveground biomass, grain + cob, leaf, and stover weight; CCC=0.86-0.94 and RMSPE=23-39%). In addition, for HI, CCC was high (0.78) with an RMSPE of 12%. The long-term scenario analysis exercise showed that genotype and N rate contributed to 44% and 36% of the HI variance. Our study demonstrated that APSIM is a suitable tool to estimate maize HI as one potential proxy of silage quality. The calibrated APSIM model can now be used to compare the inter-annual variability of HI for maize forage crops based on G × E × M interactions. Therefore, the model provides new knowledge to (potentially) improve maize silage nutritive value and aid genotype selection and harvest timing decision-making.

Mental Representations of Sickness Positively Relate to Adaptive Health Behaviors.

An ecological approach to social perception states that impressions of faces have functional value in that they guide adaptive behavior ensuring people's survival. For example, people may avoid others whose faces appear sick to avoid an illness representing a survival threat. We broadened the ecological approach in the current work by examining whether merely thinking about what illnesses on faces look like (i.e., how sickness on faces is represented) holds functional value in guiding behavior to ensure survival. Using an example of a real illness threat as proof of concept, we showed that people self-reported performing more adaptive health behaviors in response to COVID-19 if they had sicker representations of COVID-19 on faces (Experiment 1a). These sicker representations of COVID-19 on faces explained, in part, a positive relation between perceptions of COVID-19 as threatening and people's self-reported adaptive health behaviors. We then replicated these patterns when experimentally manipulating illness threat (Experiment 1b). We found that people expected more adaptive health behaviors and had sicker representations of illness on faces in response to illness threats that were more relative to less threatening. These findings suggest that mentally representing sickness on faces is enough to guide people's behaviors in response to illness threats.

Uncertainty in climate change impact studies for irrigated maize cropping systems in southern Spain.

This study investigates the main drivers of uncertainties in simulated irrigated maize yield under historical conditions as well as scenarios of increased temperatures and altered irrigation water availability. Using APSIM, MONICA, and SIMPLACE crop models, we quantified the relative contributions of three irrigation water allocation strategies, three sowing dates, and three maize cultivars to the uncertainty in simulated yields. The water allocation strategies were derived from historical records of farmer's allocation patterns in drip-irrigation scheme of the Genil-Cabra region, Spain (2014-2017). By considering combinations of allocation strategies, the adjusted R2 values (showing the degree of agreement between simulated and observed yields) increased by 29% compared to unrealistic assumptions of considering only near optimal or deficit irrigation scheduling. The factor decomposition analysis based on historic climate showed that irrigation strategies was the main driver of uncertainty in simulated yields (66%). However, under temperature increase scenarios, the contribution of crop model and cultivar choice to uncertainty in simulated yields were as important as irrigation strategy. This was partially due to different model structure in processes related to the temperature responses. Our study calls for including information on irrigation strategies conducted by farmers to reduce the uncertainty in simulated yields at field scale.

Effects of soil- and climate data aggregation on simulated potato yield and irrigation water requirement.

Input data aggregation affects crop model estimates at the regional level. Previous studies have focused on the impact of aggregating climate data used to compute crop yields. However, little is known about the combined data aggregation effect of climate (DAEc) and soil (DAEs) on irrigation water requirement (IWR) in cool-temperate and spatially heterogeneous environments. The aims of this study were to quantify DAEc and DAEs of model input data and their combined impacts for simulated irrigated and rainfed yield and IWR. The Agricultural Production Systems sIMulator Next Generation model was applied for the period 1998-2017 across areas suitable for potato (Solanum tuberosum L.) in Tasmania, Australia, using data at 5, 15, 25 and 40 km resolution. Spatial variances of inputs and outputs were evaluated by the relative absolute difference (rAD¯) between the aggregated grids and the 5 km grids. Climate data aggregation resulted in a rAD¯ of 0.7-12.1%, with high values especially for areas with pronounced differences in elevation. The rAD¯ of soil data was higher (5.6-26.3%) than rAD¯ of climate data and was mainly affected by aggregation of organic carbon and maximum plant available water capacity (i.e. the difference between field capacity and wilting point in the effective root zone). For yield estimates, the difference among resolutions (5 km vs. 40 km) was more pronounced for rainfed (rAD¯ = 14.5%) than irrigated conditions (rAD¯ = 3.0%). The rAD¯ of IWR was 15.7% when using input data at 40 km resolution. Therefore, reliable simulations of rainfed yield require a higher spatial resolution than simulation of irrigated yields. This needs to be considered when conducting regional modelling studies across Tasmania. This study also highlights the need to separately quantify the impact of input data aggregation on model outputs to inform about data aggregation errors and identify those variables that explain these errors.

Sistema de audiencias sanitarias para el desarrollo de conocimientos sobre riesgo reproductivo preconcepcional. 2020-2022

Introducción: El conocimiento preconcepcional de los factores de riesgo que posee una pareja, es vital para su modificación previa al embarazo y lograr una gestación exitosa. Objetivo: Elaborar un sistema de audiencias sanitarias que contribuya al desarrollo de conocimientos sobre riesgo reproductivo y su prevención. Métodos: Se realizó una investigación de desarrollo en el Policlínico Comunitario Andrés Casallas, del municipio Unión de Reyes, en el período comprendido entre junio de 2020 y mayo de 2022. El universo estuvo constituido por 101 mujeres en edad fértil. La investigación se realizó en dos etapas, las variables estudiadas fueron: factores de riesgo asociados al riesgo reproductivo preconcepcional y educación preconcepcional, además de las necesidades de aprendizaje, exploradas a través de la encuesta diseñada. Se aplicó una prueba piloto y se emplearon métodos empíricos y estadísticos para la validación de la encuesta. Resultados: En las mujeres estudiadas se observó un predominio de factores de riesgo ambientales asociados al riesgo reproductivo, una educación preconcepcional deficiente que las hace vulnerables, así como un nivel de conocimientos en prevención, evaluado de mal con necesidades educativas sentidas altas, por lo que se diseñaron los componentes que conforman el sistema de audiencias sanitarias. Conclusiones: El sistema de audiencias sanitarias, elaborado en correspondencia con las necesidades de aprendizaje diagnosticadas en las mujeres estudiadas, contribuirá al desarrollo de conocimientos en riesgo reproductivo preconcepcional y a la planificación futura de la gestación.

Introduction: Preconception knowledge of the risk factors that a couple has is vital for their modification prior to pregnancy and achieving a successful gestation. Objective: To develop a system of health hearings that contributes to the development of knowledge on reproductive risk and its prevention. Methods: A development research was carried out in the Community Policlinic Andrés Casallas in the municipality Union de Reyes, in the period from June 2020 to May 2022. The universe consisted of 101 women of childbearing age. The research was carried out in two stages; the variables studied were risk factors associated with preconception reproductive risk and preconception education, in addition to the learning needs, explored through the survey designed. A pilot test was applied and statistical and empirical methods were used to validate the survey. Results: In the women studied, there it was observed a predominance of environmental risk factors associated to the reproductive risk and a deficient preconception education that makes them vulnerable, as well as a level of knowledge in prevention, evaluated of bad with high felt educational needs, so the components that make up the health hearings system were designed. Conclusions: The health hearings system, designed in correspondence with the learning needs diagnosed in the women studied, will contribute to the development of knowledge in preconception reproductive risk and to the future planning of gestation.