Climate change impact on household food security and child undernutrition

该项目旨在量化受干扰的降雨模式对作物收成数量的贡献，及其对家庭粮食安全的影响，以便为5岁以下儿童提供营养食物。假定的后果是5岁以下儿童长期营养不良(发育迟缓)和微量营养素缺乏。为了找出天气和营养之间的联系，同时进行了三个步骤：首先，将通过制定降雨指数来确认暴露的程度，该指数反映了每日降雨量对各自地区主要农作物生长的适宜性。其次，为了确定家庭作物收获结果，我们将使用样本中家庭地块水平上已经经过当地验证的收获量化方法。第三，我们将组建一个由2000名0-5岁儿童组成的动态队列。这些步骤将在布基纳法索努纳人类发展支持系统和肯尼亚基苏木人类发展支持系统的两个研究地区，在研究股的3(+3)年寿命内执行。每年两次，将向父母发放一份关于家庭粮食安全状况和孩子饮食的社会经济和营养问卷，并对样本中的所有儿童进行选定的人体测量。降雨量(暴露)与其后果(直接结果：对收成数量的影响，间接结果：对家庭粮食安全和儿童营养不良的影响)之间的关系将由分级贝叶斯分析得出的暴露-反应函数(ERF)调用，更具体地说，是分布滞后非线性建模。我们将能够将天气与收成、粮食安全和儿童营养不良的关系与一大批协变量进行比较，例如社会经济家庭变量、儿童以前的疾病发作以及家庭支出和自给自足农业的收入。在最后一步，ERF将被纳入气候影响模型，由我们的中央项目组(CP2)以12公里乘12公里的分辨率缩小到研究区域。这将使我们能够对短期和长期政策相关时间范围(2030年、2050年、2100年)的气候营养影响进行预测。

This project aims to quantify the contribution of disturbed rainfall patterns to the quantity of crop harvest, and its impact on household food security to provide nutritious foods consumed by children under 5 years of age. The assumed consequences are chronic undernutrition (stunting) and micronutrient deficiency in children under 5.In order to draw linkages between weather and nutrition, three steps are applied simultaneously: firstly, the validation of the exposure will be done by developing a rainfall index capturing the suitability of daily rainfall for the growth of major crop plants of the respective regions. Secondly, for determining the household crop harvest outcome, we will use the already locally validated quantification of harvest at the household plot level of households in our sample. Thirdly, we will assemble a dynamic cohort of 2000 children aged 0-5 years. These steps will be done in both study areas in the Nouna HDSS in Burkina Faso and in the Kisumu HDSS in Kenya over the 3 (+3) year lifetime of the Research Unit. Twice a year, a socioeconomic and nutritional questionnaire will be applied to the parents on their household food security situation and their child’s diet, and selected anthropometric measurements are taken from all children in the sample.The relationship between rainfall (exposure) and its consequences (direct outcome: impact on harvest quantity, indirect outcome: influence on household food security and childhood undernutrition) will be called by ERFs (exposure-response functions) derived from hierarchical Bayesian analyses, more specifically distributed-lag non-linear modelling. We will be able to compare the weather-related association with harvest, food security and child undernutrition with a large array of co-variables, e.g. socioeconomic household variables, previous diseases episodes in the children, and household expenditures and incomes through subsistence farming. In a final step, the ERFs will be incorporated into climate impact models, downscaled to the study area at a 12km by 12km resolution by our central project team (CP2). This will enable us to make projections of climate-nutrition impact for short- and long-term policy relevant time horizons (2030, 2050, 2100).