Understanding the factors conditioning fall armyworm (Spodoptera frugiperda J.E. Smith) infestation in African smallholder maize fields and quantifying its impact on yield. A case study in Eastern Zimbabwe.

Study sites The study was conducted in Chipinge and Makoni Districts of Manicaland Province in Zimbabwe. Chipinge is located in southeastern Zimbabwe at an average altitude of 1,134 m above sea level, and is characterized by a mean annual rainfall of 1,097.5 mm (90 years average) and a mean annual temperature of 28°C (10 years average; Maposa et al. 2010). Sandy soils, black and red clays are the major soil types. The main crops are maize, cotton, and sorghum. The main livestock species are cattle, goats, pigs and chicken. The population density is about 33 inhabitants km-2 (PCO, 2012). Makoni is located in northeastern of Zimbabwe at an average altitude of 1,372 m above sea level, and is characterized by a mean annual rainfall of 750 – 1,000 mm per year (4 years average) and a mean annual temperature of 27°C (10 years average; UNDP 2016). Sandy to sandy loams are the major soil types. The main crops are maize, groundnuts and tobacco. The main livestock species are cattle, goats and chicken. The population density is about 35 inhabitants km-2 (PCO, 2012). Farm survey A total of 394 and 397 farming households were selected from Chipinge and Makoni Districts, respectively. In each district, households were selected following a stratified sampling scheme, with roughly a third of them each selected from a relatively wetter ward, a relatively drier ward and a ward of intermediate climate. In Chipinge District, Wards 16, 18 and 20 were selected as the drier, intermediate and wetter wards, respectively. In Makoni District, Wards 26, 28 and 34 were selected as the drier, intermediate and wetter wards, respectively. The head of each of these selected households was interviewed – between 2 and 7 February 2018 in Chipinge District and between 22 and 28 March 2018 in Makoni District – using a standardized questionnaire that addressed the characteristics of the main maize plot (area, soil type, presence or absence of a hedgerow, previous crop), the characteristics of the crop (maize growth stage estimated using the V notation, maize variety, crop species being intercropped if any), tillage (mode and dates), fertilization (type and quantity of fertilizer, manure, and compost) and crop protection (date and number of weeding operations, herbicide applications, and pesticide applications). Each maize plot was then scouted using the method described by McGrath et al. (2018): five sampling points of 10 plants each were selected using a ‘W’ scouting pattern and the number of plants displaying leaf damages caused by FAW larvae and with FAW frass in the whorl were recorded at each sampling point. The Davis scale, which rates the extent of leaf damage from 1 to 9 (Davis and Williams, 1992), was also used to give a score for each sampling point. Yield assessment From the 791 fields assessed during the growing season, a total of 167 fields (54 in Chipinge District and 113 in Makoni District) were selected for yield assessment using the ear digital imaging method (Makanza et al., 2018). These fields were purposefully selected to span the whole range of infestation levels observed during the growing season. For each plot, five quadrats of two meters by one meter were laid out following a ‘W’ sampling frame (as for the damage scouting). The number of plants and the number of cobs were counted in each quadrat. Cobs were then harvested and pooled for each field. After husks were removed, cobs were then laid on a black plastic sheet side by side and a picture was taken with an 8-inch Samsung's Galaxy Tab S2 camera with a resolution of 8-megapixels with an f/1.9 lens. To enable the conversion of pixel scale measurements to centimeters, a ruler was placed near the cobs before taking each picture. The pictures were later processed using a script that runs on ImageJ; an open source software (https://imagej.nih.gov/ij/features.html). The script estimates grain weight based on 2 models (i) the total kernel number derived from the number of kernels visible on the image and (ii) the average grain weight generated from average grain size (Makanza et al., 2018). Data manipulation and calculations Soil types were grouped in five texture categories: ‘Sandy’, ‘Sandy loam’, ‘Loamy’, ‘Loamy clay’, and ‘Clayey’. Intercrops were grouped in four categories: ‘None’, ‘Pulse’, ‘Pumpkin’, and ‘Pulse + Pumpkin’. Maize varieties were grouped in 10 categories: ‘SC500’, ‘SC400’, ‘SC600‘, ‘PAN413’, ‘PAN53’, ‘PHB30G19’, ‘ZAP61’, ‘Recycled’ (i.e., seeds harvested from a previous hybrid maize crop, often of unspecified variety), ‘OPV (i.e., open-pollinated varieties), and ‘Other’. Manure application, compost application, herbicide application, and pesticide application were converted into binary variables (‘Yes’, ‘No’). The number of weeding operations was converted into ‘Infrequent’ (one or less) or ‘Frequent’ (two or more). The quantities of fertilizer applied were converted into quantities of nitrogen (N) and quantities of phosphorus pentoxide (P2O5) using specific fertilizer compositions, and were expressed on a per hectare basis. For each sampling point, the proportion of plants with leaf damage and with frass in the whorl was calculated. Damage scores were transformed by subtracting 1 and dividing the value by 8 in order to obtain a number bounded between 0 and 1. Finally, the mean proportion of plants with leaf damage and with frass in the whorl, and the mean transformed damage score were calculated for each plot. For each plot, the grain weight in the five quadrats (as estimated through image analysis) was summed and converted into grain yield in kg ha-1. To be able to relate grain yield with infestation parameters – which are assessed on a per plant basis – and as the variability in plant density was high between the different plots assessed, grain yield was also calculated in kg plant-1 by dividing grain yield (in kg ha-1) by plant population (in plants ha-1).

本研究于津巴布韦马尼卡兰省奇平盖（Chipinge）与马科尼（Makoni）地区开展。奇平盖位于津巴布韦东南部，平均海拔1134米，90年平均年降水量为1097.5毫米，年平均气温28℃（10年平均；Maposa等，2010）。区域主要土壤类型为砂质土、黑黏土与红黏土，主要农作物为玉米、棉花与高粱，主要畜禽品种为牛、山羊、猪与鸡，人口密度约为33人/km²（PCO，2012）。马科尼位于津巴布韦东北部，平均海拔1372米，4年平均年降水量为750~1000毫米，年平均气温27℃（10年平均；UNDP，2016）。区域主要土壤类型为砂质土至砂壤土，主要农作物为玉米、花生与烟草，主要畜禽品种为牛、山羊与鸡，人口密度约为35人/km²（PCO，2012）。

农场调查 本研究分别从奇平盖与马科尼地区选取394户和397户农户。两区均采用分层抽样方案选取农户，每个区域约三分之一的农户分别来自相对湿润、相对干旱以及气候中等的行政选区。其中，奇平盖地区选取第16、18、20选区分别作为干旱、中等、湿润选区；马科尼地区选取第26、28、34选区分别作为干旱、中等、湿润选区。

2018年2月2日至7日对奇平盖地区的受访农户户主进行访谈，2018年3月22日至28日对马科尼地区的受访农户户主开展访谈，访谈采用标准化问卷，内容涵盖玉米主地块特征（面积、土壤类型、是否有绿篱、前茬作物）、作物特征（采用V分级法估算的玉米生育期、玉米品种、间作作物种类（若有））、耕作方式（模式与耕作日期）、施肥情况（肥料、厩肥与堆肥的类型与用量）以及作物保护措施（除草作业的日期与次数、除草剂施用情况、农药施用情况）。

随后采用McGrath等（2018）描述的方法对每个玉米地块进行虫害调查：采用‘W’型采样模式选取5个采样点，每个采样点包含10株玉米，记录每个采样点中受草地贪夜蛾（Fall Armyworm, FAW）幼虫为害造成叶片损伤以及心叶内有草地贪夜蛾虫粪的植株数量。同时采用Davis分级法（叶片为害程度分级范围为1~9级；Davis和Williams，1992）为每个采样点赋值评分。

产量评估 本研究在生长季共评估791块农田，其中选取167块农田（奇平盖地区54块，马科尼地区113块）采用穗部数字成像法（Makanza等，2018）开展产量评估。所选农田覆盖了生长季观测到的所有为害程度梯度。针对每个地块，采用与虫害调查相同的‘W’型采样框架布设5个2m×1m的样方。统计每个样方内的植株数量与果穗数量，随后采收所有果穗并按地块混合。去除苞叶后，将果穗并排铺放在黑色塑料布上，采用8英寸三星Galaxy Tab S2平板电脑（800万像素，f/1.9光圈）拍摄照片。为将像素尺度转换为厘米尺度，拍摄每张照片前均在果穗旁放置一把直尺。后续采用运行于开源软件ImageJ（https://imagej.nih.gov/ij/features.html）的脚本处理照片，该脚本基于两种模型估算籽粒重量：（i）基于图像可见籽粒数计算的总籽粒数；（ii）基于平均籽粒尺寸得到的平均籽粒重量（Makanza等，2018）。

数据处理与计算 土壤类型被划分为5个质地类别：‘Sandy’（砂质土）、‘Sandy loam’（砂壤土）、‘Loamy’（壤土）、‘Loamy clay’（壤质黏土）与‘Clayey’（黏质土）。间作类型被划分为4个类别：‘None’（无间作）、‘Pulse’（豆科作物）、‘Pumpkin’（南瓜）与‘Pulse + Pumpkin’（豆科作物+南瓜）。玉米品种被划分为10个类别：‘SC500’、‘SC400’、‘SC600’、‘PAN413’、‘PAN53’、‘PHB30G19’、‘ZAP61’、‘Recycled’（自留种，即从先前的杂交玉米作物收获的种子，品种通常未明确）、‘OPV（开放授粉品种）’与‘Other’（其他）。将厩肥施用、堆肥施用、除草剂施用与农药施用转换为二分类变量（‘是’、‘否’）。将除草作业次数划分为‘少次’（1次及以下）与‘多次’（2次及以上）。根据特定肥料成分，将施用的肥料用量转换为纯氮（N）与五氧化二磷（P₂O₅）用量，并以每公顷为单位表示。针对每个采样点，计算叶片受损植株以及心叶内有虫粪植株的占比。将为害评分进行标准化转换：先减1，再除以8，使结果范围限定于0~1之间。最后，计算每个地块的叶片受损植株占比、心叶虫粪植株占比以及标准化后的平均为害评分。将5个样方的籽粒重量（通过图像分析估算）求和后，转换为以kg/ha为单位的籽粒产量。为将籽粒产量与以单株为单位评估的为害参数关联起来，同时考虑到不同地块的植株密度差异较大，将籽粒产量（以kg/ha为单位）除以植株密度（以株/ha为单位），得到以kg/株为单位的籽粒产量。