DOI: 10.5061/dryad.4qrfj6qb3
title: Data and R scripts for: Identifying existing management practices in the control of Striga asiatica within rice–maize systems in mid-west Madagascar

description: Infestations by the parasitic weed genus Striga result in significant
 losses to cereal crop yields across sub-Saharan Africa. The problem
 disproportionately affects subsistence farmers who frequently lack access
 to novel technologies. Effective Striga management therefore requires the
 development of strategies utilising existing cultural management
 practices. We report a multi-year, landscape-scale monitoring project for
 Striga asiatica in the mid-west of Madagascar, undertaken over 2019-2020
 with the aims of examining cultural, climatic and edaphic factors
 currently driving abundance and distribution. Long-distance transects were
 established across the middle-west region of Madagascar, over which Striga
 asiatica abundance in fields was estimated. Analysis of the data
 highlights the importance of crop variety and legumes in driving Striga
 density. Moreover, the dataset revealed significant effect of
 precipitation seasonality, mean temperature and altitude in determining
 abundance. A composite management index indicated the effect of a range of
 cultural practices on changes in Striga abundance. The findings support
 the assertion that single measures are not sufficient for the effective,
 long-term management of Striga. Furthermore, the composite score has
 potential as a significant guide of integrated Striga management beyond
 the geographic range of this study. 
description: Study System Field surveys were undertaken
 during March 2020 in the mid-west of Madagascar, one of the six major
 rice-growing regions in the country (Fujisaka 1990). The mid-west covers
 23,500 km<sup>2</sup>, with an elevation between 700 m and
 1000 m above sea level. The climate is tropical semi-humid, with a warm,
 rainy season from November to April and a cool, dry season from May to
 October. Mean annual rainfall ranges from 1100mm to 1900 mm with a mean
 temperature of 22 <sup>o</sup>C.
 Large-scale Transects The aim of the sampling was to
 estimate the abundance of <i>Striga</i> within fields that
 varied in terms of their management. Because access to fields is limited
 by the absence of good roads, we structured our survey program around the
 main road system. Field sampling was based around two long-distance driven
 transects along which <i>Striga</i> abundance was estimated in
 fields adjacent to the road. These comprised a transect of 129 km along
 the RN34, and one of 25 km along the RN1b. A total of 221 fields were
 surveyed (transect 1: n=174, transect 2, n=47). Transect 1 was located
 within Vakinakaritra province, between the towns of Betafo and Morafeno
 and transect 2 was located within Itasy and Bongolava provinces,
 approximately 6km east of Ambohimarina and the outskirts of Tsiroamandidy
 (Fig. 1). Rice-maize cropping systems are largely employed within the
 study areas, with incorporation of legumes, - mainly cowpea
 (<i>Vigna unguiculata</i>), ricebean (<i>Vigna
 umbellata</i>), soybean (<i>Glycine max</i>) and
 groundnut (<i>Arachis hypogaea</i>),- and manioc
 (<i>Manihot esculenta</i>). Fieldwork was undertaken with
 support from local technicians and guides who were familiar with the
 locality and field history. Prior to commencing work within a locality,
 the Chef Fokotany (local administrative head) was sought in order to
 present ourselves and detail the work we were undertaking. 
 Within-field Sampling
 One field was surveyed on adjacent
 sides of the road every kilometre. During the initial surveys in 2019, it
 was quickly established that detection of <i>S. asiatica</i>
 was possible within pluvial rice and maize fields of typically planted
 densities at distances up to 5 m on either side of each surveyor. Quadrat
 dimensions of 200m<sup>2</sup> (10 m x 20 m) were agreed based
 on a trade-off between speed of data capture, and accuracy of measurement.
 Fields were divided into pairs of 10 m × 20 m quadrats, in which two
 observers simultaneously recorded <i>Striga</i> density, by
 walking at a steady pace along a central transect, and scanning 5 m to
 either side; in fields &gt;1200 m<sup>2</sup>, data were
 recorded from a maximum of three pairs of quadrats. A field corner was
 randomly selected as the starting point for each field survey.
 <i>Striga</i> density was estimated using a six-point, density
 structured scale, ranging from absent (0) to very high (5). Definitions of
 density states were determined during fieldwork in 2019, and a table with
 narrative descriptors of the scale used alongside representative
 photographs for each density state was produced (see Appendix
 1). Information was collated on crop
 type, rice variety, companion crop and previous crop. In addition, mean
 crop height, and percentage crop cover was estimated for each quadrat.
 Mean density score for <i>Striga</i>, average crop height and
 cover, and other weed cover for a quadrat was entered on a mobile
 application prior to moving to a subsequent quadrat. If no
 <i>Striga</i> was found in a quadrat, a thorough walk
 throughout the entire field was undertaken to verify that
 <i>Striga</i> was truly absent. If <i>Striga</i>
 was then located, density was estimated for this area which replaced a
 quadrat with a zero record on the data sheet.   
 To measure changes in
 <i>Striga</i> density between years, fields surveyed in the
 first year (2019) were relocated using a GPS-enabled smartphone. Data were
 recorded using a smartphone with the mobile application ‘Google Sheets’
 (Google LLC, 2020, Version 1.20.492.01.45) to allow rapid and paperless
 data entry. Where new fields were surveyed, geo-referencing was undertaken
 using ‘Google My Maps’ (Google LLC, 2020, Version
 2.2.1.4). In a small number of instances, it
 was not possible to verify the exact location of previously surveyed
 fields. This was a consequence of GPS error, resulting in coordinates
 being located in margins between small fields, or being clearly erroneous
 (e.g. centred on a road, non-agricultural location). In these instances,
 the field was omitted (n=19). In instances where the resurveyed field
 contained a current non-host (i.e. non-cereal) crop, the field was
 surveyed but was omitted from analyses of <i>Striga</i>
 density (n=55). An adjacent, substitute field containing a cereal crop was
 surveyed and added to the dataset. Of the resurveyed non-cereal crop
 fields, only three were found to contain low, residual levels of
 <i>Striga. </i> Our initial intention was to extend
 both transects in order to capture a greater degree of altitudinal and
 climatic heterogeneity. However, owing to logistic constraints imposed by
 the COVID 19 situation it was only possible to extend transect 1 by 16
 kilometres east. It was also not possible to either resurvey the entirety
 of fields originally surveyed in 2019 or to extend transect 2.
 Soil Samples
 Alongside the impact of
 cropping, the role of available nitrogen in determining
 <i>Striga</i> densities was addressed through collecting and
 analysing soil samples for NO<sub>3</sub>. These samples were
 collected in pairs from quadrats with contrasting
 <i>Striga</i> densities within the same field. Samples
 comprised 23 pairs representing differing densities from absent to very
 high. These were analysed immediately following collection, with data
 added to those of the 98 samples collected in 2019 for the purposes of
 analysis. Soil
 samples were obtained from the centre of each selected quadrat using a 20
 mm diameter, hand-held, tubular soil sampler to a depth of approximately
 20 cm. Soil samples were subsequently air dried for analysis.
 NO<sub>3</sub>
 analysis was undertaken using a LAQUAtwin NO<sub>3</sub>-11
 nitrate meter (Horiba
 Scientific, Japan). Owing to low levels of
 NO<sub>3</sub> within the soil, it was necessary to dilute the
 standard solution supplied with the meter. Therefore, calibration was
 undertaken between 15 and 150 ppm NO<sub>3</sub> to improve
 sensitivity. One gram of dried soil was mixed with one millilitre of water
 and ground in a pestle and mortar. The resultant solution was then placed
 on the sensor of the meter. This procedure was repeated a minimum of two
 times per soil sample. If agreement between the first two readings was
 observed (i.e. between +/- 5 ppm NO<sub>3</sub> between
 readings), then the readings were taken, and the mean of the readings was
 used. If the readings did not concur, then sampling was repeated until
 stabilisation of readings. Climate and Altitude
 Climate data were obtained from the
 WorldClim2 dataset (Fick & Hijmans 2017). Climatic parameters
 included in the analyses were mean annual rainfall and mean annual
 temperature. Precipitation seasonality was included as an additional
 climatic factor. This was obtained by calculating the coefficient of
 variation (CV) of mean monthly precipitation, which is the ratio of the
 standard deviation of the monthly total precipitation to the mean annual
 precipitation (O’Donnell, & Ignizio, 2012). Invasion risk
 modelling has identified the seasonality of precipitation as one of the
 most significant bioclimatic variables influencing the occurrence of
 <i>Striga asiatica</i> (Mudereri et al. 2020). Seasonality is
 the chief driver of variation in monthly rainfall through the year.
 Therefore, the CV of monthly precipitation is an appropriate measure of
 seasonal variation. Altitudes for surveyed sites were obtained from CGIAR
 - Consortium for Spatial Information (CGIAR-CSI 2019).
description: The script and data will work with "R". I used
 "R studio". There are no missing values. The packages listed at
 the top of individual scripts will need to be installed before
 analysis.