Data and R scripts for: Identifying existing management practices in the control of Striga asiatica within rice–maize systems in mid-west Madagascar
Cite this dataset
Scott, Donald (2022). Data and R scripts for: Identifying existing management practices in the control of Striga asiatica within rice–maize systems in mid-west Madagascar [Dataset]. Dryad. https://doi.org/10.5061/dryad.4qrfj6qb3
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.
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 km2, 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 oC.
The aim of the sampling was to estimate the abundance of Striga 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 Striga 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 (Vigna unguiculata), ricebean (Vigna umbellata), soybean (Glycine max) and groundnut (Arachis hypogaea),- and manioc (Manihot esculenta).
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.
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 S. asiatica 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 200m2 (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 Striga density, by walking at a steady pace along a central transect, and scanning 5 m to either side; in fields >1200 m2, 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. Striga 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 Striga, 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 Striga was found in a quadrat, a thorough walk throughout the entire field was undertaken to verify that Striga was truly absent. If Striga 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 Striga 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 22.214.171.124).
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 Striga 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 Striga.
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.
Alongside the impact of cropping, the role of available nitrogen in determining Striga densities was addressed through collecting and analysing soil samples for NO3. These samples were collected in pairs from quadrats with contrasting Striga 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.
NO3 analysis was undertaken using a LAQUAtwin NO3-11 nitrate meter (Horiba Scientific, Japan). Owing to low levels of NO3 within the soil, it was necessary to dilute the standard solution supplied with the meter. Therefore, calibration was undertaken between 15 and 150 ppm NO3 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 NO3 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 Striga asiatica (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).
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.
Natural Environment Research Council