10.5061/DRYAD.4QRFJ6QB3
Scott, Donald
0000-0001-5936-6558
University of Sheffield
Data and R scripts for: Identifying existing management practices in the
control of Striga asiatica within rice–maize systems in mid-west
Madagascar
Dryad
dataset
2021
FOS: Biological sciences
Natural Environment Research Council
https://ror.org/02b5d8509
2022-08-26T00:00:00Z
2022-08-26T00:00:00Z
en
https://doi.org/10.22541/au.162453823.37847249/v1
https://doi.org/10.5281/zenodo.5215588
https://doi.org/10.5281/zenodo.5215590
144212 bytes
4
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
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.
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 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. Large-scale Transects 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. 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 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 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 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. Soil Samples 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.