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45 results for “smallholder”
WEFE data collection for a pilot smallholder farm in Costa Rica
<p>In-situ and remote data collection of environmental and socioeconomic data for the planning of integrated water, energy, food, and environment systems for the case study of a smallholder farm in Costa Rica.</p>
High resolution global industrial and smallholder oil palm map for 2019
<p>The dataset contains 634 100x100 km tiles, covering areas where oil palm plantations were detected. The file '<em>grid.shp</em>' contains the grid that covers the potential distribution of oil palm. The file '<em>grid_withOP.shp</em>' shows the 100x100 grid squares with presence of oil palm plantations. The classified images (‘<em>oil_palm_map</em>’ folder, in geotiff format) are the output of the convolutional neural network based on Sentinel-1 and Sentinel-2 half-year composites. The images have a spatial resolution of 10 meters and contain three classes: [1] Industrial closed-canopy oil palm plantations, [2] Smallholder closed-canopy oil palm plantations, and [3] other land covers/uses that are not closed canopy oil palm. The file ‘<em>Validation_points_GlobalOilPalmLayer_2019.shp</em>’ includes the 13,495 points that were used to validate the product. Each point includes the attribute ‘Class’, which is the labelled class assigned by visual interpretation, and the attribute ‘predClass, which reflects the predicted class by the convolutional neural network. The ‘Class’ and ‘predClass’ values are the same as the raster files: [1] Industrial closed-canopy oil palm plantations, [2] Smallholder closed-canopy oil palm plantations, and [3] other land covers/uses that are not closed canopy oil palm.</p> <p>See article for additional information:</p> <p>Descals, Adrià, et al. "High-resolution global map of smallholder and industrial closed-canopy oil palm plantations." <em>Earth System Science Data</em> 13.3 (2021): 1211-1231.</p> <p> </p> <p>Changelog v1:</p> <p>- The analysis was extended to Sri Lanka, South India, and countries in Eastern Africa where oil palm can potentially grow.</p> <p>- The validation dataset only includes the points drawn by simple random sampling and stratified random sampling in the grid cells where the IUCN industrial layer detected oil palm.</p> <p>- The 'Class' and 'predClass' values in the validation dataset were reclassified with the same values as the raster images: [1] Industrial plantations, [2] Smallholder plantations, and [3] Other land covers/uses.</p>
Maize management and yield of smallholder farmers in Sub-Saharan Africa between 2016 and 2022
<p>Yield and management practices data were collected from smallholders’ maize fields from 2016 to 2022. All fields corresponded to maize grown in pure stands (no intercropping). Data were collected from five maize producing regions in Sub-Saharan Africa: (i) north-central Nigeria (<em>n</em> = 115), (ii) Rwanda and Burundi (<em>n</em> = 2720), (iii) central Zambia (<em>n</em> = 861)<strong>,</strong> (iv) southwest Tanzania (<em>n</em> = 3710), and (v) eastern Uganda and western Kenya (<em>n</em> = 7367). Data were collected by One Acre Fund (https://oneacrefund.org/), an NGO that provides smallholder farmers access to agricultural training, credit, crop insurance services, and farming supplies. About half of the fields in the database comprised farmers who subscribed to the One Acre Fund program and the other half farmers who did not. </p> <p>Maize grain yield, plant density, and row spacing were measured in two randomly placed boxes of 36 square meters at harvest, avoiding field edges. Field geolocation was recorded in 70% of the observations. When missing, the field geolocation was defined based on the nearby town (21%) or associated district (9%) location for the purpose of retrieving climate data. Management practices associated with each field were reported by farmers, including sowing and harvest dates, cultivar name, fertilizer inputs (types and total quantities for both organic and inorganic), fertilization method, liming, weeding, and pesticides (mainly insecticides to control fall armyworms). Farmers also reported the incidence of adversities (such as pests, diseases, Striga witchweed, hail, and excess water). Field size was reported by farmers and, in those cases in which farmers could not provide an accurate measure of their field size, or there was a strong indication of mistakes (e.g., nutrient fertilizer rates out of range), One Acre Fund personnel took in-situ measurements to determine field size. Input rates per hectare were calculated as the ratio of the farmer-reported input amount and field size. Data were subjected to quality control to remove unlikely values. Maize yield outliers were detected with a Bonferroni Outlier Test. Observations with plant densities and fertilizer rates higher than four standard deviations from the mean were excluded as well as those without geolocation, no N or P data, and atypical sowing dates. After quality control, the database contains a total of 14,773 field observations.</p> <p>Inorganic fertilizer rates were converted to nutrient rates (in elemental nutrients) following typical fertilizer nutrient contents. Organic fertilizers were encoded separately in two binary variables and one continuous variable, indicating whether compost was used, if that compost contained manure, and compost application rate. Likewise, cultivars were classified into hybrids or open pollination varieties (OPVs), which included local varieties, retained seed, and improved OPVs. For hybrids, we retrieved the associated crop cycle maturity (short, medium, and long), disease tolerance traits, and year of release from companies’ seed catalogs. Reported incidence of diseases and insect pests (e.g., anthracnose, aphids, blight, cutworms, drought, fall armyworm, stemborer, termites, and stalk or kernel rot) were simplified to two binary variables indicating whether the crop was affected by pests and/or diseases. Infestation by parasitic witchweeds (Striga hermonthica and S. asiatica) was considered as a separate variable. Fertilization methods were also simplified to whether the fertilizer was applied inside a hole or broadcasted in the surface. Number of weeding operations was simplified to zero, one or two or more weeding per season. Sowing dates were expressed as a deviation from the estimated average sowing date for each climate zone-season combination. Fields were grouped based on their location using the climate zone scheme developed by the Global Yield Gap Atlas Project (www.yieldgap.org). Isolated observations (more than three standard deviations from the median distance across sites within the climate zone) were excluded from their group. In the case of climate zones with two maize seasons, each crop season was considered as a separate group. Field elevation was retrieved from the Amazon Web Services Terrain Tiles. Total precipitation during the growing season, as well as for early, flowering, and grain filling phases, was retrieved from CHIRP. For observations with field-level coordinates data, root-zone plant-available water-holding capacity was retrieved from the World Soil Information database, and soil clay content, pH, organic carbon, and effective cation exchange capacity from iSDA. Lastly, the topography wetness index (TWI) was calculated from the elevation data. </p> <p>Table 1. List of survey-derived variables.</p> <div> <div> <table> <tbody> <tr> <td><strong>Name</strong></td> <td><strong>Type</strong></td> <td><strong>Unit</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>plant_date_dev</td> <td>discrete</td> <td>days</td> <td>sowing date deviation from cluster average</td> </tr> <tr> <td>pl_m2</td> <td>continuous</td> <td># m2</td> <td>plant density (plants per area)</td> </tr> <tr> <td>row_spacing</td> <td>continuous</td> <td>cm</td> <td>distance between rows</td> </tr> <tr> <td>hybrid</td> <td>binary</td> <td>-</td> <td>Was a commercial hybrid seed used?</td> </tr> <tr> <td>hyb_mat</td> <td>ordinal</td> <td>-</td> <td>hybrid maturity (early, medium, late)</td> </tr> <tr> <td>hyb_yor</td> <td>continuous</td> <td>-</td> <td>Year of release of the cultivar</td> </tr> <tr> <td>hyb_tol_mln</td> <td>binary</td> <td>-</td> <td>Tolerance to maize lethal necrosis</td> </tr> <tr> <td>hyb_tol_msv</td> <td>binary</td> <td>-</td> <td>Tolerance to maize streak virus</td> </tr> <tr> <td>hyb_tol_gls</td> <td>binary</td> <td>-</td> <td>Tolerance to gray leaf spot</td> </tr> <tr> <td>hyb_tol_nclb</td> <td>binary</td> <td>-</td> <td>Tolerance to northern corn leaf blight</td> </tr> <tr> <td>hyb_tol_rust</td> <td>binary</td> <td>-</td> <td>Tolerance to rust</td> </tr> <tr> <td>hyb_tol_ear_rot</td> <td>binary</td> <td>-</td> <td>Tolerance to ear rot</td> </tr> <tr> <td>N_kg_ha</td> <td>continuous</td> <td>kg/ha</td> <td>N fertilization rate</td> </tr> <tr> <td>P_kg_ha</td> <td>continuous</td> <td>kg/ha</td> <td>P fertilization rate</td> </tr> <tr> <td>K_kg_ha</td> <td>continuous</td> <td>kg/ha</td> <td>K fertilization rate</td> </tr> <tr> <td>compost</td> <td>binary</td> <td>-</td> <td>Was compost applied?</td> </tr> <tr> <td>comp_t_ha</td> <td>continuous</td> <td>t/ha</td> <td>compost rate</td> </tr> <tr> <td>manure</td> <td>binary</td> <td>-</td> <td>Did the compost contain manure?</td> </tr> <tr> <td>fert_in_hole</td> <td>binary</td> <td>-</td> <td>Was the fertilizer applied in a hole?</td> </tr> <tr> <td>lime_kg_ha</td> <td>continuous</td> <td>kg/ha</td> <td>lime rate</td> </tr> <tr> <td>weeding</td> <td>discrete</td> <td>#</td> <td>number of times the plot was weeded</td> </tr> <tr> <td>pesticide</td> <td>binary</td> <td>-</td> <td>Was any pesticide applied?</td> </tr> <tr> <td>disease</td> <td>binary</td> <td>-</td> <td>Was yield affected by diseases?</td> </tr> <tr> <td>pest</td> <td>binary</td> <td>-</td> <td>Was yield affected by pests?</td> </tr> <tr> <td>striga</td> <td>binary</td> <td>-</td> <td>Was yield affected by the Striga weed?</td> </tr> <tr> <td>water_excess</td> <td>binary</td> <td>-</td> <td>Was yield affected by water excess (heavy rain or flooding)?</td> </tr> </tbody> </table> <p> </p> <p><strong>Table 2. List of environmental variables. </strong></p> <div> </div> <div> <table> <tbody> <tr> <td><strong>Name</strong></td> <td><strong>Unit</strong></td> <td><strong>Spatial resolution</strong></td> <td><strong>Description</strong></td> <td><strong>Source</strong></td> </tr> <tr> <td>GDD</td> <td>°C days</td> <td>30 arc-sec (1km)</td> <td>Growing degree days</td> <td>www.worldclim.org</td> </tr> <tr> <td>AI</td> <td>unitless</td> <td>30 arc-sec (1km)</td> <td>Aridity Index (annual precipitation over potential evapotranspiration)</td> <td>www.worldclim.org</td> </tr> <tr> <td>TS</td> <td>°C</td> <td>30 arc-sec (1km)</td> <td>Temperature seasonality</td> <td>www.worldclim.org</td> </tr> <tr> <td>season_prec</td> <td>mm</td> <td>3 arc-min (5.6 km)</td> <td>Total rainfall during the maize season (10% of planting to 50% of the harvest)</td> <td>www.chc.ucsb.edu/data/chirps</td> </tr> <tr> <td>season_prec_1</td> <td>mm</td> <td>3 arc-min (5.6 km)</td> <td>Rainfall during the first third of the season</td> <td>www.chc.ucsb.edu/data/chirps</td> </tr> <tr> <td>season_prec_2</td> <td>mm</td> <td>3 arc-min (5.6 km)</td> <td>Rainfall during the second third of the season</td> <td>www.chc.ucsb.edu/data/chirps</td> </tr> <tr> <td>season_prec_3</td> <td>mm</td> <td>3 arc-min (5.6 km)</td> <td>Rainfall during the last third of the season</td> <td>www.chc.ucsb.edu/data/chirps</td> </tr> <tr> <td>elev</td> <td>m.a.s.l.</td> <td>75 meters</td> <td>Elevation (altitude) above sea level</td> <td>registry.opend26ata.aws/terrain-tiles</td> </tr> <tr> <td>soil_rzpawhc</td> <td>mm</td> <td>1 km</td> <td>Root zone plant-available water holding capacity</td> <td>www.isric.org</td> </tr> <tr> <td>soil_clay</td> <td>%</td> <td>30 meters</td> <td>Clay content at 0-20cm soil depth</td> <td>www.isda-africa.com </td> </tr> <tr> <td>soil_pH</td> <td>-</td> <td>30 meters</td> <td>pH (H2O) at 0-20cm soil depth</td> <td>www.isda-africa.com </td> </tr> <tr> <td>soil_orgC</td> <td>g/kg</td> <td>30 meters</td> <td>Organic carbon at 0-20cm soil depth</td> <td>www.isda-africa.com </td> </tr> <tr> <td>soil_ECEC</td> <td>cmolc/kg</td> <td>30 meters</td> <td>Effective cation exchange capacity at 0-20cm soil depth</td> <td>www.isda-africa.com </td> </tr> <tr> <td>twi</td> <td>unitless</td> <td>75 meters</td> <td>Topographic Wetness Index</td> <td>calculated from elevation</td> </tr> </tbody> </table> </div> </div> </div>
Satellite-derived crop field boundaries in heterogeneous smallholder-dominated regions in the North of Mozambique
<p>Satellite-based field delineation has rapidly evolved due to recent advances in machine learning for computer vision. However, the scarcity of labeled data for complex and dynamic smallholder landscapes remains a major bottleneck for operational field delineation and downstream applications, particularly in Sub-Saharan Africa. We here provide reference field boundaries collected in the scope of a research project funded by the F.R.S.-FNRS. A detailed description of the data can be found in our corresponding <a href="https://doi.org/10.48550/arXiv.2312.08384">pre-print</a>.</p> <p>The dataset contains multiple files:</p> <ul> <li>sites.gpkg: Vector dataset containing the sampling sites covered by the dataset. We covered 513 sites of 600x600m, or 36 ha each. </li> <li>human_fields_train.gpkg: 1,518 field delineations distributed across 200 sites. Individual fields were manually digitized based on very high resolution satellite imagery in Google Earth Pro. Each polygon contains the corresponding image acquisition date. These fields were used for model training in the paper. </li> <li>human_fields_test.gpkg: 2,199 field delineations distributed across 313 sites. Individual fields were manually digitized based on very high resolution satellite imagery in Google Earth Pro. Each polygon contains the corresponding image acquisition date. These fields were used for evaluation of all experiments described in the paper. </li> <li>pseudo_fields_train.gpkg: 766 pseudo labels obtained from predictions using a <a href="../doi/10.5281/zenodo.7315089">pre-trained FracTAL ResUNet model</a>. The pseudo-labels correspond to the selection using P99(SemCN). For other sets of pseudo-labels please contact us.</li> </ul> <p><strong>Brief description of methods</strong></p> <p>For <strong>site selection</strong>, we developed a stratified random sampling scheme to sample from regions with actively used cropland. To identify these regions, we used an existing map of active and fallow cropland for the growing season of September 2020 through August 2021 (<a href="https://doi.org/10.1016/j.jag.2022.102937">Rufin et al., 2022</a>). We aggregated the map to a 1 ha grid and calculated the proportions of active cropland. We sampled 1,000 sites from regions mapped as containing at least 50% of active cropland within a one-hectare grid cell. We defined a site extent of 600 by 600 meters, or 36 ha, in order to assure that a sufficient number of fields can be delineated, even in regions with comparatively large field size. The selected sites were screened for VHR image quality and acceptable visibility of at least five fields, resulting in 513 sample sites. </p> <p>For <strong>human labels</strong>, we tasked human annotators to collect sparse labels (i.e. at least five fields) per site. We tasked the interpreters to collect only fields containing non-tree crops by systematically excluding tree crop plantations from our data. While individual trees in the field interior were included in our labels, trees overlapping with the field boundaries were avoided and the tree canopy was considered as the field boundary for completing the labels. All field delineations underwent an iterative quality assessment, where 18% of the initial field delineations and 7% of the field delineations in a second iteration were discarded. </p> <p>The <strong>pseudo labels </strong>provided here were selected using the 1% most confident predictions from the pre-trained model. The confidence scores were computed as the median of all pixel-level field extent probabilities for each field instance. </p> <p>For more details please read the corresponding paper:</p> <p>Rufin, Wang, Lisboa, Hemmerling, Tulbure & Meyfroidt, P. (2023). <em>Taking it further: Leveraging pseudo labels for field delineation across label-scarce smallholder regions.</em> <a href="https://doi.org/10.48550/ARXIV.2312.08384">https://doi.org/10.48550/ARXIV.2312.08384</a></p>
Data from: Agricultural specialisation increases the vulnerability of pollination services for smallholder farmers
<p>Smallholder farms make up 84% of all farms worldwide and feed two billion people. These farms are heavily reliant on ecosystem services and vulnerable to environmental change, yet under-represented in the ecological literature. The high diversity of crops in these systems makes it challenging to identify and manage the best providers of an ecosystem service, such as the best pollinators to meet the needs of multiple crops. It is also unclear whether ecosystem service requirements change as smallholders transition towards more specialised commercial farming – an increasing trend worldwide. Here, we present a new metric for predicting the species providing ecosystem services in diverse multi-crop farming systems. Working in 10 smallholder villages in rural Nepal, we use this metric to test whether key pollinators, and the management actions that support them, differ based on a farmers' agricultural priority (producing nutritious food to feed the family versus generating income from cash crops). We also test whether the resilience of pollination services changes as farmers specialise on cash crops. We show that a farmers' agricultural priority can determine the community of pollinators they rely upon. Wild insects including bumblebees, solitary bees, and flies provided the majority of the pollination service underpinning nutrient production, whilst income generation was much more dependent on a single species - the domesticated honeybee <em>Apis cerana</em>. The significantly lower diversity of pollinators supporting income generation leaves cash crop farmers more vulnerable to pollinator declines. Regardless of a farmers' agricultural priority, the same collection of wild plant species (mostly herbaceous weeds and shrubs) were important for supporting crop pollinators with floral resources. Promoting these wild plants is likely to enhance pollination services for all farmers in the region.</p> <p><em>Synthesis and applications:</em> We highlight the increased vulnerability of pollination services when smallholders transition to specialised cash crop farming and emphasise the role of crop, pollinator, and wild plant diversity in mitigating this risk. The method we present could be readily applied to other smallholder settings across the world to help characterise and manage the ecosystem services underpinning the livelihoods and nutritional health of smallholder families.</p>
Dataset for the paper "EXPANDING SMALLHOLDER IRRIGATION IN CENTRAL KENYA DEMONSTRATES THE IMPORTANCE OF PROTECTING GRASSLAND LANDSCAPES"
<p>This dataset contains the labels created for the cropland mapping task.</p> <p>2912 labels (polygons) are created using the June 2022 satellite imagery extracted from the <a title="Norway's International Climate and Forests Initiative (NICFI)" href="https://www.planet.com/nicfi/#:~:text=Through%20Norway%E2%80%99s%20International%20Climate%20&%20Forests%20Initiative%20(NICFI)," target="_blank" rel="noopener">Norway's International Climate and Forests Initiative (NICFI)</a> Satellite Data Program and accessible using Planet's API.</p> <p>The dataset is a geojson file with the following fields:</p> <p> </p> <ol> <li>quad: This column references a specific quadrant or tile. Each quadrant is identified by a unique code, such as "L15-1238E-1025N", provided by Planet.</li> <li>land_type: This column indicates the type of land use for the polygon area. There are two main categories of agriculture ("Smallholder agriculture" and "Largeholder agriculture") and a category for "Other vegetation". In the paper "Smallholder agriculture" and "Largeholder agriculture" are merged into a single label "cropland" while "Other vegetation" is considered "non-cropland."</li> <li>geometry: This column contains the geometrical data defining each polygon in the form of a list of vertex coordinates. For example, [ [ 37.79092, 0.19152 ], [ 37.791563, 0.190876 ], [ 37.790544, 0.190152 ], [ 37.7899, 0.190823 ], ….] defines a polygon through a sequence of longitude and latitude pairs, which enclose a specific area of land.</li> </ol>
Dataset from: The effects of crop type, landscape composition and agroecological practices on biodiversity and ecosystem services in tropical smallholder farms
<p>1. In the tropics, smallholder farming characterizes some of the world's most biodiverse landscapes. Agroecology as a pathway to sustainable agriculture has been proposed and implemented in sub-Saharan Africa, but the effects of agricultural practices in smallholder agriculture on biodiversity and ecosystem services are understudied. Similarly, the contribution of different landscape elements, such as shrubland or grassland cover, on biodiversity and ecosystem services to fields remains unknown.</p> <p>2. We selected 24 villages situated in landscapes with varying shrubland and grassland cover in Malawi. In each village, we assessed biodiversity of eight taxa and ecosystem services in relation to crop type, shrubland and grassland cover and the number of agroecological pest and soil management practices on smallholder's fields of different crop types (bean monoculture, maize-bean intercrop, and maize monoculture).</p> <p>3. Increasing shrubland cover altered carabid and soil bacteria communities. Carabid abundance increased in maize but decreased in intercrop and bean fields with increasing shrubland cover. Carabid abundance and richness and wasp abundance increased with soil management practices. Carabid, spider, and parasitoid abundances were higher in bean monocultures, but this was modulated by surrounding shrubland cover. Natural enemy abundances in beans were especially high in landscapes with little shrubland, possibly leading to lower bean damage in monocultures compared to intercropped fields, whereas maize monocultures had higher damage. In maize, grassland cover and pest management practices were positively related to damage. Carabid abundance was higher in fields with high bean damage and increased carabid richness in fields with high maize damage. Parasitoid abundance was negatively associated with bean damage.</p> <p>4. <em>Synthesis and application:</em> Our results suggest that maintaining biodiversity and ecosystem services on smallholder farms is not achievable with a "one size fits all" approach but should instead be adapted to the landscape context and the priorities of smallholders. Shrubland is important to maintain carabid and soil bacterial diversity, but legume cultivation beneficial to natural enemies could complement pest management in landscapes with a low shrubland cover. An increased number of agroecological soil management practices can lead to improved pest control whilst the effectiveness of agroecological pest management practices needs to be re-evaluated.</p>
Data from: Agricultural specialisation increases the vulnerability of pollination services for smallholder farmers
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Dataset from: The effects of crop type, landscape composition and agroecological practices on biodiversity and ecosystem services in tropical smallholder farms
Open the record for dataset details and reuse information.
Proximity to forest mediates trade-offs between yields and biodiversity of birds in oil palm smallholdings
<p>There is much debate about how best to mitigate the effects of agricultural expansion on biodiversity, especially in the tropics. Recent studies have emphasised that proximity to natural habitats can enhance farmland biodiversity, yet few studies have examined whether or not such proximity mediates local trade-offs between yields and biodiversity, and hence alters conclusions about the ecological benefits of alternative farming strategies. Here we examine yield-biodiversity trade-offs, focusing on birds in oil palm smallholdings at different distances from remaining areas of forest, including a large forest reserve, in Ghana. We found significantly fewer birds on higher-yielding than lower-yielding farms, in terms of both species richness and abundance. For forest specialist birds (likely to be highly vulnerable to conversion of land to agriculture) we also found a greater trade-off (i.e. lower richness and abundance for a given yield) at farms further from forest, to the extent that increasing distance to the nearest forest from 1 to 10 km had a similar effect as a 3- to 5-fold increase in fruit yield brought about by increased intensification. Our study highlights the importance of accounting for the effects of natural forest in the landscape when considering agricultural policies for biodiversity protection, underlining the importance of a landscape-scale approach to conservation.</p>
[VERSION 2] Data set and analytic codes supporting "How do management decisions impact butterfly assemblages in smallholding oil palm plantations in Peninsular Malaysia?"
<p>This is <strong>VERSION 2</strong> of data set and analytic codes (with a meta data [see the meta data from VERSION 1]) supporting "How do management decisions impact butterfly assemblages in smallholding oil palm plantations in Peninsular Malaysia?". We investigated the impacts of replanting and alternative replanting decisions (replanting with monoculture versus polyculture oil palm plantations) on within-plantation environmental conditions and butterfly assemblages (diversity, density, and composition). We also assessed the effects of habitat structure and complexity within plantations on butterfly assemblages. Apart from "BantingButterflies_ButterflyData", other data are the same as in VERSION 1.</p><p><strong>## List of changes:</strong></p><p># 1. <i>Tirumala septentrionis </i>was not included in the analyses because it should have been <i>Ideopsis vulgaris</i> (had been corrected),</p><p># 2. PC5 and PC6 (from PCA) were considered as predictors for the GLMs,</p><p># 3. The Mantel test was added.</p><p><strong>## Other notes:</strong></p><p># 1. Older version of ggiNEXT could work with facet.var = "site", now it needs to be "Assemblage"</p><p># 2. Older version of ggiNEXT could work with facet.var = "order", now it needs to be "Order.q"</p><p># 3. "set.seed(42)" function was used before running "iNEXT", ANOSIM, and the Mantel test to get reproducible outputs (exactly the same outputs every time each function is run).</p><p><strong>Funding and research permission:</strong> Jardine Foundation, the Cambridge Trust, and Tim Whitmore Fund provided funding for MFH, the Biotechnology and Biological Sciences Research Council (BBSRC) funded JS (USN: 304338625), and BBSRC (BB/T012366/1) provided funding for the establishment of the plots and surveys of environmental parameters. Research permission was provided by the Economic Planning Unit (EPU) of Malaysia's Prime Minister's Department for MFH (Ref: EPU 40/200/19/3727) and JS (Ref: MEA 40/200/19/3705).</p>
Data from: The importance of shrubland and local agroecological practices for pumpkin production in sub-Saharan smallholdings
<p>Land-use and local field management affect pollinators, pest damage, and ultimately crop yields. Agroecology is implemented as a sustainable alternative to conventional agricultural practices, but little is known about its potential for pollination and pest management. Sub-Saharan Africa is underrepresented in studies investigating the relative importance of pests and pollinators for crop productivity and how this might be influenced by surrounding landscapes or agroecological practices. In Malawi, we selected 24 smallholder farms differing in landscape-scale shrubland cover, implementation of manual pest removal as an indicator of an agroecological pest management practice, and the number of agroecological soil practices employed at the household level, such as mulching, intercropping, and soil conservation tillage. We established pumpkin plots and assessed the abundance and richness of flower visitors and damage of flowers (florivory) caused by pest herbivores on flowers. Using a full-factorial hand pollination and exclusion experiment on each plot, we investigated the relative contribution of pollination and florivory to pumpkin yield. Increasing shrubland cover decreased honeybee abundance but increased the abundance and richness of non-honeybee visitors. Manual removal of herbivores considered to be pests reduced flower visitors, whereas more agroecological soil management practices increased flower visitors. Neither shrubland cover nor agroecological management affected florivory. Pollinator limitation, but not florivory, constrained pumpkin fruit set, and increasing visitor richness decreased the relative differences between hand- and animal-pollinated flowers. We recommend improved protection of shrubland habitats and increasing agroecological soil practices to promote pollinator richness on smallholder farms.</p>
Determinants of climate-smart agriculture adoption and crop productivity among smallholder farmers in Nyimba district, Zambia
<p>Data was collected among smallholder farmers' households in the Nyimba district of Zambia in a view to find determinants for crop productivity and adoption of climate-smart agriculture practices. </p> <p> </p>
No evidence for trade-offs between bird diversity, yield and water table depth on oil palm smallholdings: implications for tropical peatland landscape restoration
<p>Tropical peat swamp forests retain large carbon stocks and support unique biodiversity, but clearance and drainage for agriculture have resulted in fires, carbon emissions and biodiversity losses. Initiatives to re-wet cultivated peatlands may benefit biodiversity if this protects remaining forests from fire and agricultural encroachment, but there are concerns that re-wetting could reduce yields and damage livelihoods, as relationships between drainage, on-farm biodiversity, and crop yields have not been studied.</p> <p>We examined oil palm fruit yields and bird diversity on 41 smallholder farms in Jambi (Sumatra, Indonesia), which varied in drainage intensity (12-month mean water table per plot from August 2018 to August 2019: -52 to -3 cm below ground). We also compared farm bird diversity with a neighbouring area of protected forest (11,000 ha, 21 plots; mean water table per plot -3 to +15 cm).</p> <p>Bird species richness (3-18 species per plot), species composition, and oil palm yields (4.5-19.2 t fresh fruit bunch ha-1 yr-1) varied among farms, but were not detectably affected by water table depth, although ground-level vegetation was more complex on wetter farms. Bird richness in oil palm (mean = 10.3 species per plot) was <50% of that in forest (26 species per plot), and only three out of 35 conservation-priority species found in forest were recorded in oil palm.</p> <p>Synthesis & applications: Tropical peatlands in Indonesia have been drained to allow farmer access and improve farm yields, but we found no trade-offs between drainage depth, yields or bird diversity on smallholder oil palm farms in our study landscape. Current restoration initiatives to re-wet peat may benefit farmers by reducing fire risk, without affecting yields. Wetter farms had increased understorey vegetation complexity, but this did not affect bird diversity, so we find no evidence that re-wetting improves on-farm biodiversity within the studied range of drainage depths. However, on-farm fire reduction efforts in cultivated peatlands, including re-wetting, will be vital for reducing the risk of fires escaping into nearby forests, which contain unique and diverse bird species assemblages. Protection of remaining peatland forests from fire and clearance is key for biodiversity conservation, and for providing a source of seed dispersers and genetic material for future forest and landscape restoration efforts. Restoration of more biodiversity-friendly land covers will improve landscape permeability and help conserve species and the ecosystem services they deliver.</p>
Data from: A mix of old British and modern European breeds: Genomic prediction of breed composition of smallholder pigs in Uganda
<p>Pig herds in Africa comprise genotypes ranging from local ecotypes to commercial breeds. Many animals are composites of these two types and the best levels of crossbreeding for particular production systems are largely unknown. These pigs are managed without structured breeding programs and inbreeding is potentially limiting. The objective of this study was to quantify ancestry contributions and inbreeding levels in a population of smallholder pigs in Uganda. The study was set in the districts of Hoima and Kamuli in Uganda and involved 422 pigs. Pig hair samples were taken from adult and growing pigs in the framework of a longitudinal study investigating productivity and profitability of smallholder pig production. The samples were genotyped using the porcine GeneSeek Genomic Profiler (GGP) 50K SNP Chip. The SNP data was analyzed to infer breed ancestry and autozygosity of the Uganda pigs. The results showed that exotic breeds (modern European and old British) contributed an average of 22.8% with a range of 2–50% while "local" blood contributed 69.2% (36.9–95.2%) to the ancestry of the pigs. Runs of homozygosity (ROH) greater than 2 megabase (Mb) quantified the average genomic inbreeding coefficient of the pigs as 0.043. The scarcity of long ROH indicated low recent inbreeding. We conclude that the genomic background of the pig population in the study is a mix of old British and modern pig ancestries. Best levels of admixture for smallholder pigs are yet to be determined, by linking genotypes and phenotypic records.</p>
Data from: Local and landscape scale woodland cover and diversification of agroecological practices shape butterfly communities in tropical smallholder landscapes
<p>The conversion of biodiversity-rich woodland to farmland and subsequent management has strong, often negative, impacts on biodiversity. In tropical smallholder agricultural landscapes, the impacts of agriculture on insect communities, both through habitat change and subsequent farmland management, is understudied. The use of agroecological practices has social and agronomic benefits for smallholders. Although ecological co-benefits of agroecological practices are assumed, systematic empirical assessments of biodiversity effects of agroecological practices are missing, particularly in Africa.</p> <p>In Malawi, we assessed butterfly abundance, species richness, species assemblages and community life-history traits on 24 paired woodland and smallholder-managed farmland sites located across a gradient of woodland cover within a 1 km radius. We tested whether habitat type (woodland vs. farmland) and woodland cover at the landscape scale interactively shaped butterfly communities. Farms varied in the implementation of agroecological pest and soil management practices and flowering plant species richness.</p> <p>Farmland had lower butterfly abundances and approximately half the species richness than woodland. Farmland butterfly communities had, on average, a larger wingspan than woodland site communities. Surprisingly, higher woodland cover in the landscape had no effect on butterfly abundance in both habitats. In contrast, species richness was higher with higher woodland cover. Butterfly species assemblages were distinct between wood- and farmland and shifted across the woodland cover gradient.</p> <p>Farmland butterfly abundance, but not species richness, was higher with higher flowering plant species richness on farms. Farms with a higher number of agroecological pest management practices had a lower abundance of the dominant butterfly species, but not of rarer species. However, a larger number of agroecological soil management practices was associated with a higher abundance of rarer species. </p> <p><em>Synthesis and applications</em>: We show that diversified agroecological soil practices and flowering plant richness enhanced butterfly abundance on farms. However, our results suggest that on-farm measures cannot compensate for the negative effects of continued woodland conversion. Therefore, we call for more active protection of remaining African woodlands in tandem with promoting agroecological soil management practices and on-farm flowering plant richness to conserve butterflies whilst benefiting smallholders.</p>
Pollinator interactions of native and introduced plants in smallholder tropical orchards across a gradient of anthropogenic landscapes
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Data from: Local and landscape scale woodland cover and diversification of agroecological practices shape butterfly communities in tropical smallholder landscapes
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Data from: The importance of shrubland and local agroecological practices for pumpkin production in sub-Saharan smallholdings
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Proximity to forest mediates trade-offs between yields and biodiversity of birds in oil palm smallholdings
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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