Smallholder Fields Research Articles

Mycorrhizal fungi-mediated uptake of tree-derived nitrogen by maize in smallholder farms.

Trees within farmers' fields can enhance systems' longer-term productivity e.g., via nutrient amelioration, which is indispensable to attain sustainable agroecosystems. While arbuscular mycorrhizal fungi (AMF) are known to improve plant access to soil nutrients, the potential of AMF to mediate nutrient uptake of tree-derived N by crops from beyond the crops' rooting zones is unclear. We hypothesized that AMF quantitatively contribute to the crop uptake of tree-derived N. We set up root and AMF exclusion and control plots around faidherbia trees (Faidherbia albida) and used the 15N natural abundance technique to determine the magnitude of AMF-mediated uptake of tree-derived N by maize from beyond its rooting zone in smallholder fields. We further tested whether AMF-mediated N uptake decreases with distance-from-tree. We show that within one cropping season, maize obtained approximately 35 kg biologically fixed N ha-1 from faidherbia. One third of tree-derived N in maize leaves was attributed to AMF-mediated N uptake from beyond the maize rooting zone and two thirds to N from tree leaf litter, regardless of distance-from-tree. As hypothesized, maize grown close (1 m) to faidherbia obtained significantly more tree-derived N than at further distances (4 and 5 m). Thus, the faidherbia-AMF association can enhance agroecosystem functioning.

Perennial legume intercrops provide multiple belowground ecosystem services in smallholder farming systems

Soil degradation is a major underlying cause of poverty and malnutrition in smallholder agrarian communities across the globe. Legume diversification, through polyculture or intercropping, is a strategy that increases yields and income while improving family nutrition. However, the potential for these legume-cereal intercrops to restore soil functions in smallholder fields remains uncertain, with many studies failing to detect increases in soil organic matter (SOM). In sub-Saharan Africa, smallholders typically grow maize either intercropped with short-lived annual food legumes or as a monoculture. Recently, a novel maize system which includes perennial legumes in the genus Desmodium as intercrops, known as “push-pull” (hereafter, PP) has been increasingly adopted by smallholders across the region. The yield, nutritional and economic benefits of the PP system are well-documented. We investigated the effects of PP maize intercropping on soil fertility compared to the predominant maize culture systems (hereafter, NPP) using soils from long-term experiments and smallholder fields in western Kenya. Maize-desmodium intercrops promoted SOM accrual and increased plant available phosphorus. In the long-term research station experiments soil organic N was 20% greater and labile organic N reserves were five-fold greater in PP compared to NPP soils. Despite equal P fertilizer application rates, extractable soil P was two-fold greater in PP compared to NPP soils. Soils under maize-food legume intercrops did not show any detectable accrual of SOM compared to maize monocultures. Soil benefits in smallholder PP fields tended to mirror those found in the controlled experiments. Taken together, our results demonstrate that the perennial legumes in PP system restore soil function and fertility. Developing additional well-designed intercropping systems that include perennial legumes could play a significant role in reversing the trajectory of soil degradation in smallholder farming systems while enhancing human well-being.

Methodology of Analyzing Maize Density Loss in Smallholder’s Fields and Potential Optimize Approach

Increasing plant density is a key measure to close the maize (Zea mays L.) yield gap and ensure food security. However, there is a large plant density difference in the fields sown by agronomists and smallholders. The primary cause of this phenomenon is the lack of an effective methodology to systematically analyze the density loss. To identify the plant density loss processes from experimental plots to smallholder fields, a research methodology was developed in this study involving a farmer survey and measurements in a smallholder field. The results showed that the sowing density difference caused by farmer decision-making and plant density losses caused by mechanical and agronomic factors explained 15.5%, 5.5% and 6.8% of the plant density difference, respectively. Changing smallholder attitudes toward the value of increasing the plant density could help reduce this density loss and increase farm yields by 12.3%. Therefore, this methodology was effective for analyzing the plant density loss, and to clarify the primary causes of sowing density differences and plant density loss. Additionally, it was beneficial to identify the priorities and stakeholders who share responsibility for reducing the density loss. The methodology has wide applicability to address the sowing density differences and plant density loss in other areas to narrow crop yield gaps and ensure food security.

Diversity and population densities of plant-parasitic nematodes in commercial and smallholder pineapple production in Kenya

Summary Plant-parasitic nematodes (PPN) cause significant yield reduction in commercial pineapple (Ananas comosus) worldwide. In Kenya, few nematode studies have been conducted, although the main commercial pineapple producer has sole dispensation to use Telone II (1,3-Dichloropropene) indicating the magnitude of the nematode problem. This study was conducted with the aim to investigate the population densities and diversity of nematodes in two commercial plantations with two contrasting management practices. We additionally assessed the influence of crop age and compared this with nearby smallholder pineapple production systems. Soil and root samples were collected from fields of different ages in each commercial plantation and from 29 smallholder fields. A total of 18 genera were associated with pineapple, with a relatively greater diversity found in smallholder than commercial farms. The most prevalent genus was Meloidogyne spp. (M. javanica) followed by Helicotylenchus spp., Tylenchus spp. and Aphelenchoides spp. PPN densities were higher in relatively older fields of 24 and 36 months than from fallow and 3-month-old fields. Regression analysis additionally demonstrated the rise of PPN densities with age of pineapple fields, especially Meloidogyne spp., while free-living nematode densities declined. This study provides an indication of the high level of PPN infection in pineapple in Kenya, which would constitute an important factor contributing to low yields. The study confirms an obvious need for pineapple producers to control PPN to improve crop yields.

Evaluating the Effectiveness of Picture-Based Agricultural Extension Lessons Developed Using Participatory Testing and Editing with Smallholder Women Farmers in Nepal

Printed pictures are traditional forms of agricultural extension for smallholder farmers. They receive historical academic criticism but remain inexpensive, do not require technical skills (unlike smartphones), and bypass language/literacy barriers. Here, a comprehensive participatory pipeline is described that included 56 Nepalese women farmer editors to develop 100 picture-based lessons. Thereafter, the Theory of Planned Behavior is used as a framework to evaluate 20 diverse lessons using quantitative and qualitative data (Nvivo-11) collected from four groups, focusing on low-literacy women: the women farmer editors (n = 56); smallholder field testers who had prior exposure to extension agents and the actual innovations (control group, n = 120), and those who did not (test group, n = 60); expert stakeholders (extension agents/scientists, n = 25). The expected comprehension difference between farmer groups was non-substantive, suggesting that the participatory editing/testing approaches were effective. There were surprising findings compared to the academic literature: smallholders comprehended the pictures without the help of extension agents, perhaps because of the participatory approaches used; children assisted their mothers to understand caption-based lessons; the farmers preferred printed pictures compared to advanced information and communication technologies (ICTs); and the resource-poor farmers were willing to pay for the printed materials, sufficient to make them cost-neutral/scalable. These findings have implications for smallholder farmers beyond Nepal.

Fusion Approach for Remotely-Sensed Mapping of Agriculture (FARMA): A Scalable Open Source Method for Land Cover Monitoring Using Data Fusion

The increasing availability of very-high resolution (VHR; <2 m) imagery has the potential to enable agricultural monitoring at increased resolution and cadence, particularly when used in combination with widely available moderate-resolution imagery. However, scaling limitations exist at the regional level due to big data volumes and processing constraints. Here, we demonstrate the Fusion Approach for Remotely-Sensed Mapping of Agriculture (FARMA), using a suite of open source software capable of efficiently characterizing time-series field-scale statistics across large geographical areas at VHR resolution. We provide distinct implementation examples in Vietnam and Senegal to demonstrate the approach using WorldView VHR optical, Sentinel-1 Synthetic Aperture Radar, and Sentinel-2 and Sentinel-3 optical imagery. This distributed software is open source and entirely scalable, enabling large area mapping even with modest computing power. FARMA provides the ability to extract and monitor sub-hectare fields with multisensor raster signals, which previously could only be achieved at scale with large computational resources. Implementing FARMA could enhance predictive yield models by delineating boundaries and tracking productivity of smallholder fields, enabling more precise food security observations in low and lower-middle income countries.

Enhanced mapping of a smallholder crop farming landscape through image fusion and model stacking

ABSTRACT Globally, Smallholder farming systems (SFS) are recognized as one of the most important pillars of rural economic development and poverty alleviation because of their contribution to food security. However, support for this agricultural sector is hampered by lack of reliable information on the distributions and acreage of smallholder fields. This information is essential in not only monitoring food security and informing markets but also in guiding the determination of levels of support required from government by individual farmers. There is urgent need for robust techniques that can be used to cost-effectively and time-efficiently map smallholder crop fields especially in Sub-Saharan Africa and Asia. This study attempts to do this by using an approach in which optical and Synthetic Aperture Radar (SAR) data are systematically combined and classified using Extreme Gradient Boosting (Xgboost). We also investigated model stacking as another technique to improve classification accuracy. We combined Xgboost with Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Networks (ANN), and Naïve Bayes (NB). The combined use of multi-temporal Sentinel-2 bands, spectral indices, and Sentinel-1 produced better results than exclusive use of optical data (α = 0.95, p = 0.0005). Furthermore, stacking of classification algorithms based on model comparisons achieved higher accuracy than stacking the algorithms indiscriminately (α = 0.95, p = 0.0100). Through systematic fusion of SAR and optical data and hyper-parameter tuning of Xgboost, we achieved a maximum classification accuracy of 97.71%, while achieving a maximum accuracy of 96.06% through model stacking. This highlights the importance of multi-sensor data fusion and multi-classifier systems when mapping fragmented agricultural landscapes.

Year-to-year crop shifts promote weed diversity in tropical permanent rainfed cultivation

In the past decades, the expansion and modernisation of agriculture in the mountainous areas of Southeast Asia has had severe impacts on biodiversity, as the once species-rich forests were turned into homogeneous fields receiving ample external inputs. A common feature of permanent cropping with annual crops is the frequent change of crop choice, depending on market opportunities or other motives. However, the precise effect of crop shifts on weeds in tropical areas is largely unknown. In this study, we investigated the short-term effect of crop sequences on the diversity of weed communities in smallholder fields of Northern Thailand. Crop choices were upland rice, maize, fallow and young tree plantations with or without intercrop. We counted the number of crop shifts and the number of crops involved during a 3-years period preceding weed sampling. We showed that the number of crop shifts did not affect weed density and biomass. However, herbaceous species number and diversity (measured as Shannon index) increased by 36% and 46% respectively, while herbaceous species dominance decreased by 38%, in fields with yearly crop shifts compared to fields with no shifts in the previous three years. The effect of a particular crop on diversity, or the effect of intercropping with young trees, was weaker. It was likely due to the more variable resources (especially light) in fields with two crop shifts, allowing species with different niches to co-exist. Crop type and frequent crop shifts did not affect shrub and tree species number, diversity or dominance. Some species were strongly associated with fields with no crop shift in the sequence (e.g. the tree Antidesma velutinosum) or to fields with two crop shifts in the sequence (e.g. the herb Centella asiatica, the C4 grass Digitaria radicosa). Overall, this study showed that in this agronomical system, maintaining yearly crop shifts does not significantly affect weed abundance, but supports in-field plant species diversity, which is likely to impact the services provisioned by tropical mountainous agro-ecosystems.

Estimating Yields of Household Fields in Rural Subsistence Farming Systems to Study Food Security in Burkina Faso

Climate change has an increasing impact on food security and child nutrition, particularly among rural smallholder farmers in sub-Saharan Africa. Their limited resources and rainfall dependent farming practices make them sensitive to climate change-related effects. Data and research linking yield, human health, and nutrition are scarce but can provide a basis for adaptation and risk management strategies. In support of studies on child undernutrition in Burkina Faso, this study analyzed the potential of remote sensing-based yield estimates at household level. Multi-temporal Sentinel-2 data from the growing season 2018 were used to model yield of household fields (median 1.4 hectares (ha), min 0.01 ha, max 12.6 ha) for the five most prominent crops in the Nouna Health and Demographic Surveillance (HDSS) area in Burkina Faso. Based on monthly metrics of vegetation indices (VIs) and in-situ harvest measurements from an extensive field survey, yield prediction models for different crops of high dietary importance (millet, sorghum, maize, and beans) were successfully generated producing R² between 0.4 and 0.54 (adj. R² between 0.32 and 0.5). The models were spatially applied and resulted in a yield estimation map at household level, enabling predictions of up to 2 months prior to harvest. The map links yield on a 10-m spatial resolution to households and consequently can display potential food insecurity. The results highlight the potential for satellite imagery to provide yield predictions of smallholder fields and are discussed in the context of health-related studies such as child undernutrition and food security in rural Africa under climate change.

Sustainable options for reducing carbon inputs and improving the eco-efficiency of smallholder wheat-maize cropping systems in the Huanghuaihai Farming Region of China

The large inputs to the winter wheat-summer maize cropping systems (WMSs) in the Huanghuaihai Farming Region (HFR) of China have increased resource consumption and pollution risk. The purposes of the current study were to seek effective ways to reduce the negative impacts of smallholder systems on the environment by using the life cycle assessment (LCA) method and data from field investigations and high-yield demonstration fields with WMSs in four representative counties across the HFR. High-yield demonstration fields with optimized fertilizer management had a higher carbon balance (CB), lower carbon intensity and higher fertilizer use efficiency than smallholder fields. The CB and carbon emissions differed among counties due to differences in input and output levels. Fertilizer application contributed the most to carbon emissions from WMSs in the HFR, accounting for 34.5% of the total carbon emissions. Thus, an efficient way to reduce the carbon footprint is to reduce the difference in nitrogen application between farmers’ and high-yield fields. The dominant WMS production unit in the HFR is smallholding, which requires large resource inputs and high carbon consumption for high yield. Therefore, optimizing the carbon input management of farmers’ fields, especially the management of fertilizers, is crucial for promoting high-yield, low-carbon production and improving ecological benefits for WMS smallholders in the HFR, which will further promote clean and sustainable agricultural production in this region.

Micro-Spatial Analysis of Maize Yield Gap Variability and Production Factors on Smallholder Farms

Site-specific land management practice taking into account variability in maize yield gaps (the difference between yields in the 90th percentiles and other yields on smallholder farmers’ fields) could improve resource use efficiency and enhance yields. However, the applicability of the practice is constrained by inability to identify patterns of resource utilization to target application of resources to more responsive fields. The study focus was to map yield gaps on smallholder fields based on identified spatial arrangements differentiated by distance from the smallholder homestead and understand field-specific utilization of production factors. This was aimed at understanding field variability based on yield gap mapping patterns in order to enhance resource use efficiency on smallholder farms. The study was done in two villages, Mukuyu and Shikomoli, with high and low agroecology regarding soil fertility in Western Kenya. Identification of spatial arrangements at 40 m, 80 m, 150 m and 300 m distance from the homestead on smallholder farms for 70 households was done. The spatial arrangements were then classified into near house, mid farm and far farm basing on distance from the homestead. For each spatial arrangement, Landsat sensors acquired via satellite imagery were processed to generate yield gap maps. The focal statistics analysis method using the neighborhoods function was then applied to generate yield gap maps at the different spatial arrangements identified above. Socio-economic, management and biophysical factors were determined, and maize yields estimated at each spatial arrangement. Heterogeneous patterns of high, average and low yield gaps were found in spatial arrangements at the 40 m and 80 m distances. Nearly homogenous patterns tending towards median yield gap values were found in spatial arrangements that were located at the 150 m and 300 m. These patterns correspondingly depicted field-specific utilization of management and socio-economic factors. Field level management practices and socio-economic factors such as application of inorganic fertilizer, high frequency of weed control, early land preparation, high proportion of hired and family labor use and allocation of large land sizes were utilized in spatial arrangements at 150 and 300 m distances. High proportions of organic fertilizer and family labor use were utilized in spatial arrangements at 40 and 80 m distances. The findings thus show that smallholder farmers preferentially manage the application of socio-economic and management factors in spatial arrangements further from the homestead compared to fields closer to the homestead which could be exacerbating maize yield gaps. Delineating management zones based on yield gap patterns at the different spatial arrangements on smallholder farms could contribute to site-specific land management and enhance yields. Investigating the value smallholder farmers attach to each spatial arrangement is further needed to enhance the spatial understanding of yield gap variation on smallholder farms.

Delineation of agricultural fields in smallholder farms from satellite images using fully convolutional networks and combinatorial grouping

Accurate spatial information of agricultural fields in smallholder farms is important for providing actionable information to farmers, managers, and policymakers. Very High Resolution (VHR) satellite images can capture such information. However, the automated delineation of fields in smallholder farms is a challenging task because of their small size, irregular shape and the use of mixed-cropping systems, which make their boundaries vaguely defined. Physical edges between smallholder fields are often indistinct in satellite imagery and contours need to be identified by considering the transition of the complex textural pattern between fields. In these circumstances, standard edge-detection algorithms fail to extract accurate boundaries. This article introduces a strategy to detect field boundaries using a fully convolutional network in combination with a globalisation and grouping algorithm. The convolutional network using an encoder-decoder structure is capable of learning complex spatial-contextual features from the image and accurately detects sparse field contours. A hierarchical segmentation is derived from the contours using the oriented watershed transform and by iteratively merging adjacent regions based on the average strength of their common boundary. Finally, field segments are obtained by adopting a combinatorial grouping algorithm exploiting the information of the segmentation hierarchy. An extensive experimental analysis is performed in two study areas in Nigeria and Mali using WorldView-2/3 images and comparing several state-of-the-art contour detection algorithms. The algorithms are compared based on the precision-recall accuracy assessment strategy which is tolerating small localisation errors in the detected contours. The proposed strategy shows promising results by automatically delineating field boundaries with F-scores higher than 0.7 and 0.6 on our two test areas, respectively, outperforming alternative techniques.

Monitoring crop phenology using a smartphone based near-surface remote sensing approach

Smallholder farmers play a critical role in supporting food security in developing countries. Monitoring crop phenology and disturbances to crop growth is critical in strengthening farmers’ ability to manage production risks. This study assesses the feasibility of using crowdsourced near-surface remote sensing imagery to monitor winter wheat phenology and identify damage events in northwest India. In particular, we demonstrate how streams of pictures of individual smallholder fields, taken using inexpensive smartphones, can be used to quantify important phenological stages in agricultural crops, specifically the wheat heading phase and how it can be used to detect lodging events, a major cause of crop damage globally. Near-surface remote sensing offers granular visual field data, providing detailed information on the timing of key developmental phases of winter wheat and crop growth disturbances that are not registered by common satellite remote sensing vegetation indices or national crop cut surveys. This illustrates the potential of near-surface remote sensing as a scalable platform for collecting high-resolution plot-specific data that can be used in supporting crop modeling, extension and insurance schemes to increase resilience to production risk and enhance food security in smallholder agricultural systems.

Effectiveness of hermetic technologies in limiting aflatoxin B1 and fumonisin B1 contamination of stored maize grain under smallholder conditions in Zimbabwe

There is limited empirical evidence on the efficacy of hermetic storage containers in reducing mycotoxin occurrence in stored maize grain under smallholder field conditions. Levels of aflatoxin B1 (AFB1) and fumonisin B1 (FB1) in maize samples collected from hermetic metal silos (148), hermetic grain bags (121) and conventional stores (179) during 2015 and 2016 storage seasons in two rural districts of Zimbabwe were assessed. AFB1 was determined using high performance liquid chromatography with post-column derivatisation and fluorescence detection, whilst FB1 was determined using direct competitive ELISA. All maize samples collected at harvest in 2015 and 2016 seasons contained FB1 at levels ranging from 10 to 462 μg/kg and 13 to 537 μg/kg, respectively. Use of hermetic containers did not seem to have any effect on the development of FB1 in stored maize grain, as there was no significant difference (P>0.05) in the increase of FB1 contamination in hermetic and conventional stores. Prior to storage, the levels of AFB1 in the maize ranged from below the limit of quantitation (LOQ) to 25.0 μg/kg, whilst levels during storage ranged from <LOQ to 8.60 μg/kg in hermetic silos, <LOQ to 8.37 μg/kg in hermetic bags and <LOQ to 791 μg/kg in conventional stores over the two storage seasons. The occurrence of AFB1 in maize stored in hermetic containers, was significantly (P<0.05) lower than that in conventional stores. Hermetic containers were more effective than conventional stores in limiting contamination of maize with AFB1 and subsequent human exposure to these toxins. Therefore, hermetic storage containers are recommended to smallholder maize producers for safe and effective limitation of AFB1 contamination during storage and hence reduce exposure among consumers.

Assessment of potential environmental risks of transgene flow in smallholder farming systems in Asia: Brassica napus as a case study in Korea

The cultivation of genetically modified (GM) crops has raised many questions regarding their environmental risks, particularly about their ecological impact on non-target organisms, such as their closely-related relative species. Although evaluations of transgene flow from GM crops to their conventional crops has been conducted under large-scale farming system worldwide, in particular in North America and Australia, few studies have been conducted under smallholder farming systems in Asia with diverse crops in co-existence. A two-year field study was conducted to assess the potential environmental risks of gene flow from glufosinate-ammonium resistant (GR) Brassica napus to its conventional relatives, B. napus, B. juncea, and Raphanus sativus under simulated smallholder field conditions in Korea. Herbicide resistance and simple sequence repeat (SSR) markers were used to identify the hybrids. Hybridization frequency of B. napus × GR B. napus was 2.33% at a 2 m distance, which decreased to 0.007% at 75 m. For B. juncea, it was 0.076% at 2 m and decreased to 0.025% at 16 m. No gene flow was observed to R. sativus. The log-logistic model described hybridization frequency with increasing distance from GR B. napus to B. napus and B. juncea and predicted that the effective isolation distances for 0.01% gene flow from GR B. napus to B. napus and B. juncea were 122.5 and 23.7 m, respectively. Results suggest that long-distance gene flow from GR B. napus to B. napus and B. juncea is unlikely, but gene flow can potentially occur between adjacent fields where the smallholder farming systems exist.

Assessing yield and fertilizer response in heterogeneous smallholder fields with UAVs and satellites

Agricultural intensification and efficient use and targeting of fertilizer inputs on smallholder farms is key to sustainably improve food security. The objective of this paper is to demonstrate how high-resolution satellite and unmanned aerial vehicle (UAV) images can be used to assess the spatial variability of yield, and yield response to fertilizer. The study included 48 and 50 smallholder fields monitored during the 2014 and 2015 cropping seasons south-east of Koutiala (Mali), cropped with the five major crops grown in the area (cotton, maize, sorghum, millet and peanuts). Each field included up to five plots with different fertilizer applications and one plot with farmer practice. Fortnightly, in-situ in each field data were collected synchronous with UAV imaging using a Canon S110 NIR camera. A concurrent series of very high-resolution satellite images was procured and these images were used to mask out trees. For each plot, we calculated vegetation index means, medians and coefficients of variation. Cross-validated general linear models were used to assess the predictability of relative differences in crop yield and yield response to fertilizer, explicitly accounting for the effects of fertility treatments, between-field and within-field variabilities. Differences between fields accounted for a much larger component of variation than differences between fertilization treatments.Vegetation indices from UAV images strongly related to ground cover (R2 = 0.85), light interception (R2 = 0.79) and vegetation indices derived from satellite images (R2 values of about 0.8). Within-plot distributions of UAV-derived vegetation index values were negatively skewed, and within-plot variability of vegetation index values was negatively correlated with yield. Plots on shallow soils with poor growing conditions showed the largest within-plot variability. GLM models including UAV derived estimates of light interception explained up to 78% of the variation in crop yield and 74% of the variation in fertilizer response within a single field. These numbers dropped to about 45% of the variation in yield and about 48% of the variation in fertilizer response when lumping all fields of a given crop, with Q2 values of respectively 22 and 40% respectively when tested with a leave-field-out procedure. This indicates that remotely sensed imagery doesn’t fully capture the influence of crop stress and management. Assessment of crop fertilizer responses with vegetation indices therefore needs a reference under similar management. Spatial variability in UAV-derived vegetation index values at the plot scale was significantly related to differences in yields and fertilizer responses. The strong relationships between light interception and ground cover indicate that combining vertical photographs or high-resolution remotely sensed vegetation indices with crop growth models allows to explicitly account for the spatial variability and will improve the accuracy of yield and crop production assessments, especially in heterogeneous smallholder conditions.

Contribution of Agroforestry Tress for Integrated Nutrient Management to Improve Maize Yield on Smallholder Fields

Contribution of Agroforestry Tress for Integrated Nutrient Management to Improve Maize Yield on Smallholder Fields

A generalized computer vision approach to mapping crop fields in heterogeneous agricultural landscapes

Abstract Smallholder farms dominate in many parts of the world, particularly Sub-Saharan Africa. These systems are characterized by small, heterogeneous, and often indistinct field patterns, requiring a specialized methodology to map agricultural land cover. Using a variety of sites in South Africa, we present a new approach to mapping agricultural fields, based on efficient extraction of a vast set of simple, highly correlated, and interdependent features, followed by a random forest classifier. We achieved similar high performance across agricultural types, including the spectrally indistinct smallholder fields as well as the more easily distinguishable commercial fields, and demonstrated the ability to generalize performance across large geographic areas. In sensitivity analyses, we determined multi-temporal information provided greater gains in performance than the addition of multi-spectral bands available in DigitalGlobe Worldview-2 imagery.

Molecular Determination and Characterization of Phytoplasma 16S rRNA Gene in Selected Wild Grasses from Western Kenya

Napier grass (Pennisetum purpuruem) production for zero grazing systems has been reduced to rates of up to 90% in many smallholder fields by the Napier stunt (Ns) disease caused by phytoplasma sub-group 16SrXI in western Kenya. It is hypothesized that several other wild grasses in Kenya could be infected by phytoplasmas that would otherwise pose a significant threat to Napier, other important feeds and food crops. This study therefore sought to detect and identify phytoplasma strains infecting wild grasses in western Kenya using 16S ribosomal RNA (ribonucleic acid) gene as well as identify wild grass species hosting phytoplasmas in 646 wild grass samples that were collected in October 2011 and January 2012 during a random crossectional survey conducted in Bungoma and Busia counties of western Kenya. DNA was extracted and nested polymerase reaction (nPCR) used to detect phytoplasmas. Two sub-groups of phytoplasmas were detected in eight grass species observed to grow near infected Napier fields. Only one of the two phytoplasmas reported was related to the Ns phytoplasma. There was a strong association between proportions of phytoplasma infection and the grass species collected (p = 0.001). C. dactylon, D. scalarum, B. brizantha, poverty grass and P. maximum had high proportions of infection and were abundantly distributed in western Kenya hence considered wild phytoplasma hosts. E. indica and C. ciliaris were scarcely distributed and had low infection rates. There was statistically significant difference in proportions of infection per location of survey (p = 0.001). Phytoplasma subgroups 16SrXI and 16SrXIV were the only phytoplasma genotypes distributed among wild grasses in western Kenya. Phytoplasma subgroup 16SrXIV predominantly infects only C. dactylon and B. brizantha grasses while phytoplasma subgroup 16SrXI is broad spectrum and infects a large number of wild grasses. In general, there is a diversity of wild grasses hosting phytoplasmas in western Kenya. These host grasses may be the reason for the high rates observed in the spread of Ns disease in western Kenya by acting as reservoirs for Ns phytoplasma.

<b>Soil weed seed bank <i>in situ</i> and <i>ex situ</i> at a smallholder field in Maranhão State, northeastern Brazil

The objective of this research was to assess the density, floristic composition, phytosociology and diversity of a soil weed seed bank ex situ by germination in a greenhouse and in situ by weed sampling on a smallholder corn field located in Lago Verde County, Maranhão State. Fifteen pairs of 25 m2 plots were designated. In half of these plots, 90 soil samples were collected with an open metal template measuring 25 x 16 x 3 cm and placed in a greenhouse to germinate. In the other half, 90 weed samples were collected using the same metal template. We recorded a total of 1,998 individuals from 40 species, 31 genera and 16 families, from which 659 individuals germinated in situ and 1,339 exsitu. Density was higher ex situ, with 372 plants m-2. The Cyperaceae family had the highest floristic richness with nine species, followed by the Poaceae with six. The dominant species based on the Importance Value Index were Lindernia crustacea (IVI 27.7%) in situ and Scleria lithosperma (IVI 37.0%) ex situ. Floristic diversity was higher ex situ, with H’ = 2.66 nats ind-1. These results could help predict infestation potential and could lead to improved weed management strategies in corn-growing areas on smallholdings in Maranhão State, northeastern Brazil.