Leguminous cover crops differentially affect maize yields in three contrasting soil types of Kakamega, Western Kenya

Maize production in smallholder farming systems in Kenya is largely limited by low soil fertility. As mineral fertilizer is expensive, green manuring using leguminous cover crops could be an alternative strategy for farmers to enhance farm productivity. However due to variability in soil type and crop management, the effects of green manure are likely to differ with farms. The objectives of this study were to evaluate Mucuna pruriens and Arachis pintoi on (i) biomass and nitrogen fixation (^15N natural abundance), (ii) soil carbon and nitrogen stocks and (iii) their effects on maize yields over two cropping seasons in Kakamega, Western Kenya. Mucuna at 6 weeks accumulated 1–1.3 Mg ha^{-1} of dry matter and 33–56 kg ha^{-1} nitrogen of which 70% was nitrogen derived from the atmosphere (Ndfa). Arachis after 12 months accumulated 2–2.7 Mg ha^{-1} of dry matter and 51–74 kg N ha^{-1} of which 52-63 % was from Ndfa. Soil carbon and nitrogen stocks at 0–15 cm depth were enhanced by 2-4 Mg C ha^{-1} and 0.3–1.0 Mg N ha^{-1} under Mucuna and Arachis fallow, irrespective of soil type. Maize yield increased by 0.5-2 Mg ha^{-1} in Mucuna and 0.5–3 Mg ha^{-1} in Arachis and the response was stronger on Nitisol than on Acrisol or Ferralsol. We concluded that leguminous cover crops seem promising in enhancing soil fertility and maize yields in Kenya, provided soil conditions and rainfall are suitable.

The chemical variability at the surface of Mars: implications for exogenic processes

The chemical composition of sediments and rocks, as well as their distribution at the Martian surface, represent a long term archive of processes, which have formed the planetary surface. A survey of chemical compositions by means of Compositional Data Analysis represents a valuable tool to extract direct evidence for weathering processes and allows to quantify weathering and sedimentation rates. clr-biplot techniques are applied for visualization of chemical relationships across the surface (“chemical maps”). The variability among individual suites of data is further analyzed by means of clr-PCA, in order to extract chemical alteration vectors between fresh rocks and their crusts and for an assessment of different source reservoirs accessible to soil formation. Both techniques are applied to elucidate the influence of remote weathering by combined analysis of several soil forming branches. Vector analysis in the Simplex provides the opportunity to study atmosphere surface interactions, including the role and composition of volcanic gases

Characterization and efficacy of bacterial strains for biological control of soil-borne diseases caused by Phytophthora cactorum and Meloidogyne javanica on Rosaceous plants

S'avaluaren 58 soques de Pseudomonas fluorescens i Pantoea agglomerans per la seva eficàcia en el biocontrol de la malaltia causada per l'oomicet Phytophthora cactorum en maduixera i pel nematode formador de gal·les Meloidogyne javanica en el portaempelt GF-677. Es desenvolupà un mètode ex vivo d'inoculació de fulla amb l'objectiu de seleccionar soques bacterianes com a agents de control biològic de P. cactorum en maduixera. Tres soques de P. fluorescens es seleccionaren com a soques eficaces en el biocontrol del patogen en fulles i en la reducció de la malaltia en plantes de maduixera. La combinació de soques semblà millorar la consistència del biocontrol en comparació amb les soques aplicades individualment. Tres soques de P. fluorescens es seleccionaren per la seva eficàcia en la reducció de la infecció de M. javanica en portaempelts GF-677. La combinació d'aquestes soques no incrementà l'eficàcia del biocontrol, però semblà reduir la seva variabilitat.

Spatial covariation of Azotobacter abundance and soil properties: A case study using the wavelet transform

The soil microflora is very heterogeneous in its spatial distribution. The origins of this heterogeneity and its significance for soil function are not well understood. A problem for understanding spatial variation better is the assumption of statistical stationarity that is made in most of the statistical methods used to assess it. These assumptions are made explicit in geostatistical methods that have been increasingly used by soil biologists in recent years. Geostatistical methods are powerful, particularly for local prediction, but they require the assumption that the variability of a property of interest is spatially uniform, which is not always plausible given what is known about the complexity of the soil microflora and the soil environment. We have used the wavelet transform, a relatively new innovation in mathematical analysis, to investigate the spatial variation of abundance of Azotobacter in the soil of a typical agricultural landscape. The wavelet transform entails no assumptions of stationarity and is well suited to the analysis of variables that show intermittent or transient features at different spatial scales. In this study, we computed cross-variograms of Azotobacter abundance with the pH, water content and loss on ignition of the soil. These revealed scale-dependent covariation in all cases. The wavelet transform also showed that the correlation of Azotobacter abundance with all three soil properties depended on spatial scale, the correlation generally increased with spatial scale and was only significantly different from zero at some scales. However, the wavelet analysis also allowed us to show how the correlation changed across the landscape. For example, at one scale Azotobacter abundance was strongly correlated with pH in part of the transect, and not with soil water content, but this was reversed elsewhere on the transect. The results show how scale-dependent variation of potentially limiting environmental factors can induce a complex spatial pattern of abundance in a soil organism. The geostatistical methods that we used here make assumptions that are not consistent with the spatial changes in the covariation of these properties that our wavelet analysis has shown. This suggests that the wavelet transform is a powerful tool for future investigation of the spatial structure and function of soil biota. (c) 2006 Elsevier Ltd. All rights reserved.

Degradation of atrazine and isoproturon in surface and sub-surface soil materials undergoing different moisture and aeration conditions

The influence of different moisture and aeration conditions on the degradation of atrazine and isoproturon was investigated in environmental samples aseptically collected from surface and sub-surface zones of agricultural land. The materials were maintained at two moisture contents corresponding to just above field capacity or 90% of field capacity. Another two groups of samples were adjusted with water to above field capacity, and, at zero time, exposed to drying-rewetting cycles. Atrazine was more persistent (t(1/2) = 22-3S days) than isoproturon (t(1/2) = 5-17 days) in samples maintained at constant moisture conditions. The rate of degradation for both herbicides was higher in samples maintained at a moisture content of 90% of field capacity than in samples with higher moisture contents. The reduction in moisture content in samples undergoing desiccation from above field capacity to much lower than field capacity enhanced the degradation of isoproturon (t(1/2) = 9-12 days) but reduced the rate of atrazine degradation (t(1/2) = 23-35-days). This demonstrates the variability between different micro-organisms in their susceptibility to desiccation. Under anaerobic conditions generated in anaerobic jars, atrazine degraded much more rapidly than isoproturon in materials taken from three soil profiles (0-250 cm depth). It is suggested that some specific micro-organisms are able to survive and degrade herbicide under severe conditions of desiccation. (C) 2004 Society of Chemical Industry.

Sampling the soil in long-term forest plots: The implications of spatial variation

Long-term monitoring of forest soils as part of a pan-European network to detect environmental change depends on an accurate determination of the mean of the soil properties at each monitoring event. Forest soil is known to be very variable spatially, however. A study was undertaken to explore and quantify this variability at three forest monitoring plots in Britain. Detailed soil sampling was carried out, and the data from the chemical analyses were analysed by classical statistics and geostatistics. An analysis of variance showed that there were no consistent effects from the sample sites in relation to the position of the trees. The variogram analysis showed that there was spatial dependence at each site for several variables and some varied in an apparently periodic way. An optimal sampling analysis based on the multivariate variogram for each site suggested that a bulked sample from 36 cores would reduce error to an acceptable level. Future sampling should be designed so that it neither targets nor avoids trees and disturbed ground. This can be achieved best by using a stratified random sampling design.

Characterising errors in airborne laser altimetry data to extract soil roughness

Airborne laser altimetry has the potential to make frequent detailed observations that are important for many aspects of studying land surface processes. However, the uncertainties inherent in airborne laser altimetry data have rarely been well measured. Uncertainty is often specified as generally as 20cm in elevation, and 40cm planimetric. To better constrain these uncertainties, we present an analysis of several datasets acquired specifically to study the temporal consistency of laser altimetry data, and thus assess its operational value. The error budget has three main components, each with a time regime. For measurements acquired less than 50ms apart, elevations have a local standard deviation in height of 3.5cm, enabling the local measurement of surface roughness of the order of 5cm. Points acquired seconds apart acquire an additional random error due to Differential Geographic Positioning System (DGPS) fluctuation. Measurements made up to an hour apart show an elevation drift of 7cm over a half hour. Over months, this drift gives rise to a random elevation offset between swathes, with an average of 6.4cm. The RMS planimetric error in point location was derived as 37.4cm. We conclude by considering the consequences of these uncertainties on the principle application of laser altimetry in the UK, intertidal zone monitoring.

Modeling the plant uptake of organic chemicals, including the soil-air-plant pathway

The soil−air−plant pathway is potentially important in the vegetative accumulation of organic pollutants from contaminated soils. While a number of qualitative frameworks exist for the prediction of plant accumulation of organic chemicals by this pathway, there are few quantitative models that incorporate this pathway. The aim of the present study was to produce a model that included this pathway and could quantify its contribution to the total plant contamination for a range of organic pollutants. A new model was developed from three submodels for the processes controlling plant contamination via this pathway: aerial deposition, soil volatilization, and systemic translocation. Using the combined model, the soil−air−plant pathway was predicted to account for a significant proportion of the total shoot contamination for those compounds with log KOA > 9 and log KAW < −3. For those pollutants with log KOA < 9 and log KAW > −3 there was a higher deposition of pollutant via the soil−air−plant pathway than for those chemicals with log KOA > 9 and log KAW < −3, but this was an insignificant proportion of the total shoot contamination because of the higher mobility of these compounds via the soil−root−shoot pathway. The incorporation of the soil−air−plant pathway into the plant uptake model did not significantly improve the prediction of the contamination of vegetation from polluted soils when compared across a range of studies. This was a result of the high variability between the experimental studies where the bioconcentration factors varied by 2 orders of magnitude at an equivalent log KOA. One potential reason for this is the background air concentration of the pollutants under study. It was found background air concentrations would dominate those from soil volatilization in many situations unless there was a soil hot spot of contamination, i.e., >100 mg kg−1.

Community patterns of soil bacteria and nematodes in relation to geographic distance

Ecosystems consist of aboveground and belowground subsystems and the structure of their communities is known to change with distance. However, most of this knowledge originates from visible, aboveground components, whereas relatively little is known about how soil community structure varies with distance and if this variability depends on the group of organisms considered. In the present study, we analyzed 30 grasslands from three neighboring chalk hill ridges in southern UK to determine the effect of geographic distance (1e198 km) on the similarity of bacterial communities and of nematode communities in the soil. We found that for both groups, community similarity decayed with distance and that this spatial pattern was not related to changes either in plant community composition or soil chemistry. Site history may have contributed to the observed pattern in the case of nematodes, since the distance effect depended on the presence of different nematode taxa at one of the hill ridges. On the other hand, site-related differences in bacterial community composition alone could not explain the spatial turnover, suggesting that other factors, such as biotic gradients and local dispersal processes that we did not include in our analysis, may be involved in the observed pattern. We conclude that, independently of the variety of causal factors that may be involved, the decay in similarity with geographic distance is a characteristic feature of both communities of soil bacteria and nematodes.

Spatio-temporal surface soil heat flux estimates from satellite data: results for the AMMA experiment at the Fakara (Niger) supersite

This paper describes a method that employs Earth Observation (EO) data to calculate spatiotemporal estimates of soil heat flux, G, using a physically-based method (the Analytical Method). The method involves a harmonic analysis of land surface temperature (LST) data. It also requires an estimate of near-surface soil thermal inertia; this property depends on soil textural composition and varies as a function of soil moisture content. The EO data needed to drive the model equations, and the ground-based data required to provide verification of the method, were obtained over the Fakara domain within the African Monsoon Multidisciplinary Analysis (AMMA) program. LST estimates (3 km × 3 km, one image 15 min−1) were derived from MSG-SEVIRI data. Soil moisture estimates were obtained from ENVISAT-ASAR data, while estimates of leaf area index, LAI, (to calculate the effect of the canopy on G, largely due to radiation extinction) were obtained from SPOT-HRV images. The variation of these variables over the Fakara domain, and implications for values of G derived from them, were discussed. Results showed that this method provides reliable large-scale spatiotemporal estimates of G. Variations in G could largely be explained by the variability in the model input variables. Furthermore, it was shown that this method is relatively insensitive to model parameters related to the vegetation or soil texture. However, the strong sensitivity of thermal inertia to soil moisture content at low values of relative saturation (<0.2) means that in arid or semi-arid climates accurate estimates of surface soil moisture content are of utmost importance, if reliable estimates of G are to be obtained. This method has the potential to improve large-scale evaporation estimates, to aid land surface model prediction and to advance research that aims to explain failure in energy balance closure of meteorological field studies.

Describing seasonal variability in the distribution of daily effective temperatures for 1985-2009 compared to 1904-1984 for De Bilt, Holland

This paper proposes a method for describing the distribution of observed temperatures on any day of the year such that the distribution and summary statistics of interest derived from the distribution vary smoothly through the year. The method removes the noise inherent in calculating summary statistics directly from the data thus easing comparisons of distributions and summary statistics between different periods. The method is demonstrated using daily effective temperatures (DET) derived from observations of temperature and wind speed at De Bilt, Holland. Distributions and summary statistics are obtained from 1985 to 2009 and compared to the period 1904–1984. A two-stage process first obtains parameters of a theoretical probability distribution, in this case the generalized extreme value (GEV) distribution, which describes the distribution of DET on any day of the year. Second, linear models describe seasonal variation in the parameters. Model predictions provide parameters of the GEV distribution, and therefore summary statistics, that vary smoothly through the year. There is evidence of an increasing mean temperature, a decrease in the variability in temperatures mainly in the winter and more positive skew, more warm days, in the summer. In the winter, the 2% point, the value below which 2% of observations are expected to fall, has risen by 1.2 °C, in the summer the 98% point has risen by 0.8 °C. Medians have risen by 1.1 and 0.9 °C in winter and summer, respectively. The method can be used to describe distributions of future climate projections and other climate variables. Further extensions to the methodology are suggested.

Mean climate and variability of the atmosphere and ocean on an aquaplanet

Numerical experiments are described that pertain to the climate of a coupled atmosphere–ocean–ice system in the absence of land, driven by modern-day orbital and CO2 forcing. Millennial time-scale simulations yield a mean state in which ice caps reach down to 55° of latitude and both the atmosphere and ocean comprise eastward- and westward-flowing zonal jets, whose structure is set by their respective baroclinic instabilities. Despite the zonality of the ocean, it is remarkably efficient at transporting heat meridionally through the agency of Ekman transport and eddy-driven subduction. Indeed the partition of heat transport between the atmosphere and ocean is much the same as the present climate, with the ocean dominating in the Tropics and the atmosphere in the mid–high latitudes. Variability of the system is dominated by the coupling of annular modes in the atmosphere and ocean. Stochastic variability inherent to the atmospheric jets drives variability in the ocean. Zonal flows in the ocean exhibit decadal variability, which, remarkably, feeds back to the atmosphere, coloring the spectrum of annular variability. A simple stochastic model can capture the essence of the process. Finally, it is briefly reviewed how the aquaplanet can provide information about the processes that set the partition of heat transport and the climate of Earth.

Effects of past climate variability on fire and vegetation in the cerrãdo savanna of the Huanchaca Mesetta, NE Bolivia

Cerrãdo savannas have the greatest fire activity of all major global land-cover types and play a significant role in the global carbon cycle. During the 21st century, temperatures are projected to increase by ∼ 3 ◦C coupled with a precipitation decrease of ∼ 20 %. Although these conditions could potentially intensify drought stress, it is unknown how that might alter vegetation composition and fire regimes. To assess how Neotropical savannas responded to past climate changes, a 14 500-year, high-resolution, sedimentary record from Huanchaca Mesetta, a palm swamp located in the cerrãdo savanna in northeastern Bolivia, was analyzed with phytoliths, stable isotopes, and charcoal. A nonanalogue, cold-adapted vegetation community dominated the Lateglacial–early Holocene period (14 500–9000 cal yr BP, which included trees and C3 Pooideae and C4 Panicoideae grasses. The Lateglacial vegetation was fire-sensitive and fire activity during this period was low, likely responding to fuel availability and limitation. Although similar vegetation characterized the early Holocene, the warming conditions associated with the onset of the Holocene led to an initial increase in fire activity. Huanchaca Mesetta became increasingly firedependent during the middle Holocene with the expansion of C4 fire-adapted grasses. However, as warm, dry conditions, characterized by increased length and severity of the dry season, continued, fuel availability decreased. The establishment of the modern palm swamp vegetation occurred at 5000 cal yr BP. Edaphic factors are the first-order control on vegetation on the rocky quartzite mesetta. Where soils are sufficiently thick, climate is the second-order control of vegetation on the mesetta. The presence of the modern palm swamp is attributed to two factors: (1) increased precipitation that increased water table levels and (2) decreased frequency and duration of surazos (cold wind incursions from Patagonia) leading to increased temperature minima. Natural (soil, climate, fire) drivers rather than anthropogenic drivers control the vegetation and fire activity at Huanchaca Mesetta. Thus the cerrãdo savanna ecosystem of the Huanchaca Plateau has exhibited ecosystem resilience to major climatic changes in both temperature and precipitation since the Lateglacial period.

Towards more precise sugar beet management based on geostatistical analysis of spatial variability within fields

Within-field variation in sugar beet yield and quality was investigated in three commercial sugar beet fields in the east of England to identify the main associated variables and to examine the possibility of predicting yield early in the season with a view to spatially variable management of sugar beet crops. Irregular grid sampling with some purposively-located nested samples was applied. It revealed the spatial variability in each sugar beet field efficiently. In geostatistical analyses, most variograms were isotropic with moderate to strong spatial dependency indicating a significant spatial variation in sugar beet yield and associated growth and environmental variables in all directions within each field. The Kriged maps showed spatial patterns of yield variability within each field and visual association with the maps of other variables. This was confirmed by redundancy analyses and Pearson correlation coefficients. The main variables associated with yield variability were soil type, organic matter, soil moisture, weed density and canopy temperature. Kriged maps of final yield variability were strongly related to that in crop canopy cover, LAI and intercepted solar radiation early in the growing season, and the yield maps of previous crops. Therefore, yield maps of previous crops together with early assessment of sugar beet growth may make an early prediction of within-field variability in sugar beet yield possible. The Broom’s Barn sugar beet model failed to account for the spatial variability in sugar yield, but the simulation was greatly improved when corrected for early canopy development cover and when the simulated yield was adjusted for weeds and plant population. Further research to optimize inputs to maximise sugar yield should target the irrigation and fertilizing of areas within fields with low canopy cover early in the season.

Spatial variability of physical attributes of an alfisol under different hillslope curvatures

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)