No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Using present and past climosequences to estimate soil organic carbon and related physical quality indicators under future climatic conditions
Abstract
This research aimed at testing the use of present and past climosequences to estimate soil organic carbon (SOC) and related physical quality indicators under future climatic conditions. The influence of climate on soil features was studied for four combinations of typical Mediterranean soil types and cropping systems, placed along climosequences of the past (P1: 1961–1990), present (P2: 1981–2010) and future (P3: 2021–2050). The four test areas were located in Italy, each one characterized by the same soil typology and cropping system, placed on similar morphological position and parent material, wide enough to cross climatic boundaries. Legacy soil profiles that were sampled in the P1 time-period were re-sampled in 2010–2011, to check for possible variations in soil characteristics. Besides SOC content and stock (Cstock), we examined some physical quality indicators for which the existence of relations with SOC is well-known, namely soil compaction and soil crusting susceptibility, soil erodibility, and soil loss by water erosion. Among several climatic indexes, the de Martonne index (IDM) resulted the most correlated with SOC. The IDM vs. SOC relationship was significant and not different in both P1 and P2 climosequences, highlighting the temporal stability of the relation between climate and SOC content. In the Vertisols of Sicily, cultivated under cereals, the P3 climosequence predicted a SOC reduction of more than 11%. This will lead to an increase of soil erodibility, susceptibility to compaction, and surface crust formation. On the contrary, in the Luvisols under forage crops of the Po Plain, a substantial Cstock increase (28.8%) is expected, with a consequent improvement in soil physical indicators. For the Luvisols under meadows of Sardinia, an increase in erosion of 13.5% is expected, because of increased precipitation volume (7.4%) and aggressiveness. In the Andosols under olive trees of Campania there is a predicted reduction in the Cstock (−6.3%) and an associated increase in soil loss (4.6%), while no marked variation is expected for the other soil physical indicators. We can conclude that climosequences are a useful tool to predict the future dynamics of some soil physical characteristics affected by climate change. Cstock and soil loss by water erosion are expected to change significantly under future climatic conditions, while minor changes are observed for erodibility, compaction and crusting susceptibility, even when SOC variations are significant. In the climosequences, the considered soil physical quality indicators resulted proportionally more affected by the cropping system than by the climate and, within the same cropping system, more variable according to the climate than the time. This outcome confirms the fundamental role of soil physics in controlling the resilience of the soil system to climate change.
... C values were obtained from bibliographic data (see Appendix Table A.4). Given the fact that soil conservation practices are hardly applied in Misiones, and the combined effect of different conservation practices is hard to estimate for large areas (Pellegrini et al., 2018), the support practices factor (P factor) was not considered. According to the Third National Communication of the Argentine Republic to the UN Framework Convention on Climate Change (SAyDS, 2015) climate projections for 2039 anticipate that there would be no significant variations in annual precipitation in the region. ...
Misiones rainforest is one of the most threatened subtropical forests worldwide. Anthropogenic pressure by agriculture and forestry expansion continues transforming landscapes with negative consequences on ecosystem service provision, such as soil erosion control. Understanding how land use and land cover change (LUCC) management, policies, and social factors influenced in the past, allows decision-makers to anticipate potential effects on future land use and soil loss, contributing to the sustainable planning and management of productive activities. We developed three spatially explicit scenarios for Misiones province by 2030 using the Dinamica EGO modeling platform: 1) Business as Usual (BAU), 2) Low Deforestation (ALTlow), and 3) High Deforestation (ALThigh), based on different international and domestic socioeconomic contexts. We used land cover data from 2002 to 2015 as well as biophysical, social-infrastructure, political-administrative factors, and legal restrictions to estimate changes that may occur by 2030. We analyzed magnitude, intensity, and spatial pattern of future forest cover changes through transition rates and a cellular automata allocation model. Moreover, we used the Universal Soil Loss Equation (USLE) integrated into a Geographic Information System (GIS) to determine soil water erosion and soil loss tolerance in each scenario. Our results revealed that around 19% of the remaining native forest would be transformed into either agriculture or cultivated forest by 2030 for all scenarios. In addition, and contrary to that trend, the ALTlow scenario showed a recovery of 3% of native forest. Regarding soil erosion, our study indicated that the mean annual soil loss by 2030 would range from 12.03 to 19.15 t. ha−1.year−1 for ALTlow and ALThigh scenarios, respectively. Additionally, between 21% and 31% of Misiones province showed soil loss values higher than tolerance. Our work shows that a 10% decrease in the deforestation rate, compared to the current rate, would lead not only to a recovery of native forest cover, but also to a reduction in soil loss of about 4.5 Mt.yr−1 by 2030. This study demonstrates the suitability of the applied model to simulate future LUCC processes and provides inputs for decision-making involving natural resource management and the potential impacts of these decisions on ecosystem services. Finally, our results highlight the need for appropriate policies and regulations, especially, in terms of land use change restrictions in areas of high erosion risk.
... * (Si+Cl)−0.00012 * (Si+Cl) 2 +ρ100kPa SOC -soil organic carbon (%), Si -silt (2-50 μm) (%), Cl -clay (< 2 μm) (%) Soil crusting susceptibility For calculating soil crusting susceptibility, the FAO (1979) crusting index (Ic, dimensionless) was used (Pellegrini et al., 2018). The soil crusting susceptibility calculated according to the following equation (3): ...
The aim of this study is to determine some relationship between surface and
subsurface physical soil qualities (soil compaction, crust formation and Kerodibility
of subsurface soils) affected by long-term continuous cultivation in
terms of soil organic carbon (SOC) content. In this current study, it was
investigated six different soils formed under in “mesic” temperature and “xeric”
moisture regime soil environmental conditions. The results of the study showed
that soil compaction, crust formation, erodibility K are very important (P <
0.001) for organic carbon (OC), organic carbon stock, and organic matter (OM).
The research also determined that the study area generally has silt texture,
neutral pH, high amount of CaCO3, high amount of OC and OM, medium level in
the upper layer and the lower layer. No significant differences were detected
between soil properties in the surface and subsurface soil layers.
... On the other hand, at the landscape spatial scale, it is critical to consider indicators that focus on long-term changes, to overcome the short-term variation in land use and management. Soil indicators that change slowly are more related to inherent soil qualities and can reflect the impacts of climate and long-term management changes (Pellegrini et al., 2018). ...
The concepts of soil quality and soil health concern the ability of soil to function and provide ecosystem services. This chapter reports a historical and critical overview of the two terms and a review of the methods and indicators currently used to assess and monitor soil quality and health. Different approaches are described and guidelines on the selection and use of indicators across spatial and temporal scales are provided. Key indicators are introduced, grouped according to their relevance to provide 10 specific soil functions, with their main references, sensitivity to changes, and relative cost of sampling and analysis.
... In particular, topsoils (0-30 cm) of arable lands at the global scale show mean values of 51.0 ± 0.66 Mg C ha − 1 , while rice fields and forests account for 55.6 ± 3.01 and 71.1 ± 2.10 Mg C ha − 1 respectively. Most authors agree that, under equal pedoclimatic conditions, SOC content is generally smaller in cropland soils than in forests, grasslands or shrublands (see, for instance, Pellegrini et al., 2018). ...
In the European Union, the setting of Operational Groups (OG) is supported by the European Innovation Partnership to tackle specific problems and favor innovation in agriculture. They constitute an important aspect of the current Common Agricultural Policy. Increasing or maintaining soil organic carbon (SOC) content under arable farming has been acknowledged as a primary target of European agriculture. SOC-preserving agriculture needs its techniques to be tailored to local conditions, namely, the combination of factors related to the environment (climate and soil characteristics), to the farming system (land use type, farm specialization, crop management), but also to the social and cultural context (market and availability of production means, subsidies, farmers’ education, propensity for innovation and change). In this paper we present inspirational ideas and show success examples of local adaptations strategies to increase or maintain SOC content in soils under arable farming in Europe. They include:
· Adoption of soil management strategies to improve SOC storage in irrigated systems.
· Precision farming and other high-tech solutions able to generate local diagnosis and adaptive strategies for increasing SOC and reducing greenhouse gasses emissions.
· Innovative strategies for extending soil cover periods and introducing cover crops in rotations in areas with limited water availability or prone to harsh weather conditions.
· Management of rainfed and low input crops to maintain and increase SOC in dry climates and erosive prone soils.
These case studies could facilitate the setting up of OGs and the application of innovative practices in different European countries.
... All these changes have reduced the capacity for water retention, decreased water permeability, and increased the probability of soil crusting processes (Farres, 1978). This has led to markedly decreased infiltration rates (Dunne et al., 1991;Lavee et al., 1991;Chamizo et al., 2010;Pellegrini et al., 2018) even though the intensity of the rain has not increased. The overall result is less water in the vegetative root zone and greater superficial flow, which erodes the most fertile soil layer (Bryan et al., 1984;Kirkby, 1987;Thornes, 1990;Imeson and Lavee, 1998;García-Ruiz et al., 2013) and decreases the reserves of seeds and nutrients. ...
Organic carbon (OC) is an important factor for soil and the environment. This research aimed to identify OC sequestration under different age of gambier crops in central Gambier, Pesisir Selatan, West Sumatra. Soil samples were taken from different crop ages ( 2, 5, and 10 years old), and then under secondary forest nearby as a comparison at 0-30 cm soil depth. The results showed that, in general, soil OC content was considered low to very low either under Gambier crops (<2%), or under forest land use (2.11%). Then, the OC stock increased by increasing crop age from 2 to 10 years old. The OC stock at ten years old Gambier reached 90% of that at the forest on the top 30 cm soil depths. Rate of OC sequestration was approximately 1.03 Mg ha ⁻¹ y ⁻¹ and 4.88 Mg ha ⁻¹ y ⁻¹ , respectively, for crop age between 2 and 5 as well as between 5 and10 years old. This low soil organic carbon (SOC) content combined with fine soil particles caused the soil had medium-high soil BD, low-medium total soil porosity, rather slow-medium hydraulic conductivity rate, and unstable-rather stable soil aggregate stability. The study concludes that SOC sequestration under Gambier plantation in the sloping area was quite slow, especially during the first five years due to new crop establishment and the leaves harvested.
Soil indicators may be used for assessing both land suitability for restoration and the effectiveness of restoration strategies in restoring ecosystem functioning and services. In this review paper, several soil indicators, which can be used to assess the effectiveness of ecological restoration strategies in dryland ecosystems at different spatial and temporal scales, are discussed. The selected indicators represent the different viewpoints of pedology, ecology, hydrology, and land management. Two overall outcomes stem from the review. (i) The success of restoration projects relies on a proper understanding of their ecology, namely the relationships between soil, plants, hydrology, climate, and land management at different scales, which are particularly complex due to the heterogeneous pattern of ecosystems functioning in drylands. (ii) The selection of the most suitable soil indicators follows a clear identification of the different and sometimes competing ecosystem services that the project is aimed at restoring.
This study presents the use of the soil aridity index (SAI) as a substitute of the aridity index (AI) for the assessment of the desertification risk at the national and more detailed scales. The SAI expresses the mean annual number of days when the moisture control section is dry in soils with Mediterranean type of climate. This value was firstly estimated using software based on the EPIC model, then a multiple regression was found between SAI and long-term mean annual air temperature, total annual rainfall and soil available water capacity. The index was calculated for 13,000 Italian soils, spatialised on mapping units at 1:250,000 scale, and classed according to the influence of increasing water scarcity on agriculture and forestry. SAI classes were tested against i) AI classes and ii) vegetation density in natural and natural like areas, evaluated by means of a NDVI analysis. SAI classes showed a good agreement with AI classes, but separated areas with different vegetation cover more consistently than AI classes, and at a much more detailed scale. The SAI, being influenced by both soil and climate, it is a useful indicator of vulnerable lands, where increased rainfall deficit, and enhanced soil erosion, could lead to desertification
Keywords: aridity index, land degradation, GIS, NDVI, Italy
The Earth’s land resources are finite, whereas the number of people that the land must support increases rapidly, this situation has been a great concern in the area of agriculture. Crop production must be increased to meet the rapidly growing food demands through sophisticated agricultural processes, while it is important to protect other natural resources and the environment. New agricultural research is needed to provide additional information to farmers, policy makers and other decision makers on how to accomplish sustainable agriculture over the wide variations in climate change around the world. Therefore many researchers have over the years shown interest in finding ways to estimate the yield of crops before harvest. This paper reviews some of the crop growth models that have been successfully developed and used over time. The applications of crop growth models in agricultural meteorology, the role that climate changes play in these models and few of the successfully used crop models in agro-meteorology are also discussed in detail.
Soil erosion by water is one of the major threats to soils in the European Union, with a negative impact on
ecosystem services, crop production, drinking water and carbon stocks. The European Commission’s Soil
Thematic Strategy has identified soil erosion as a relevant issue for the European Union, and has
proposed an approach to monitor soil erosion. This paper presents the application of a modified version
of the Revised Universal Soil Loss Equation (RUSLE) model (RUSLE2015) to estimate soil loss in Europe for
the reference year 2010, within which the input factors (Rainfall erosivity, Soil erodibility, Cover-
Management, Topography, Support practices) are modelled with the most recently available pan-
European datasets. While RUSLE has been used before in Europe, RUSLE2015 improves the quality of
estimation by introducing updated (2010), high-resolution (100 m), peer-reviewed input layers. The
mean soil loss rate in the European Union’s erosion-prone lands (agricultural, forests and semi-natural
areas) was found to be 2.46 t ha-1 yr-1, resulting in a total soil loss of 970 Mt annually.
A major benefit of RUSLE2015 is that it can incorporate the effects of policy scenarios based on land-
use changes and support practices. The impact of the Good Agricultural and Environmental Condition
(GAEC) requirements of the Common Agricultural Policy (CAP) and the EU’s guidelines for soil protection
can be grouped under land management (reduced/no till, plant residues, cover crops) and support
practices (contour farming, maintenance of stone walls and grass margins). The policy interventions
(GAEC, Soil Thematic Strategy) over the past decade have reduced the soil loss rate by 9.5% on average in
Europe, and by 20% for arable lands. Special attention is given to the 4 million ha of croplands which
currently have unsustainable soil loss rates of more than 5 t ha-1 yr-1, and to which policy measures
should be targeted.
We report the results of a study conducted under the framework of the Agroscenari project, aimed to verify the possibility of using the MODIS remote sensing images of the decade 2001-2010 to evaluate the land use stability under some agricultural systems (meadow-pasture and arable land) in Sardinia and Sicily. In fact, the stability of the cropping system was crucial in the context of a research aimed at evaluating possible changes induced by climate change in the soil quality, as a prerequisite to be able to observe possible changes in the soil characteristics in test areas identified along climatic gradients.
The time series’ analysis of the NDVI index, allowed us to reconstruct multi-temporal signatures with high chronological detail, so allowing an objective assessment of land use dynamics in the project’s test areas.
Adaptation to climate variability and change is important both for impact assessment (to estimate adaptations which are likely to occur) and for policy development (to advise on or prescribe adaptations). This paper proposes an "anatomy of adaptation" to systematically specify and differentiate adaptations, based upon three questions: (i) adapt to what? (ii) who or what adapts? and (iii) how does adaptation occur? Climatic stimuli include changes in long-term mean conditions and variability about means, both current and future, and including extremes. Adaptation depends fundamentally on the characteristics of the system of interest, including its sensitivities and vulnerabilities. The nature of adaptation processes and forms can be distinguished by numerous attributes including timing, purposefulness, and effect. The paper notes the contribution of conceptual and numerical models and empirical studies to the understanding of adaptation, and outlines approaches to the normative evaluation of adaptation measures and strategies.
In this study, the Savitzky-Golay filter was applied to smooth observed unnatural variations in the temporal profiles of the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) time series from the MODerate Resolution Imaging Spectroradiometer (MODIS). We computed two sets of land cover classifications based on the NDVI and EVI time series before and after applying the Savitzky-Golay filter. The resulting classification from the filtered versions of the vegetation indices showed a substantial improvement in accuracy when compared to the classifications from the unfiltered versions. The classification by the EVIsg had the highest K (0.72) for all classes compared to those of the EVI (0.67), NDVI (0.63), and NDVIsg (0.62). Therefore, we conclude that the EVIsg is best suited for land cover classification compared to the other data sets in this study.
Management decisions both at the field and off-site have the potential to contrib-ute to climate change mitigation and adaptation. Climate change threatens to increase the potential for soil erosion, reduce soil quality, lower agricultural productivity and negatively impact food security and global sustainability, making it one of the most severe challenges we will face in the 21st century. This paper looks at the potential of management to help us, not only mitigate climate change, but also to help us adapt to a changing climate. Different aspects of carbon management, nitrogen management, manure management, management in low-input systems (sustainable agriculture), and grazing land management are discussed as examples. Management decisions regarding conservation practices, such as no-till, con-servation agriculture, and returning crop residue to the field to increase nutrient cycling, can contribute to carbon sequestration and help us mitigate and adapt to climate change. Additionally, management of grasslands, restoration of degraded/desertified lands, nitrogen management to reduce greenhouse gas emissions, precision conservation management at a field and/or watershed level, and other management alternatives can also help us mitigate and/or adapt to climate change. Management for climate change mitigation and adaptation is key for environmental conservation, sustainability of cropping systems, soil and water quality, and food security. This paper suggests, based on a review of the literature, that management decisions that reduce soil erosion, increase carbon sequestration to improve soil functions, soil quality, and soil health, and contribute to the resilience of soils and cropping systems will be needed to respond to climate change and related challenges such as food security. Our review suggests that without management decisions that increase soil and water conservation, food security for the world's growing population will be harder to achieve.
The greatest obstacle to soil erosion modelling at larger spatial scales is the lack of data on soil characteristics. One key parameter for modelling soil erosion is the soil erodibility, expressed as the K-factor in the widely used soil erosion model, the Universal Soil Loss Equation (USLE) and its revised version (RUSLE). The K-factor, which expresses the susceptibility of a soil to erode, is related to soil properties such as organic matter content, soil texture, soil structure and permeability. With the Land Use/Cover Area frame Survey (LUCAS) soil survey in 2009 a pan-European soil dataset is available for the first time, consisting of around 20,000 points across 25 Member States of the European Union. The aim of this study is the generation of a harmonised high-resolution soil erodibility map (with a grid cell size of 500 m) for the 25 EU Member States. Soil erodibility was calculated for the LUCAS survey points using the nomograph of Wischmeier and Smith (1978). A Cubist regression model was applied to correlate spatial data such as latitude, longitude, remotely sensed and terrain features in order to develop a high-resolution soil erodibility map. The mean K-factor for Europe was estimated at 0.032 t ha h ha− 1 MJ− 1 mm− 1 with a standard deviation of 0.009 t ha h ha− 1 MJ− 1 mm− 1. The yielded soil erodibility dataset compared well with the published local and regional soil erodibility data. However, the incorporation of the protective effect of surface stone cover, which is usually not considered for the soil erodibility calculations, resulted in an average 15% decrease of the K-factor. The exclusion of this effect in K-factor calculations is likely to result in an overestimation of soil erosion, particularly for the Mediterranean countries, where highest percentages of surface stone cover were observed.
A number of processes of degradation threaten soil functions. Ten of them are acknowledged by the European Union and fifteen by the Organisation for Economic Co-operation and Development (OECD), but at least another seven have been indicated by different authors in Italy and in other parts of the world. This short review paper summarizes the nature, economic relevance, and territorial impact of soil degradation in Italy, and with reference to Europe as a whole, and highlights the most relevant research needs in soil conservation. The direct annual costs of the main soil degradation processes are estimated to be over 38,000,000,000 euro per year in Europe as a whole, while in Italy, only for landslides, floods, and soil erosion, costs amount to 900,000,000 euro. Loss of the ability to produce food commodities because of soil degradation is particularly important in Italy, since selfsufficiency in food has recently decreased to less than 80% and Italian agricultural soils are hit by several problems, such as limited soil drainage, unfavorable texture and stoniness, shallow rooting depth, and poor chemical properties. On average, soil sealing, reduction in organic matter, and soil compaction in Italy are comparable with those of many other countries, but the occurrence of soil erosion, floods, and landslides is more widespread than in most parts of Europe, and also the presence of salt-affected soils is becoming a major worry. The fight against soil degradation in Italy is certainly more difficult than in other countries because of the high environmental variability. However, according to the current trends, Italy is mostly probably destined not to achieve the European objective to significantly reduce main soil degradation processes by the year 2020. There are several research needs in the field of soil conservation in Italy. These include: i) a better basic knowledge about many soil degradation processes and of pedodiversity; ii) reliable, sensitive, and locally validated models for main degradation processes; iii) assessment of resilience of different soils against degradation processes, as well as of their reaction to the measures foreseen in the current European agricultural policy.
ABSTRACT
Climate change scenarios of seasonal maximum, minimum temperature and precipitation in five Italian regions, over the period 2021-2050 against 1961-1990 are assessed. The regions selected by the AGROSCENARI project are important from the local agricultural practises and are situated as
follows: in the Northern Italy - Po valley and hilly area of Faenza; in Central part of Italy- Marche, Beneventano and Destra Sele, and in Sardinia Island - Oristano. A statistical downscaling technique applied to the ENSEMBLES global climate simulations, A1B scenario, is used to reach this objective. The method consists of a multivariate regression, based on Canonical Correlation Analysis, using as possible predictors mean sea level pressure, geopotential height at 500hPa and temperature at 850 hPa. The observational data set (predictands) for the selected regions is composed by a reconstruction of minimum, maximum
temperature and precipitation daily data on a regular grid with a spatial resolution of 35 km, for 1951-2009 period (managed by the Meteorological and Climatological research unit for agriculture -
Agricultural Research Council, CRA - CMA). First, a set-up of statistical model has been made using predictors from ERA40 reanalysis and the seasonal indices of temperature and precipitation from local
scale, 1958-2002 period. Then, the statistical downscaling model has been applied to the predictors derived from the ENSEMBLES global climate models, A1B scenario, in order to obtain climate change scenario of
temperature and precipitation at local scale, 2021-2050 period. The projections show that increases could be expected to occur under scenario conditions in all seasons, in both minimum and maximum temperature. The magnitude of changes is more intense during summer when the changes could reach values around 2°C for minimum and maximum temperature. In the case of precipitation, the pattern of changes is more complex, different from season to season and over the regions, a
reduction of precipitation could be expected to occur during summer. The temperature and precipitation projections from hilly area of Faenza are then used as input in a weather generator, in order to produce a
synthetic series of daily data. These series feed a water balance and crop growth model (CRITERIA) to evaluate the impact of climate change scenario in irrigation crop water needs, for 2021-2050 period. As
reference crop the kiwifruit, which is characterised by high water need and widespread in this area, has been selected.
Il compattamento del suolo provocato dall’utilizzo di macchine agricole sempre più potenti e pesanti è unanimemente ritenuto una delle principali cause del declino del potenziale produttivo dei suoli e, più in generale, motivo di degradazione ambientale.
La suscettibilità al compattamento è stata valutata utilizzando l’equazione di stima di Smith et al. (1997a), che prevede come dato di input la percentuale di limo+argilla. La validazione del modello è stata eseguita attraverso la determinazione dell’indice di compressione (compression index - C) tramite prove di compattamento uni-assiale su campioni disturbati di tre Inceptisuoli vertici (Vertic haploxerepts) con diverso uso del suolo (seminativo, pascolo, vegetazione spontanea), provenienti da aree geografiche diverse (Vicarello di Volterra (PI), S. Quirico d’Orcia (SI), Soverìa Simeri (CZ)). C rappresenta la pendenza della retta interpolante i punti della curva di compressione che mette in relazione la massa volumica apparente ed il logaritmo decimale della pressione applicata. I campioni sono stati equilibrati a 3 diversi livelli di umidità, corrispondenti al contenuto idrico alla capacità di campo (-33 kPa) (CC), al 110% (p/p) (1,1LP) e al 90% del limite plastico (0,9LP); sono state applicate 4 diverse pressioni (50, 100, 200 e 400 kPa) utilizzando una pressa idraulica dotata di cella di carico e sistema di controllo della pressione.
I risultati evidenziano come C, rappresentando unicamente la pendenza media delle rette interpolanti per i diversi livelli di umidità, non tiene adeguatamente in considerazione il dato di massa volumica apparente risultante dalle prove di compressione. Ciò può condurre a valutazioni erronee riguardo la reale suscettibilità del suolo al compattamento. Per migliorare la rappresentatività di C è stata operata una correzione introducendo, come fattore additivo, la massa volumica apparente determinata alla pressione di riferimento di 100 kPa (100kPa). I valori di C corretto (Ccm) riproducono con maggiore precisione la risposta del suolo all’azione di costipamento. I valori di 100kPa sono inoltre risultati correlati in misura significativa con il contenuto di sostanza organica (s.o.) (R2=0,77); ciò ha consentito di produrre una nuova funzione di stima di C, i cui risultati appaiono significativamente correlati (p<0,01) con i dati sperimentali (R2=0,59).
Ulteriori indagini sono comunque necessarie per verificare l’esistenza di correlazioni significative tra il contenuto percentuale di limo+argilla, la s.o. e C in suoli con tessitura diversa. In questa eventualità l’utilizzo dei modelli di stima potrà fornire valide indicazioni per la prevenzione dei danni causati dal transito delle macchine agricole.
In this study the possible effects of two predicted climate change scenarios on soil water regime of Hungarian Calcic Chernozem
soils has been investigated. Soil profiles classified as Calcic Chernozem — in total 49 — were selected from the MARTHA soil
physical database that incorporates soil data at national scale. These profiles were subdivided into three groups (sandy loam,
loam and clayey loam) in accordance with their mechanical composition. Soil water retention curves were scaled separately
for each of the three textural groups, using similar media scaling in order to represent the variability of soil hydrophysical
data with one parameter, the scaling factor (SF). Reference soil profiles were chosen according to the cumulative distribution
function of the scaling factor, six for each textural group. Daily downscaled meteorological data from A2 and B2 climate scenarios
of the Hadley Centre (2070–2100) and data from a reference period (RF, 1961–1990) were used in this study to characterize
different climatic situations. Nine representative years were selected in case of all the three scenarios, using the cumulative
probability function of the annual precipitation sum. Scenario analyses were performed, validating the SWAP soil water balance
simulation model for the 18 reference soil profiles and 27 representative years in order to evaluate the expected changes
in soil water regime under different from the present (RF) climatic conditions (A2 and B2 scenarios). Our results show that
the scaling factor could be used as a climate sensitivity indicator of soil water regime. The large climate sensitivity of
the majority of Chernozem soil subtypes water regime has been proven.
In this paper we present and discuss a comparison between statistical and regional climate modeling techniques for downscaling GCM prediction. The comparison is carried out over the Capitanata region, an area of agricultural interest in south-eastern Italy, for current (1961-1990) and future (2071-2100) climate. The statistical model is based on Canonical Correlation Analysis (CCA), associated with a data pre-filtering obtained by a Principal Component Analysis (PCA), whereas the Regional Climate Model REGCM3 was used for dynamical downscaling. Downscaling techniques were applied to estimate rainfall, maximum and minimum temperatures and average number of consecutive wet and dry days. Both methods have comparable skills in estimating stations data. They show good results for spring, the most important season for agriculture. Both statistical and dynamical models well reproduce the statistical properties of precipitation, the crucial variable for the growth of crops.
Atmospheric datasets coming from long term reanalyzes of low spatial resolution are used for different purposes. Wind over the sea is, for example, a major ingredient of oceanic simulations. However, the shortcomings of those datasets prevent them from being used without an adequate corrective preliminary treatment. Using a regional climate model (RCM) to perform a dynamical downscaling of those large scale reanalyzes is one of the methods used in order to produce fields that realistically reproduce atmospheric chronology and where those shortcomings are corrected. Here we assess the influence of the configuration of the RCM used in this framework on the representation of wind speed spatial and temporal variability and intense wind events on a daily timescale. Our RCM is ALADIN-Climate, the reanalysis is ERA-40, and the studied area is the Mediterranean Sea.
First, the dynamical downscaling significantly reduces the underestimation of daily wind speed, in average by 9 % over the whole Mediterranean. This underestimation has been corrected both globally and locally, and for the whole wind speed spectrum. The correction is the strongest for periods and regions of strong winds. The representation of spatial variability has also been significantly improved. On the other hand, the temporal correlation between the downscaled field and the observations decreases all the more that one moves eastwards, i.e. further from the atmospheric flux entry. Nonetheless, it remains ∼0.7, the downscaled dataset reproduces therefore satisfactorily the real chronology.
Second, the influence of the choice of the RCM configuration has an influence one order of magnitude smaller than the improvement induced by the initial downscaling. The use of spectral nudging or of a smaller domain helps to improve the realism of the temporal chronology. Increasing the resolution very locally (both spatially and temporally) improves the representation of spatial variability, in particular in regions strongly influenced by the complex surrounding orography. The impact of the interactive air-sea coupling is negligible for the temporal scales examined here. Using two different forcing datasets induces differences on the downscaled fields that are directly related to the differences between those datasets. Our results also show that improving the physics of our RCM is still necessary to increase the realism of our simulations. Finally, the choice of the optimal configuration depends on the scientific objectives of the study for which those wind datasets are used.
"Space-for-time" substitution is widely used in biodiversity modeling to infer past or future trajectories of ecological systems from contemporary spatial patterns. However, the foundational assumption-that drivers of spatial gradients of species composition also drive temporal changes in diversity-rarely is tested. Here, we empirically test the space-for-time assumption by constructing orthogonal datasets of compositional turnover of plant taxa and climatic dissimilarity through time and across space from Late Quaternary pollen records in eastern North America, then modeling climate-driven compositional turnover. Predictions relying on space-for-time substitution were ∼72% as accurate as "time-for-time" predictions. However, space-for-time substitution performed poorly during the Holocene when temporal variation in climate was small relative to spatial variation and required subsampling to match the extent of spatial and temporal climatic gradients. Despite this caution, our results generally support the judicious use of space-for-time substitution in modeling community responses to climate change.
Cited By (since 1996): 7, Export Date: 11 January 2013, Source: Scopus, doi: 10.1016/j.geomorph.2011.02.006, Language of Original Document: English, Correspondence Address: Fantappié, M.; CRA-ABP, Research Centre for Agrobiology and Pedology, 50121, Firenze, Italy; email: m.fanta@soilmaps.it, References: Alvarez, R., Lavado, R.S., Climate, organic matter and clay content relationships in the Pampa and Chaco soils, Argentina (1998) Geoderma, 83, pp. 127-141;
Soil organic C content, a major source of system stability in agroe-cosystems, is controlled by many factors that have complex inter-actions. The purpose of this study was to evaluate the major controls over soil organic carbon content, and to predict regional patterns of carbon in range and cultivated soils. We obtained pedon and climate data for 500 rangeland and 300 cultivated soils in the U.S. Central Plains Grasslands, and statistically analyzed relationships between C and soil texture and climate. Regression models of the regional soils database indicated that organic C increased with precipitation and clay content, and decreased with temperature. Analysis of cul-tivated and rangeland soils indicated that C losses due to cultivation increased with precipitation, and that relative organic C losses are lowest in clay soils. Application of the regression models to a re-gional climate database showed potential soil organic matter losses to be highest in the northeastern section of the Central Plains Grass-lands, decreasing generally from east to west. These statistical data analyses can be combined with more mechanistic models to evaluate controls of soil organic matter formation and turnover, and the im-plications for regional management. S OIL ORGANIC MATTER is a major component of
World soils contain about 1500 gigatons of organic carbon. This large carbon reserve can increase atmospheric concentrations of CO2 by soil misuse or mismanagement, or it can reverse the ‘greenhouse’ effect by judicious land use and proper soil management. Soil Processes and the Carbon Cycle describes soil processes and their effects on the global carbon cycle while relating soil properties to soil quality and potential and actual carbon reserves in the soil. In addition, this book deals with modeling the carbon cycle in soil, and with methods of soil carbon determinations.
The volume is an attempt to provide a global review of the mechanisms, incidence and control measures related to the problems of soil compaction in agriculture, as well as forestry and other cropping systems. After an introduction to soil compaction problems in world agriculture, chapters are presented in sections on: theory, determination and prediction of soil-vehicle mechanics; effects of soil compaction on soil structure, bulk density, hydraulic properties, aeration, strength and biological properties; mechanisms and incidence of crop responses to soil compaction, with chapters providing regional and crop-type overviews; vehicle and traffic systems in crop production, their quantification and the benefits of different equipment; economic aspects of soil compaction using different equipment and traffic systems, and the control of compaction in practice; and a conclusion and recommendations of future research. Thirteen chapters are abstracted separately in Ecological Abstracts; seven in Geographical Abstracts: Physical Geography. -J.W.Cooper
This paper extends a procedure for transforming the standard textural triangle into a new diagram based on mean particle size, d g , and standard deviations of particle size, σ g . Values of d g and σ g , obtained from the mechanical analysis on percents of clay, silt, and sand, provide a unified basis for comparing soils and physical and chemical properties of the soils. It is shown that the procedure proposed earlier is valid for samples that do not have a lognormal distribution for the whole sample. Using this approach, samples with very different distributions of each fraction can be accurately described. Information from different classification systems (e.g., USDA, ISSS, and engineering) can be used to calculate d g and σ g , thus making it possible to unify the systems. The procedure further makes it possible to extend the scale to include particles with diameters > 2 mm. Mean particle size and standard deviates are correlated with soil properties. An application is shown in the from of a relationship between water retention and d g and σ g .
This document describes the use of the ArcView GIS tool to estimate soil erosion using RUSLE (Revised Universal Soil Loss Equation). Calculations that are required will be done using capabilities available within the spatial analyst extension. In this example, we will start with a DEM (digital elevation model) and grid themes for the USLE K and USLE C factors. Note that USLE K can be obtained by adding the K factor as an attibute to a soil theme's table. The C factor can be obtained from tables or using the RUSLE program given information about land use and management. A land use theme can be used to add the C factors as an attirbute field. The LS factor can be estimated from the DEM. The technique described here for computing LS requires a flow accumulation theme. Flow accumulation can be computed from a DEM using the hydrologic extension or other watershed delineation techniques. Flow accumulation is used to estimate slope length in this example. The slope length can be estimated in other ways too such as assuming it is a constant, creating a map and assigning slope length values, etc. The slope length is often difficult to estimate due to insufficient detail in commonly available DEMs (such as 1:24000 USGS DEMs). Thus, the technique described here for estimating the slope length and LS value are necessary. First we will compute slope steepness using the DEM. Before beginning the analysis, check the Analysis Properties to make sure the extent and gird cell size are acceptable. The grid cell size should be set to the grid cell size of the DEM for this analysis. Make the DEM active, select the Surface pull down menu as shown below and select the Derive Slope option.
Knowledge of soil moisture dynamics at reasonable temporal and spatial resolutions is required to improve hydrological understanding and modelling. However, in the Mediterranean area, considered as one of the most vulnerable areas to global change, there is a severe lack of detailed soil moisture data sets. The objective of this study is to investigate the inter-annual and intra-annual spatial and temporal distributions of soil moisture in a Mediterranean mountain area, using the detailed hydrological data set gathered in the Vallcebre Research catchments (NE Spain). With the use of time-domain reflectometry, soil moisture was measured weekly between 1998 and 2003 at eight representative profiles. Time series of soil moisture showed a clear seasonal pattern. Results obtained revealed that soil moisture on hillslopes under forest cover was lower than in downslope areas covered with grasses. However, differences were not persistent through the year but were minimal during the driest and wettest conditions and maximal during the drying and wetting-up periods. During the wetting-up and drying-down periods, the soil moisture distribution depended mainly on local controls. However, during wet conditions, soil moisture patterns were controlled by nonlocal factors that limited the vegetation-related variability of soil moisture. In this mountain area, forest cover is likely to increase as the climate changes, which will most probably reduce the average soil moisture at the catchment scale as well as its spatial variability. These changes may ultimately be seen as a threat to the future of water resources in this area. Copyright © 2012 John Wiley & Sons, Ltd.
One of the key questions in climate change research relates to the future dynamics of the large amount of C that is currently stored in soil organic matter. Will the amount of C in this pool increase or decrease with global warming? The future trend in amounts of soil organic C will depend on the relative temperature sensitivities of net primary productivity and soil organic matter decomposition rate. Equations for the temperature dependence of net primary productivity have been widely used, but the temperature dependence of decomposition rate is less clear. The literature was surveyed to obtain the temperature dependencies of soil respiration and N dynamics reported in different studies. Only laboratory-based measurements were used to avoid confounding effects with differences in litter input rates, litter quality, soil moisture or other environmental factors. A considerable range of values has been reported, with the greatest relative sensitivity of decomposition processes to temperature having been observed at low temperatures. A relationship fitted to the literature data indicated that the rate of decomposition increases with temperature at 0°C with a Q10 of almost 8. The temperature sensitivity of organic matter decomposition decreases with increasing temperature, indicated by the Q10 decreasing with temperature to be about 4.5 at 10°C and 2.5 at 20°C. At low temperatures, the temperature sensitivity of decomposition was consequently much greater than the temperature sensitivity of net primary productivity, whereas the temperature sensitivities became more similar at higher temperatures. The much higher temperature sensitivity of decomposition than for net primary productivity has important implications for the store of soil organic C in the soil. The data suggest that a 1°C increase in temperature could ultimately lead to a loss of over 10% of soil organic C in regions of the world with an annual mean temperature of 5°C, whereas the same temperature increase would lead to a loss of only 3% of soil organic C for a soil at 30°C. These differences are even greater in absolute amounts as cooler soils contain greater amounts of soil organic C. This analysis supports the conclusion of previous studies which indicated that soil organic C contents may decrease greatly with global warming and thereby provide a positive feed-back in the global C cycle.
Scientific planning for soil and water conservation requires knowledge of the relations between those factors that cause loss of soil and water and those that help to reduce such losses. The soil loss prediction procedure presented in this handbook provides specific guidelines which are needed for selecting the control practices best suited to the particular needs of each site. The procedure is founded on an empirical soil loss equation that is believed to be applicable wherever numerical values of it factors are available. KEYWORDS: TROPAG textbar Miscellaneous subjects textbar Climatology textbar Land Conservation and Management textbar USA (Mainland).
[1] We utilize energy budget diagnostics from the Coupled Model Intercomparison Project phase 5 (CMIP5) to evaluate the models' climate forcing since preindustrial times employing an established regression technique. The climate forcing evaluated this way, termed the adjusted forcing (AF), includes a rapid adjustment term associated with cloud changes and other tropospheric and land-surface changes. We estimate a 2010 total anthropogenic and natural AF from CMIP5 models of 1.9 ± 0.9 W m−2 (5–95% range). The projected AF of the Representative Concentration Pathway simulations are lower than their expected radiative forcing (RF) in 2095 but agree well with efficacy weighted forcings from integrated assessment models. The smaller AF, compared to RF, is likely due to cloud adjustment. Multimodel time series of temperature change and AF from 1850 to 2100 have large intermodel spreads throughout the period. The intermodel spread of temperature change is principally driven by forcing differences in the present day and climate feedback differences in 2095, although forcing differences are still important for model spread at 2095. We find no significant relationship between the equilibrium climate sensitivity (ECS) of a model and its 2003 AF, in contrast to that found in older models where higher ECS models generally had less forcing. Given the large present-day model spread, there is no indication of any tendency by modelling groups to adjust their aerosol forcing in order to produce observed trends. Instead, some CMIP5 models have a relatively large positive forcing and overestimate the observed temperature change.
Ecophysiological models are widely used to forecast potential impacts of climate change on future agricultural productivity and to examine options for adaptation by local stakeholders and policy makers. However, protocols followed in such assessments vary to such an extent that they constrain cross-study syntheses and increase the potential for bias in projected impacts. We reviewed 221 peer-reviewed papers that used crop simulation models to examine diverse aspects of how climate change might affect agricultural systems. Six subject areas were examined: target crops and regions; the crop model(s) used and their characteristics; sources and application of data on [CO2] and climate; impact parameters evaluated; assessment of variability or risk; and adaptation strategies. Wheat, maize, soybean and rice were considered in approximately 170 papers. The USA (55 papers) and Europe (64 papers) were the dominant regions studied. The most frequent approach used to simulate response to CO2 involved adjusting daily radiation use efficiency (RUE) and transpiration, precluding consideration of the interacting effects of CO2, stomatal conductance and canopy temperature, which are expected to exacerbate effects of global warming. The assumed baseline [CO2] typically corresponded to conditions 10–30 years earlier than the date the paper was accepted, exaggerating the relative impacts of increased [CO2]. Due in part to the diverse scenarios for increases in greenhouse gas emissions, assumed future [CO2] also varied greatly, further complicating comparisons among studies. Papers considering adaptation predominantly examined changes in planting dates and cultivars; only 20 papers tested different tillage practices or crop rotations. Risk was quantified in over half the papers, mainly in relation to variability in yield or effects of water deficits, but the limited consideration of other factors affecting risk beside climate change per se suggests that impacts of climate change were overestimated relative to background variability. A coordinated crop, climate and soil data resource would allow researchers to focus on underlying science. More extensive model intercomparison, facilitated by modular software, should strengthen the biological realism of predictions and clarify the limits of our ability to forecast agricultural impacts of climate change on crop production and associated food security as well as to evaluate potential for adaptation.
As a part of a study on soil compaction, measurements of soil bulk density were taken three times during the season at five sites and three depths. Strong negative correlations were obtained between soil organic matter content and (1) soil bulk density and, (2) the increase in bulk density during the season. Key words: Bulk density, compaction, organic matter
Rebound (decrease in bulk density after removal of mechanical stress) of five agricultural soils from four soil orders was measured at stresses of 1,000, 5,000 and 10,000 g cm ⁻² and pore water pressures from 0 to −1 bar.
Rebound was usually < 0.05 g cm ⁻³ . It was larger in soils containing expanding clays than in soils containing nonexpanding clays. It increased with stress in soils containing expanding clays but not in soils containing nonexpanding clays. Rebound decreased with repeated loading. It could not be delineated in terms of soil type, pore water pressure, or water content. In the prediction of bulk density of unsaturated agricultural soils based on their compression characteristics, rebound is usually small enough that it can be neglected.
This paper illustrates an example of ‘early warning’ assessment of sensitivity to land degradation (LD) over Italy by monitoring changes of its main determinants during a long-term period (1960–2008) and by providing a short-term evaluation for 2015. These objectives were gained (i) by analysing trends of several climate, vegetation, and land use variables, regarded as the main underlying factors to LD, (ii) by calculating the standard Environmental Sensitive Area Index (ESAI) in 1960, 1990, 2000 and 2008, and (iii) by projecting the ESAI changes in the near future. An evident increase in the number and extent of areas sensitive to LD was observed during the last fifty years in southern Italy. Interestingly, the reduction of rainfall amounts, together with increasing population density and agricultural intensification, are leading northern Italy to a high level of sensitivity too. The applicability of the ESA scheme to a permanent monitoring of LD sensitivity in the Mediterranean landscape was discussed for improvements at the regional scale.
Factors affecting the compaction susceptibility of South African forestry soils were assessed. Two traditional measures of compaction susceptibility were used: maximum bulk density (ρmbd) determined by the standard Proctor test, defined compactibility, and the compression index using a simple uni-axial test, defined compressibility. Soils were chosen from a broad range of geological and climatic regions and they varied greatly in texture (8 to 66 g 100 g−1 clay) and organic matter content (0.26 to 5.77 g 100 g−1 organic carbon). Soils showed a wide range in ρmbd values, from 1.24 to 2.00 Mg m−3, and this reflected the wide range of particle size distributions and organic matter contents of the soils. Very good correlations were achieved between measures of particle size distribution, particularly clay plus silt and both compactibility and compressibility. Both compactibility and compressibility were significantly correlated with loss-on-ignition (LOI) which is a measure reflecting the combined effects of soil texture and organic matter on soil physical properties. Indices of compaction susceptibility were influenced more by particle size distribution than by organic carbon content. Clear effects of organic carbon on compaction behaviour were only evident for soils with low clay contents (< 25 g 100 g−1. No clear relationship between compactibility and compressibility was found. Compactibility generally increased with decreasing clay plus silt content, whereas compressibility increased up to about 70 g 100 g−1 clay plus silt before decreasing again. It is difficult to define compaction susceptibility solely in terms of indices of compactibility or compressibility particularly as there is no clear relationship between these two properties. A classification system for compaction risk assessment is presented, based on the relationship between compactibility (ρmbd) and LOI, and between clay plus silt content and compressibility.
Compression curves (bulk density vs. log applied stress) determined on 36 world agricultural soil samples at a given water content were linear over the range of stresses from about 1 to 10 kg cm ⁻² . Compression curves determined for soils at different water contents were approximately parallel to each other over the range of initial pore water potentials from −0.05 to −1.0 bar. The compression index, C (slope of the compression curve), increased approximately linearly as clay content of the soil increased up to about 33% and then remained approximately constant as clay content further increased. The maximum C value was 0.55 for soils with predominantly 2:1 type clays and 0.50 for soils with predominantly kaolinite or iron oxides in the clay fraction. If the compression curve at one water content is known, a procedure is suggested to predict compression curves at other water contents.
This paper represents a component of a large-scale project to investigate the effect of predicted climate change on Scottish agriculture.Weather places constraints on time available for carrying out field operations during the autumn, winter and spring because of problems arising when heavy machines operate on wet soil which has low bearing strength. Particular problems also arise for land spreading of farm wastes such as slurry, where spreading in some weather and soil conditions leads to watercourse pollution. Soil moisture is strongly influenced by rainfall, and a changed climate is expected to include a major change to winter rainfall patterns. In this study, a soil water and heat simulation model was used to predict the effect of changed climate scenarios on the number of available workdays in autumn, winter and spring (1 September–30 April) with different soil types and at a number of Scottish sites. Results showed a substantial reduction in numbers of available workdays in the changed climate because of the expected increase in winter rainfall. Also, higher winter temperatures would reduce the incidences of frozen soil and snow cover, further reducing workday numbers for tillage operations, but slightly increasing the opportunities for field spreading of wastes. In September and April only and at drier sites, higher temperature and potential transpiration values in the changed climate offset the increase in rainfall to give a small increase in workday numbers.
The particle-size distribution is a fundamental soil physical property commonly used for attributing the textural class and, given its strict linkage with both several soil edaphyc properties and geomorphology processes, it represents a routine lab determination.Over recent decades, various new methods for grain-size analysis have been developed, among them, the X-ray granulometer represents the unique innovative technology based on the Stokes' law, directly comparable with the standard methods (pipette and hydrometer). The authors illustrate the possibility of employing the Micromeritics SediGraph 5210 device for analyzing soil particles up to 250 μm. This aim involves some modifications to the soil sample preparation along with a proper set up of the apparatus in order to assure the conformity with the Stokes' law.A data set of 180 Italian soils, distributed over the entire soil textural triangle, was analyzed by both the SediGraph and pipette methods. Two-thirds of samples were employed for the calibration phase while the remaining part was used for validation.A set of six multilinear regressions, one for each analyzed grain-size class, was developed to convert the SediGraph data into pipette-equivalent values. Regardless of the high significance level (p < 0.001) of all the regressions, the coefficient of determination was always larger than 0.87, with the only exception of very fine sand (50–100 μm) fraction (R2 = 0.64).No regressions were needed when SediGraph clay content is ≥ 68%; in such a case no conversion was required because SediGraph results match with the pipette data.
Scanning electron micrographs (SEM) of crusts of loessial soils are presented. SEM observations were performed on crusts formed by raindrop impact at various stages of their formation. The crust structure was compared to the natural undisturbed soil. During the crust formation, a middle-term stage developed at which coarse particles, stripped of the fine ones, composed the surface layer of the soil. At the final stage of the crust formation, the coarse particles were washed away, and a thin seal skin, about 0.1 millimeter thick, formed the uppermost layer of the soil. A depositional crust, which was formed mainly by the translocation of fine particles, was marked by the presence of a thin skin also about 0.1 millimeter thick, suggesting involvement of similar secondary mechanisms of formation. This work illustrates the use of SEM for the study of soil crust formation and structure.
(C) Williams & Wilkins 1980. All Rights Reserved.