Multi-modelling predictions show high uncertainty of required carbon input changes to reach a 4‰ target

Soils store vast amounts of carbon (C) on land, and increasing soil organic carbon (SOC) stocks in already managed soils such as croplands may be one way to remove C from the atmosphere, thereby limiting subsequent warming. The main objective of this study was to estimate the amount of additional C input needed to annually increase SOC stocks by 4%(0) at 16 long-term agricultural experiments in Europe, including exogenous organic matter (EOM) additions. We used an ensemble of six SOC models and ran them under two configurations: (1) with default parametrization and (2) with parameters calibrated site-by-site to fit the evolution of SOC stocks in the control treatments (without EOM). We compared model simulations and analysed the factors generating variability across models. The calibrated ensemble was able to reproduce the SOC stock evolution in the unfertilised control treatments. We found that, on average, the experimental sites needed an additional 1.5 +/- 1.2 Mg C ha(-)(1) year(-1) to increase SOC stocks by 4%(0) per year over 30 years, compared to the C input in the control treatments (multi-model median +/- median standard deviation across sites). That is, a 119% increase compared to the control. While mean annual temperature, initial SOC stocks and initial C input had a significant effect on the variability of the predicted C input in the default configuration (i.e., the relative standard deviation of the predicted C input from the mean), only water-related variables (i.e., mean annual precipitation and potential evapotranspiration) explained the divergence between models when calibrated. Our work highlights the challenge of increasing SOC stocks in agriculture and accentuates the need to increasingly lean on multi-model ensembles when predicting SOC stock trends and related processes. To increase the reliability of SOC models under future climate change, we suggest model developers to better constrain the effect of water-related variables on SOC decomposition

Soil organic carbon changes simulated with the AMG model in a high-organic-matter Mollisol

Soil organic carbon (SOC) management requires a precise knowledge of how it is affected by soil use. Simulation models could help for this purpose. The AMG model is simple, requires information that is easily available, and uses few parameters. This model has neither been calibrated/adjusted nor validated for loamy soils with high SOC concentrations. We hypothesized that AMG would satisfactorily simulate SOC stock changes in soils with these characteristics. The aims of this work were: 1) to adjust and validate AMG for different tillage systems, nitrogen (N) fertilization levels and crop types for loamy-high-SOC Mollisols, and 2) to simulate future SOC changes under different production scenarios. We used SOC stocks (0-20 cm depth) from three long-term experiments (1976-2012) (tillage systems, crop rotations, and N fertilization) in the Southeastern Buenos Aires Province, Argentina (37º 45' S, 58º 18' W) on a complex of Mollisols. Data from two of those experiments was split into two groups to adjust unknown model parameters and for cross validation. Data from the third experiment was used for independent validation. The model was used to simulate SOC stock variation (30 yr) under different combinations of initial SOC stocks (SOCi, three levels) and crop rotations (six rotations regarding continuous cropping and crop-pasture rotations). Model performance was evaluated through statistical indicators based on observed-simulated value differences, and simple linear regression of observed on simulated values. Cross validation yielded promising indicators with the mean observed-simulated value differences close to 0 (P > 0.05). Root mean square error (RMSE) and RMSE as percentage of the mean of observed values (RMSEp) were 6.0 Mg C ha-1 and 7.5%, respectively, which are acceptable. Simple linear regression of observed and simulated values was highly significant (P 0.05), respectively, although R2 was low. Indicators of model performance by groups of treatments were, in general, acceptable and did not show clear trends associated with any management type. However, model performance was poorer under no tillage (NT) and N fertilization probably because of little observed data available for that treatment factor combination. Validation with independent data confirmed that AMG simulated SOC change satisfactorily. Future scenario simulations showed that when the SOCi stock was high (close to SOC saturation), even rotations with high intensification and carbon input produced a SOC stock decrease. Conversely, when the SOCi stock was low (35% loss of SOC with respect to saturation) all scenarios led to a SOC stock increase. However, AMG failed to acceptably simulate the expected effect of pastures in the rotation. The AMG model satisfactorily simulated SOC stock changes due to different management techniques of soils with a loamy surface texture and high original SOC stock. Therefore, the model could be used as a tool to help management planning with an admissible simulation error (RMSEp ~6%).El manejo del carbono orgánico del suelo (SOC) requiere del conocimiento de cómo es afectado por su uso. Modelos de simulación podrían ayudar en esta tarea. El modelo AMG es simple, requiere información fácilmente disponible y se basa en pocos parámetros. Este modelo no ha sido calibrado ni validado para suelos de textura franca con elevado contenido de SOC. Nosotros hipotetizamos que AMG simulará satisfactoriamente los cambios en el SOC debidos al uso agrícola de suelos de tales características. Los objetivos fueron: 1) ajustar y validar AMG en diferentes condiciones de sistema de labranza, fertilización con nitrógeno (N) y tipos de cultivos para Mollisoles de textura superficial franca y elevado contenido de SOC, y 2) simular cambios futuros de SOC bajo diferentes escenarios de producción. Utilizamos los contenidos de SOC (0-20 cm) de tres experimentos de larga duración (1976-2012) de sistemas de labranza, rotaciones de cultivos y fertilización con N en el sudeste de la provincia de Buenos Aires, Argentina (37º 45' S, 58º 18' W) sobre un complejo de Mollisoles. Los datos de dos ellos fueron divididos en dos grupos al azar para ajustar algunos de los parámetros del modelo y para validación cruzada, respectivamente. Los datos del tercer experimento fueron utilizados para una validación independiente. El modelo fue usado para simular la variación del SOC (30 años) bajo diferentes combinaciones de contenido inicial de SOC (SOCi, tres niveles) y rotaciones de cultivos (seis rotaciones considerando agricultura continua y rotaciones cultivo-pastura). El desempeño del modelo fue evaluado a través de indicadores estadísticos basados en la diferencia observados-simulados y regresiones lineales simples de observados vs. simulados. La validación cruzada dio resultados prometedores con una media de observados-simulados cercana a 0 (P > 0,05). La raíz del cuadrado medio del error (RMSE) y el RMSE como porcentaje de la media de los valores observados (RMSEp) fueron 6,0 Mg C ha-1 y 7,5%, respectivamente, que son valores aceptables. La regresión lineal simple de observados vs. simulados fue altamente significativa (P 0,05), aunque el R2 fue bajo. Los indicadores por grupos de tratamientos fueron, en general, aceptables y no mostraron tendencias asociadas a un manejo en particular. Sin embargo, el desempeño del modelo fue más pobre bajo siembra directa (NT) con fertilización con N, posiblemente debido a la poca información disponible para esa combinación de tratamientos. La validación con datos independientes confirmó el buen desempeño de AMG. Las simulaciones a futuro mostraron que cuando SOCi era alto (cercano a la saturación de SOC), aún las rotaciones con alta intensificación y aporte de carbono provocaron disminución del contenido de SOC. Por el contrario, cuando SOCi fue bajo (35% de pérdida del SOC a saturación) todos los escenarios condujeron a aumentar el SOC. Sin embargo, AMG no fue capaz de simular aceptablemente el efecto esperado de las pasturas en la rotación. El modelo AMG simuló satisfactoriamente los cambios en contenido de SOC debido a diferentes manejos del suelo con textura franca y elevado contenido original de SOC. Por lo tanto, el modelo podría ser utilizado como herramienta de apoyo a la planificación del manejo con un error admisible (RMSEp ~6%).A gestão do carbono orgânico do solo (SOC) necessita de um conhecimento rigoroso de como o uso do solo a pode afetar . Com esse objetivo podem ser utilizados modelos de simulação. O modelo AMG é simples, requer informação facilmente disponível e baseia-se num reduzido número de parâmetros. Esse modelo não tem contudo sido calibrado/ajustado nem validado para solos argilosos com elevado nível de SOC. Neste estudo partiu-se da hipótese que o modelo AMG poderá simular satisfatoriamente as variações de SOC devidas ao uso agrícola em solos com essas características. Os objetivos foram: 1) ajustar e validar o AMG sob diferentes condições de sistema de preparação do solo, fertilização com azoto (N) e tipos de cultura para Molisolos com textura argilosa e elevado teor de SOC, e 2) simular variações futuras de SOC sob diferentes cenários de produção. Para as simulações utilizaram-se os teores de SOC (0-20 cm) de três ensaios de longa duração (1976-2012) com sistemas de preparação do solo, rotações de culturas e fertilização com N no sueste da provincia de Buenos Aires, Argentina (37º 45' S, 58º 18' W) sobre um complexo de Molisolos. Os dados provenientes de dois de esses ensaios foram divididos em dois grupos ao acaso para ajustar parâmetros do modelo e para a validação cruzada, respetivamente. Os dados do terceiro ensaio forma usados para validar o modelo. O modelo foi usado para simular a variação de SOC (30 anos) sob diferentes combinações de teor inicial de SOC (SOCi, três níveis) e rotações de culturas (seis rotações com agricultura continua e rotações cultura-pastagem). O desempenho do modelo foi avaliado mediante índices estatísticos baseados na diferença observados-simulados, e regressões lineares simples entre observados e simulados. A validação cruzada apresentou resultados promissores com uma média da diferença entre observados e simulados próxima de 0 (P > 0,05). A raiz do quadrado médio do erro (RMSE) e o RMSE expresso como percentagem da média dos valores observados (RMSEp) foram 6,0 Mg C ha-1 e 7,5%, respetivamente, os quais são valores considerados aceitáveis. A regressão linear simples entre observados e simulados foi altamente significativa (P 0,05), apesar do valor de R2 ser baixo. Os índices por grupos de tratamentos foram, em geral, aceitáveis e não mostraram tendências associadas a uma gestão em particular. Contudo, o desempenho do modelo foi mais pobre em condições de fertilização com NT e N, possivelmente devido à pouca informação disponível para essa combinação de tratamentos. A validação com dados independentes confirmou que o AMG simulou a alteração do SOC de forma satisfatória. Os cenários futuros mostraram que quando o nível de SOCi foi elevado (próximo a saturação de SOC), mesmo as rotações com elevada intensificação e aportes de carbono provocaram diminuição do conteúdo de SOC. Pelo contrário, quando SOCi foi baixo (35% de perdas do SOC a saturação) todos os cenários aumentaram o nível de SOC. No entanto, o AMG não simulou aceitavelmente o efeito das pastagens na rotação. O modelo AMG simulou satisfatoriamente as variações de SOC devido a diferentes gestões do solo com textura argilosa e elevado teor inicial de SOC. Como tal, o modelo poderia ser usado como ferramenta de apoio no planeamento da gestão com um erro considerado admissível (RMSEp ~6%).EEA BalcarceFil: Moreno, Rocio. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; ArgentinaFil: Studdert, Guillermo. Unidad Integrada Balcarce, Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce, Argentina. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina.Fil: Monterubbianesi, María Gloria. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; ArgentinaFil: Irigoyen, Andrea Inés. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentin

Ensemble modelling, uncertainty and robust predictions of organic carbon in long-term bare-fallow soils

ACKNOWLEDGEMENTS This study was supported by the project “C and N models inter-comparison and improvement to assess management options for GHG mitigation in agro-systems worldwide” (CN-MIP, 2014- 2017), which received funding by a multi-partner call on agricultural greenhouse gas research of the Joint Programming Initiative ‘FACCE’ through national financing bodies. S. Recous, R. Farina, L. Brilli, G. Bellocchi and L. Bechini received mobility funding by way of the French Italian GALILEO programme (CLIMSOC project). The authors acknowledge particularly the data holders for the Long Term Bare-Fallows, who made their data available and provided additional information on the sites: V. Romanenkov, B.T. Christensen, T. Kätterer, S. Houot, F. van Oort, A. Mc Donald, as well as P. Barré. The input of B. Guenet and C. Chenu contributes to the ANR “Investissements d’avenir” programme with the reference CLAND ANR-16-CONV-0003. The input of P. Smith and C. Chenu contributes to the CIRCASA project, which received funding from the European Union's Horizon 2020 Research and Innovation Programme under grant agreement no 774378 and the projects: DEVIL (NE/M021327/1) and Soils‐R‐GRREAT (NE/P019455/1). The input of B. Grant and W. Smith was funded by Science and Technology Branch, Agriculture and Agri-Food Canada, under the scope of project J-001793. The input of A. Taghizadeh-Toosi was funded by Ministry of Environment and Food of Denmark as part of the SINKS2 project. The input of M. Abdalla contributes to the SUPER-G project, which received funding from the European Union's Horizon 2020 Research and Innovation Programme under grant agreement no 774124.Peer reviewedPostprin

Defining Quantitative Targets for Topsoil Organic Carbon Stock Increase in European Croplands: Case Studies With Exogenous Organic Matter Inputs

The EU Mission Board for Soil Health and Food proposed a series of quantitative targets for European soils to become healthier. Among them, current soil organic carbon (SOC) concentration losses in croplands (0.5% yr(-1) on average at 20 cm depth) should be reversed to an increase of 0.1-0.4% yr(-1) by 2030. Quantitative targets are used by policy makers to incentivize the implementation of agricultural practices that increase SOC stocks. However, there are different approaches to calculate them. In this paper, we analyzed the effect of exogenous organic matter (EOM) inputs on the evolution of SOC stocks, with a particular focus on the new European targets and the different approaches to calculate them. First, we illustrated through two case-study experiments the different targets set when the SOC stock increase is calculated considering as reference: 1) the SOC stock level at the onset of the experiment and 2) the SOC stock trend in a baseline, i.e., a control treatment without EOM addition. Then, we used 11 long-term experiments (LTEs) with EOM addition in European croplands to estimate the amount of carbon (C) input needed to reach the 0.1 and 0.4% SOC stock increase targets proposed by the Mission Board for Soil Health and Food, calculated with two different approaches. We found that, to reach a 0.1 and 0.4% increase target relative to the onset of the experiment, 2.51 and 2.61 Mg C ha(-1) yr(-1) of additional C input were necessary, respectively. Reaching a 0.1 and 0.4% increase target relative to the baseline required 1.38 and 1.77 Mg C ha(-1) yr(-1) of additional input, respectively. Depending on the calculation method used, the estimated amounts of additional C input required to reach each quantitative target were significantly different from each other. Furthermore, the quality of C input as represented by the C retention rate of the additional organic material (EOM and crop residue), had a significant effect on the variation of SOC stocks. Our work highlights the necessity to take into consideration the additional C input required to increase SOC stocks, especially for soils with decreasing SOC stocks, when targets are set independently of the baseline

Argentina: Soil Organic Carbon Sequestration Potential National Map. National Report. Version 1.0. Year: 2021

Soil organic carbon (SOC) is a key factor affecting soil physical fertility, as it improves several soil properties such as infiltration, structural stability, porosity, aeration and structure. It also improves soil chemical fertility since C is part of the soil organic matter, which constitutes the main reservoir of nutrients for crops (nitrogen, sulfur, zinc, among others). SOC is positively correlated with soil microbial biomass that acts on nutrient cycling and metabolization processes of toxic molecules. The total SOC stock in topsoil (0-30cm) is about 19.7 Pg C (FAO-ITPS GSOC map, 2018). Thus, due to the size of the soil carbon pool, even small increments in the net soil C storage may represent a substantial C sink potential. Although agricultural greenhouse gas emissions (GHGs) contribute to an important share of Argentina GHG emissions (135.53 MtCO2eq, 37% of total country GHG emissions; SAyDS, 2019), increasing ASOC stocks through judicious land use and sustainable soil management (SSM) practices may represent an important strategy to reduce and mitigate GHG emissions. In Argentina, the total productive area is about 157 million hectares (INDEC, 2021). Agricultural área (croplands) is about 40 (forty) million hectares, predominantly under no tillage system (91% agricultural area; AAPRESID, 2020). Soybean is the main product (45 million tons in 17 million hectares), followed by corn (44 million tons in 6.3 million hectares), wheat (17 million tons in 6.5 million hectares), barley (4.1 million tons in 0.1 million hectares) and sunflower (2.7 million tons in 1.3 million hectares).The rest of the area (over 124 Million hectares) is occupied with grasslands and shrublands dedicated to livestock production, and other agricultural uses. In the last decade’s agricultural land increased and SOC content decayed. This process of land use change was explained by increasing soybean monoculture and displacing livestock area, reducing SOC content (Lavado & Taboada, 2009). There has been an intense expansion of agriculture at the expense of grasslands, native forests and other natural resources in semiarid, sub-humid and subtropical regions of the country (Volante et al., 2012). Currently, soils of the Chaco-Pampean region exhibit SOC levels between 40-70% of the contents of virgin soils (Alvarez & Steinbach, 2009; Sainz Rozas et al., 2011; Milesi Delaye et al., 2013). Several farming practices may be used to restore or diminish the SOC loss, reduce soil erosion, sequester atmospheric carbon dioxide (CO2 ) and improve the soil quality (Poffenbarger et al., 2020). Among these practices, the inclusion of cover crops (CC) during winter has been postulated as one of the most promising activities (Ruis & Blanco-Canqui, 2017). The inclusion of CC showed average SOC sequestration rates of 0.45 tC/ha/yr (± 0.03), in Argentina (Alvarez et al., 2017; Beltran et al., 2018; Romaniuk et al., 2018). Increasing nutrient availability, crop growth and residue returns by increasing fertilizer use showed an increment of SOC around 0.18 tC/ha/yr (± 0.03) (Duval et al., 2020; Restovich et al., 2019). The inclusion of cycles with perennial pastures in crop rotations showed average SOC sequestration rates of 0.76 tC/ha/yr (± 0.03), exhibiting the greatest potential to increase SOC stocks (Costantini et al., 2016; Gil et al., 2016). Sustainable soil management (SSM) practices (FAO, 2020) such as the above mentioned practices have demonstrated potential to increase SOC stocks in different agricultural systems in Argentina, and thus sequester atmospheric CO2 as SOC to mitigate GHG emissions. However, SOC sequestration from these practices show highly variable sequestration rates, depending on edapho-climatic conditions, land use and management, among other factors. It is therefore relevant to identify which regions, soils, climates and systems have a greater potential to increase SOC stocks, in order to establish priorities for research and implementation of private and public policies. In this Soil organic carbon (SOC) is a key factor affecting soil physical fertility, as it improves several soil properties such as infiltration, structural stability, porosity, aeration and structure. It also improves soil chemical fertility since C is part of the soil organic matter, which constitutes the main reservoir of nutrients for crops (nitrogen, sulfur, zinc, among others). SOC is positively correlated with soil microbial biomass that acts on nutrient cycling and metabolization processes of toxic molecules. The total SOC stock in topsoil (0-30cm) is about 19.7 Pg C (FAO-ITPS GSOC map, 2018). Thus, due to the size of the soil carbon pool, even small increments in the net soil C storage may represent a substantial C sink potential. Although agricultural greenhouse gas emissions (GHGs) contribute to an important share of Argentina GHG emissions (135.53 MtCO2eq, 37% of total country GHG emissions; SAyDS, 2019), increasing ASOC stocks through judicious land use and sustainable soil management (SSM) practices may represent an important strategy to reduce and mitigate GHG emissions. In Argentina, the total productive area is about 157 million hectares (INDEC, 2021). Agricultural área (croplands) is about 40 (forty) million hectares, predominantly under no tillage system (91% agricultural area; AAPRESID, 2020). Soybean is the main product (45 million tons in 17 million hectares), followed by corn (44 million tons in 6.3 million hectares), wheat (17 million tons in 6.5 million hectares), barley (4.1 million tons in 0.1 million hectares) and sunflower (2.7 million tons in 1.3 million hectares).The rest of the area (over 124 Million hectares) is occupied with grasslands and shrublands dedicated to livestock production, and other agricultural uses. In the last decade’s agricultural land increased and SOC content decayed. This process of land use change was explained by increasing soybean monoculture and displacing livestock area, reducing SOC content (Lavado & Taboada, 2009). There has been an intense expansion of agriculture at the expense of grasslands, native forests and other natural resources in semiarid, sub-humid and subtropical regions of the country (Volante et al., 2012). Currently, soils of the Chaco-Pampean region exhibit SOC levels between 40-70% of the contents of virgin soils (Alvarez & Steinbach, 2009; Sainz Rozas et al., 2011; Milesi Delaye et al., 2013). Several farming practices may be used to restore or diminish the SOC loss, reduce soil erosion, sequester atmospheric carbon dioxide (CO2) and improve the soil quality (Poffenbarger et al., 2020). Among these practices, the inclusion of cover crops (CC) during winter has been postulated as one of the most promising activities (Ruis & Blanco-Canqui, 2017). The inclusion of CC showed average SOC sequestration rates of 0.45 tC/ha/yr (± 0.03), in Argentina (Alvarez et al., 2017; Beltran et al., 2018; Romaniuk et al., 2018). Increasing nutrient availability, crop growth and residue returns by increasing fertilizer use showed an increment of SOC around 0.18 tC/ha/yr (± 0.03) (Duval et al., 2020; Restovich et al., 2019). The inclusion of cycles with perennial pastures in crop rotations showed average SOC sequestration rates of 0.76 tC/ha/yr (± 0.03), exhibiting the greatest potential to increase SOC stocks (Costantini et al., 2016; Gil et al., 2016). Sustainable soil management (SSM) practices (FAO, 2020) such as the above mentioned practices have demonstrated potential to increase SOC stocks in different agricultural systems in Argentina, and thus sequester atmospheric CO2 as SOC to mitigate GHG emissions. However, SOC sequestration from these practices show highly variable sequestration rates, depending on edapho-climatic conditions, land use and management, among other factors. It is therefore relevant to identify which regions, soils, climates and systems have a greater potential to increase SOC stocks, in order to establish priorities for research and implementation of private and public policies.Fil: Frolla, Franco Daniel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bordenave; ArgentinaFil: Angelini, Marcos Esteban. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; Argentina. Wageningen University. Soil Geography and Landscape group; Holanda. International Soil Reference and Information Centre. World Soil Information; HolandaFil: Beltran, Marcelo Javier. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Peralta, Guillermo Ezequiel. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Di Paolo, Luciano E. Global Soil Partnership Secretariat - FAO; ItaliaFil: Rodriguez, Dario Martin. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Schulz, Guillermo. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Pascale Medina, Carla. Food and Agriculture Organization (FAO). Alianza Sudamericana de Suelos; Argentin

Response of soil viral and microbial functional diversity to long-term agricultural management in Jackson, West Tennessee

Soil microbial communities are a critical component for ecosystem stability and function. Viruses, as an important biotic controller, have the potential to regulate the abundance and diversity of bacterial communities through infection. Soil is known to harbor abundant and diverse viral assemblages but their ecological role and influence on microbial processes has not been fully elucidated. Microbes can be influenced by viruses not only from infection but though biogeochemical feedbacks of the “microbial (bacterium–phage–DOC) loop” or “viral shunt”. However, we know relatively little about the microbial community and function under the regulation of viruses in soil and how they respond to agricultural management under climate change. The objectives in this dissertation were (1) to estimate variability of soil viral and bacterial communities under a long-term conventional tillage, cover cropping and inorganic N fertilization management practices, and (2) to access the effect of seasonal change on soil bacteria community diversity and structure, and (3) identify the correlation between viruses and their host, and (4) reveal the response of microbial functional genes (C-degradation and N-cycling genes) on cover cropping and fertilization, and the potential roles of viruses on C-degradation. Soil treatments including two nitrogen rates (0, 67 kg N/ha -1), three levels of cover crop (no-cover, hairy vetch and winter wheat), and two tillage managements (conventional tillage and no tillage) from West Tennessee Agriculture Research and Education Center in Jackson were used. The soil bacterial diversity, functional gene and viral diversity was evaluated by 16S rRNA amplicon sequencing, RAPD-PCR, and bulk soil metagenomic sequencing. My findings highlight the importance of microbial on N fertilization and cover cropping in maintaining long-term C pool stability and N concentractions in agricultural soil, and the impact of viruses on C metabolism through regulating microbial metabolism using auxiliary metabolic genes. This study improves our understanding the ecological roles of soil viruses in influencing soil functions under long-term conservation agricultural management

Application of artificial neural networks to estimate soil organic carbon in a high-organic-matter Mollisol

Soil organic carbon (SOC) has a key role in the global carbon (C) cycle. The complex relationships among the components of C cycle make the modelling of SOC variation difficult. Artificial neural networks (ANN) are models capable to determine interrelationships based on information. The objective was to develop and evaluate models based on the ANN technique to estimate the SOC in Mollisols of the Southeastern of Buenos Aires Province, Argentina (SEBA). Data from three long term experiments were used. Management and meteorological variables were selected as input. Management information included numerical variables (initial SOC (SOCI); number of years from the beginning of the experiment (Year), proportion of soybean in the crop sequence; (Prop soybean); crop yields (Yield), proportion of cropping in the crop rotation (Prop agri), and categorical variables (Crop, Tillage). In addition, two meteorological inputs (minimum (Tmin) and mean air temperature (Tmed)), were selected. The ANNs were adequate to estimate SOC in the upper 0.20 m of Mollisols of the SEBA. The model with the best performance included six management variables (SOCI, Year, Prop soybean, Tillage, Yield, Prop agri) and one meteorological variable (Tmin), all of them easily available and with low level of uncertainty. Soil organic C changes related to soil use in the SEBA could be satisfactorily estimated using an ANN developed with simple and easily available input variables. Artificial neural network technique appears as a valuable tool to develop robust models to help predicting SOC changes.El carbono orgánico del suelo (SOC) tiene un papel clave en el ciclo global del carbono. Las relaciones complejas entre los componentes del ciclo de C hacen difícil la modelización de la variación del SOC. Las Redes Neuronales Artificiales (ANN) son modelos capaces de determinar las interrelaciones existentes basadas en información disponible. El objetivo fue desarrollar y evaluar modelos basados en la técnica de ANN para estimar el SOC en Mollisoles del sudeste de la Provincia de Buenos Aires, Argentina (SEBA). Fueron empleados datos provenientes de tres experimentos de larga duración conducidos en el SEBA. Variables de manejo y meteorológicas fueron seleccionadas como entradas de las ANN. La información de manejo incluyó variables numéricas (SOC inicial (SOCI); número de años desde el inicio del experimento (Year), proporción de soja (Prop soybean), rendimiento de cultivos (Yield), proporción de la agricultura en la secuencia (Prop agri)) y variables categóricas (cultivo (Crop), sistema de labranza (Tillage)). Además, dos variables meteorológicas (temperatura mínima (Tmin) y temperatura promedio (Tmed)) fueron consideradas. Las ANN estimaron adecuadamente el SOC en los 0,20 m superiores de Mollisoles del SEBA. El modelo con mejor desempeño fue desarrollado a partir de una variable meteorológica (Tmin) y seis variables de manejo (SOCI, Year, Prop sowbean, Tillage, Yield, Prop agri), todas ellas fácilmente accesibles y con bajo nivel de incertidumbre.O carbono orgânico do solo (SOC) tem um papel fundamental no ciclo global do carbono. As relações complexas entre os componentes do ciclo do C dificulta a modelação da variação do SOC. As redes neuronais artificiais (ANN) são modelos capazes de determinar as inter-relações existentes com base em informação disponível. O objetivo deste trabalho foi desenvolver e avaliar modelos baseados na técnica de ANN para estimar o SOC em Molisolos do sudeste da província de Buenos Aires, Argentina (SEBA). Foram utilizados dados de três ensaios de longa duração conduzidos em SEBA. Variáveis meteorológicas e de gestão foram selecionadas como dados de entrada das ANN. Informações de gestão incluíram variáveis numéricas (concentração inicial de SOC (SOCI); número de anos desde o início do ensaio (Year), a proporção de soja na sequência da colheita; (Prop soja), rendimento da colheita (Yield); proporção de cultivo na sequência da rotação da cultura (Prop agri)) e variáveis categóricas (cultivo (Crop), e sistema de lavoura (Tillage)). Além disso, consideraram-se duas variáveis meteorológicas (temperatura média do ar (Tmed) e temperatura mínima do ar (Tmin)). Os modelos baseados em ANN demonstraram ser adequados para estimar o SOC nas camadas superiores (0,20 m) dos Molisolos do SEBA. O modelo com melhor desempenho foi desenvolvido a partir de uma variável meteorológica (Tmin) e seis variáveis de gestão (SOCI, Year, Prop soja, Tillage, Yield, Prop agri), sendo todas as varáveis facilmente acessíveis e com baixo nível de incerteza. As alterações no SOC relacionadas com o uso do solo no SEBA poderiam ser satisfatoriamente estimadas usando uma ANN desenvolvida a partir de variáveis simples e facilmente disponíveis. A técnica de ANN parece ser uma ferramenta válida para desenvolver modelos robustos para ajudar a prever as alterações de SOC

Yield Progress in Forage Maize in NW Europe—Breeding Progress or Climate Change Effects?

Yield increases in forage maize (Zea mays L.) in NW Europe over time are well documented. The driving causes for these, however, remain unclear as there is little information available regarding the role of plant traits triggering this yield progress. Ten different hybrids from the same maturity group, which have typically been cultivated in Northwest Germany from 1970 to recent and are thus representing breeding progress over four decades, were selected for a 2-year field study in northern Germany. Traits that were investigated included leaf area index, leaf architecture, photosynthesis, radiation use efficiency, root mass, root length density, and turnover. Based on a mixed model analysis with these traits as co-variates, parameters related to leaf characteristics, in particular the number and length of leaves, the radiation use efficiency, and the leaf orientation, were identified as most influential on the yield progress (0.13 tons ha-1 year-1). In contrast to our hypothesis, root biomass only increased negligibly in newer hybrids compared to older ones, confirming the ‘functional equilibrium’ theory for high input production systems. Due to an abundance of nutrients and water in such high input systems, there is no incentive for breeders to select for carbon partitioning toward the rooting system. Breeding evidence to increase forage quality were also negligible, with no change in cob starch concentration, forage digestibility, nor NDF content and NDF digestibility. The observed increase in yield over the last four decades is due to a combination of increased temperature sums (~240 GDD within 40 years), and a higher radiation interception and radiation use efficiency. This higher radiation interception was driven by an increased leaf area index, with a higher number of leaves (16 instead of 14 leaves within 40 years) and longer leaves of newer compared to older hybrids. Future selection and adaptation of maize hybrids to changing environmental conditions are likely to be the key for high productivity and quality and for the economic viability of maize growing and expansion in Northern Europe

What is the future for agroforestry in Italy?

The successful promotion of agroforestry in Italy depends on both a recognition of tradition and the opportunities for innovation. In Italy, agroforestry has traditionally been a key component of landscape management. Complex systems, based on the integration among crops–livestock–fruit/forest trees, provided a wide variety of products (e.g. food, feed, fibers, fuelwood and timber) and other ecosystem services (e.g. soil erosion control and biodiversity preservation). Silvopastoral systems have been used for centuries and are still managed in marginal areas. The integration of fruits trees (in primis olive trees) with crops and grazing was widely practiced and is still profitable. Coltura promiscua was historically developed integrating fruit and forest trees and particularly multifunctional trees (e.g. Juglans regia L. and Prunus avium L.) to support vines and intercrops. Building on recent research, projects have also focused on innovation in agroforestry. The adoption of shade tolerant forage species and crops has been studied in silvopastoral and olive systems. Silvopastoral systems can significantly offset the greenhouse gas emissions produced by livestock and shield grazing animals from “heat waves”. Integration of fast growing timber trees (like Populus) in arable systems can help reverse the decline in plantation forestry in Italy. Finally, the constraints imposed by the EU agricultural policy, especially the prevalent provisions for monocrops severely limiting the introduction of innovative agroforestry approaches, are discussed. New political measures and certification actions are strongly required