Catálogo de publicaciones - revistas

<jats:title>Abstract </jats:title><jats:p>Conservation agriculture is often assumed to reduce soil N<jats:sub>2</jats:sub>O emissions. Yet, studies analyzing the specific effect of conservation agriculture practices on N<jats:sub>2</jats:sub>O emissions give contradictory results. Herein, we synthesized a comprehensive database on the three main conservation agriculture practices (cover crops, diversified crop rotations, and no-till and/or reduced tillage (NT/RT)) to elucidate the role of conservation practices on N<jats:sub>2</jats:sub>O emissions. Further, we used a random meta-forest approach to identify the most important predictors of the effects of these practices on soil N<jats:sub>2</jats:sub>O emissions. Averaged across all comparisons, NT/RT significantly decreased soil N<jats:sub>2</jats:sub>O emissions by 11% (95% CI: –19 to –1%) compared to conventional tillage. The reductions due to NT/RT were more commonly observed in humid climates and in soils with an initial carbon content &lt; 20 g kg<jats:sup>–1</jats:sup>. The implementation of cover crops and diversified crop rotations led to variable effects on soil N<jats:sub>2</jats:sub>O emissions. Cover crops were more likely to reduce soil N<jats:sub>2</jats:sub>O emissions at neutral soil pH, and in soils with intermediate carbon (~20 g kg<jats:sup>–1</jats:sup>) and nitrogen (~3 g kg<jats:sup>–1</jats:sup>) contents. Diversified crop rotations tended to increase soil N<jats:sub>2</jats:sub>O emissions in temperate regions and neutral to alkaline soils. Our results provide a comprehensive predictive framework to understand the conditions in which the adoption of various conservation agriculture practices can contribute to climate change mitigation. Combining these results with a similar mechanistic understanding of conservation agriculture impacts on ecosystem services and crop production will pave the way for a wider adoption globally of these management practices.</jats:p>

<jats:title>Abstract</jats:title><jats:p>Soil health metrics with strong links to ecological function and agricultural productivity are needed to ensure that future management of agricultural systems meets sustainability goals. While ecological metrics and crop yields are often considered separately from one another, our work sought to assess the links between the two in an agricultural context where productivity is a key consideration. Here, we investigated the value of soil health tests in terms of their relevance to agricultural management practices and crop yields at contrasting long term cropping systems experiments. One site was on a sandy loam Leptic Podzol and the other on a sandy clay loam Endostagnic Luvisol. Furthermore, the experiments had different management systems. One contained legume-supported rotations with different grass-clover ley durations and organic amendment usage, while the other compared a range of nutrient input options through fertiliser and organic amendments on the same rotation without ley periods. Metrics included field tests (earthworm counts and visual evaluation of soil structure scores) with laboratory analysis of soil structure, chemistry and biology. This analysis included bulk density, macroporosity, pH, available phosphorus, exchangeable potassium, soil organic matter and potentially mineralizable nitrogen. Using a novel combination of long-term experiments, management systems and distinctive soil types, we demonstrated that as well as providing nutrients, agricultural management which resulted in better soil organic matter, pH, potassium and bulk density was correlated with higher crop yields. The importance of ley duration and potentially mineralizable nitrogen to yield in legume-supported systems showed the impact of agricultural management on soil biology. In systems with applications of synthetic fertiliser, earthworm counts and visual evaluation of soil structure scores were correlated with higher yields. We concluded that agricultural management altered yields not just through direct supply of nutrients to crops, but also through the changes in soil health measured by simple metrics.</jats:p>

<jats:title>Abstract</jats:title><jats:p>Sustainable food production for a growing world population will pose a central challenge in the coming decades. Organic farming is among the feasible approaches to achieving this goal if the yield gap to conventional farming can be decreased. However, uncertainties exist to which extend—and for which phenotypes in particular—organic and conventional agro-ecosystems require differentiated breeding strategies. To answer this question, a heterogeneous spring barley population was established between a wild barley and an elite cultivar to examine this question. This initial population was divided into two sets and sown one in organic and the other in conventional managed agro-ecosystems, without any artificial selection for two decades. A fraction of seeds harvested each year was sown the following year. Various generations, up to the 23th were whole-genome pool-sequenced to identify adaptation patterns towards ecosystem and climate conditions in the allele frequency shifts. Additionally, a meta-data analysis was conducted to link genomic regions’ increased fitness to agronomically related traits. This long-term experiment highlights for the first time that allele frequency pattern difference between the conventional and organic populations grew with subsequent generations. Further, the organic-adapted population showed a higher genetic heterogeneity. The data indicate that adaptations towards new environments happen in few generations. Drastic interannual changes in climate are manifested in significant allele frequency changes. Particular wild form alleles were positively selected in both environments. Clustering these revealed an increased fitness associated with biotic stress resistance, yield physiology, and yield components in both systems. Additionally, the introduced wild alleles showed increased fitness related to root morphology, developmental processes, and abiotic stress responses in the organic agro-ecosystem. Concluding the genetic analysis, we demonstrate that breeding of organically adapted varieties should be conducted in an organically managed agro-ecosystem, focusing on root-related traits, to close the yield gap towards conventional farming.</jats:p>

<jats:title>Abstract</jats:title><jats:p>Conservation Agriculture (CA) is actively promoted as an alternative farming system that combines environmental, economic, and social sustainability. Three pillars define CA: (i) minimum mechanical soil disturbance, (ii) permanent soil organic cover, and (iii) species diversification. The local context, constraints, and needs of the farmers influence the translation of the pillars into practices. Currently, there is no method for categorizing this diversity of CA practices, which hampers impact assessment, understanding of farmer choices and pathways, stakeholder communication, and policymaking. This paper presents a systematic method to identify and categorize the diversity of CA practices at the regional level, anchored in the three pillars and based on practices implemented by CA farmers. The classification method is grounded on the intersection of an archetypal analysis and a hierarchical clustering analysis. This method was used to study CA practices in Wallonia, Belgium, based on a survey of practices in a sample of 48 farmers. Combining the two clustering methods increases the proportion of classified farmers while allowing for the distinction between three CA-types with extreme and salient practices, and two intermediate CA-types comprising farmers whose practices fall between these references. The study reveals that three explanatory factors influence the implementation of CA practices in Wallonia: (i) the proportion of tillage-intensive crops and (ii) temporary grasslands in the crop sequence, and (iii) the organic certification. These factors lead to trade-offs that hinder the three pillars of CA from being fully implemented simultaneously. This new classification method can be replicated in other regions where CA is practiced, by adapting input variables according to context and local knowledge.</jats:p>