Distribution of soil carbon and microbial biomass in arable soils under different tillage regimes

We have measured total soil organic carbon (SOC), dissolved organic carbon (DOC), and microbial lipid contents (as indices of microbial biomass and community structure), and their distributions to 60 cm depth in soils from replicated medium-term (2003-2008) experimental arable plots subject to different tillage regimes in Scotland. The treatments were zero tillage (ZT), minimum tillage (MT; cultivation to 7 cm), the conventional tillage (CT) practice of ploughing to 20 cm, and deep ploughing (DP) to 40 cm depth. In the 0-30 cm depth range, SOC content (corrected for bulk density differences between tillage treatments) was greatest under ZT and MT, but over 0-60 cm depth the SOC contents of these treatments were similar to the CT and DP treatments. DOC concentrations declined with increasing depth in ZT and MT above 20 cm, but there were no significant differences with depth in the CT and DP treatments. Beneath 20 cm, there was little change in DOC concentration with depth for all treatments, although for the MT treatment, there was less DOC beneath the depth of cultivation. The total microbial biomass decreased with increasing depth over the 0-60 cm range in the ZT and MT treatments, whereas it decreased with depth only below 30-40 cm in the CT and DP treatments. The microbial biomass was significantly different only between 0-5 cm in the ZT, CT and DP treatments, but not for other depths between all treatments. The bacterial biomass was greater in the ZT treatment than in MT, CT and DP near the soil surface, but not significantly different over the whole profile (0-60 cm). The fungal biomass decreased with depth in the ZT and MT treatments over the whole 0-60 cm depth range, whereas it decreased with depth only below 20 cm in the CT and DP treatments.</p

Brief history of agricultural systems modeling

Agricultural systems science generates knowledge that allows researchers to consider complex problems or take informed agricultural decisions. The rich history of this science exemplifies the diversity of systems and scales over which they operate and have been studied. Modeling, an essential tool in agricultural systems science, has been accomplished by scientists from a wide range of disciplines, who have contributed concepts and tools over more than six decades. As agricultural scientists now consider the “next generation” models, data, and knowledge products needed to meet the increasingly complex systems problems faced by society, it is important to take stock of this history and its lessons to ensure thatwe avoid re-invention and strive to consider all dimensions of associated challenges. To this end, we summarize here the history of agricultural systems modeling and identify lessons learned that can help guide the design and development of next generation of agricultural system tools and methods. A number of past events combined with overall technological progress in other fields have strongly contributed to the evolution of agricultural system modeling, including development of process-based bio-physical models of crops and livestock, statisticalmodels based on historical observations, and economic optimization and simulation models at household and regional to global scales. Characteristics of agricultural systems models have varied widely depending on the systems involved, their scales, and the wide range of purposes that motivated their development and use by researchers in different disciplines. Recent trends in broader collaboration across institutions, across disciplines, and between the public and private sectors suggest that the stage is set for themajor advances in agricultural systems science that are needed for the next generation ofmodels, databases, knowledge products and decision support systems. The lessons fromhistory should be considered to help avoid roadblocks and pitfalls as the community develops this next generation of agricultural systems models