Soil Carbon and Nitrogen Dynamics of Integrated Crop-Pasture Systems with Annual and Perennial Forages

Increased demand for food and bioenergy crops and the subsequent intensification of crop production creates a challenge for the conservation of natural resources in Latin America and the world. In Uruguay, no-till cash-crop production area has increased from 0.4 to 1.5 million ha in the last decade (DIEA 2011) mostly at the expense of pastureland through expanding grain production to soils with lower land use capability. Production systems based on crop-pasture rotations shifted to a longer annual cropping phase with a shorter pasture phase, or to continuous annual crop-ping. Long-term experiments in the country have shown that the rotation of annual crops and perennial pastures minimizes soil erosion in tilled systems, maintaining a positive long-term soil carbon (C) and nitrogen (N) balance that contrasts with C and N losses in annual cropping systems (García-Préchac et al. 2004). Research and extension on soil conservation in crop-pasture systems have led to a massive adoption of no-tillage practices, reaching about 90% of cash crop area by the 2009 growing season (DIEA 2011). However, the gradual increase in no-till adoption by farm operators has been associated with a dramatic increase in continuous annual cropping to the detriment of the pasture phase of the rotation. Our overarching question is: What is the impact of an increased frequency of annual crops in the C and N cycling of these systems? The objective of this study was to assess the impact of the pasture phase and cropping intensity on the soil C and N cycling of an Oxyaquic Argiudoll soil of eastern Uruguay using long term field experimental data and a cropping systems simulation model

An intelligent interface for integrating climate, hydrology, agriculture, and socioeconomic models

Understanding the interactions between natural processes and human activities poses major challenges as it requires the integration of models and data across disparate disciplines. It typically takes many months and even years to create valid end-to-end simulations as different models need to be configured in consistent ways and generate data that is usable by other models. MINT is a novel framework for model integration that captures extensive knowledge about models and data and aims to automatically compose them together. MINT guides a user to pose a well-formed modeling question, select and configure appropriate models, find and prepare appropriate datasets, compose data and models into end-to-end workflows, run the simulations, and visualize the results. MINT currently includes hydrology, agriculture, and socioeconomic models.Office of the VP for Researc

The response of maize, sorghum, and soybean yield to growing-phase climate revealed with machine learning

Accurate representation of crop responses to climate is critically important to understand impacts of climate change and variability in food systems. We use Random Forest (RF), a diagnostic machine learning tool, to explore the dependence of yield on climate and technology for maize, sorghum and soybean in the US plains. We analyze the period from 1980 to 2016 and use a panel of county yields and climate variables for the crop-specific developmental phases: establishment, critical window (yield potential definition) and grain filling. The RF models accounted for between 71% to 86% of the yield variance. Technology, evaluated through the time variable, accounted for approximately 20% of the yield variance and indicates that yields have steadily increased. Responses to climate confirm prior findings revealing threshold-like responses to high temperature (yield decrease sharply when maximum temperature exceed 29 °C and 30 °C for maize and soybean), and reveal a higher temperature tolerance for sorghum, whose yield decreases gradually as maximum temperature exceeds 32.5 °C. We found that sorghum and soybean responded positively to increases in cool minimum temperatures. Maize yield exhibited a unique and negative response to low atmospheric humidity during the critical phase that encompasses flowering, as well as a strong sensitivity to extreme temperature exposure. Using maize as a benchmark, we estimate that if warming continues unabated through the first half of the 21st century, the best climatic conditions for rainfed maize and soybean production may shift from Iowa and Illinois to Minnesota and the Dakotas with possible modulation by soil productivity

Implications of carbon saturation model structures for simulated nitrogen mineralization dynamics

Carbon (C) saturation theory suggests that soils have a limited capacity to stabilize organic C and that this capacity may be regulated by intrinsic soil properties such as clay concentration and mineralogy. While C saturation theory has advanced our ability to predict soil C stabilization, few biogeochemical ecosystem models have incorporated C saturation mechanisms. In biogeochemical models, C and nitrogen (N) cycling are tightly coupled, with C decomposition and respiration driving N mineralization. Thus, changing model structures from non-saturation to C saturation dynamics can change simulated N dynamics. In this study, we used C saturation models from the literature and of our own design to compare how different methods of modeling C saturation affected simulated N mineralization dynamics. Specifically, we tested (i) how modeling C saturation by regulating either the transfer efficiency (ε, g C retained g−1 C respired) or transfer rate (k) of C to stabilized pools affected N mineralization dynamics, (ii) how inclusion of an explicit microbial pool through which C and N must pass affected N mineralization dynamics, and (iii) whether using ε to implement C saturation in a model results in soil texture controls on N mineralization that are similar to those currently included in widely used non-saturating C and N models. Models were parameterized so that they rendered the same C balance. We found that when C saturation is modeled using ε, the critical C : N ratio for N mineralization from decomposing plant residues (rcr) increases as C saturation of a soil increases. When C saturation is modeled using k, however, rcr is not affected by the C saturation of a soil. Inclusion of an explicit microbial pool in the model structure was necessary to capture short-term N immobilization–mineralization turnover dynamics during decomposition of low N residues. Finally, modeling C saturation by regulating ε led to similar soil texture controls on N mineralization as a widely used non-saturating model, suggesting that C saturation may be a fundamental mechanism that can explain N mineralization patterns across soil texture gradients. These findings indicate that a coupled C and N model that includes saturation can (1) represent short-term N mineralization by including a microbial pool and (2) express the effects of texture on N turnover as an emergent property

Assessment of the redistribution of soil carbon using a new index—a case study in the Haihe River Basin, North China

Abstract Soil carbon redistribution is an important process in the terrestrial carbon cycle. This study describes a new index, soil carbon redistribution (SCR) index, that can be used to assess long-term soil carbon redistribution at a large watershed scale. The new index is based on the theoretical preconditions that soil carbon redistribution is mainly controlled by vegetation type, precipitation, topography/slope, and soil carbon concentration. The Haihe River Basin served as an example for this analysis. The SCR index was calculated, and a GIS-based map shows its spatial patterns. The results suggested that soil carbon was usually prone to being carried away from mountainous regions with natural vegetation, while it was prone to deposition in the plain and plateau regions with cultivated vegetation. The methods in the paper offer a tool that can be used to quantify the potential risk where soil carbon is prone to being carried away and deposited in a large watershed

Predicting maize phenology: intercomparison of functions for developmental response to temperature

Accurate prediction of phenological development in maize (Zea mays L.) is fundamental to determining crop adaptation and yield potential. A number of thermal functions are used in crop models, but their relative precision in predicting maize development has not been quantified. The objectives of this study were (i) to evaluate the precision of eight thermal functions, (ii) to assess the effects of source data on the ability to differentiate among thermal functions, and (iii) to attribute the precision of thermal functions to their response across various temperature ranges. Data sets used in this study represent >1000 distinct maize hybrids, >50 geographic locations, and multiple planting dates and years. Thermal functions and calendar days were evaluated and grouped based on their temperature response and derivation as empirical linear, empirical nonlinear, and process-based functions. Precision in predicting phase durations from planting to anthesis or silking and from silking to physiological maturity was evaluated. Large data sets enabled increased differentiation of thermal functions, even when smaller data sets contained orthogonal, multi-location and -year data. At the highest level of differentiation, precision of thermal functions was in the order calendar days < empirical linear < process based < empirical nonlinear. Precision was associated with relatively low temperature sensitivity across the 10 to 26 degrees C range. In contrast to other thermal functions, process-based functions were derived using supra-optimal temperatures, and consequently, they may better represent the developmental response of maize to supra-optimal temperatures. Supra-optimal temperatures could be more prevalent under future climate-change scenarios, but data sets in this study contained few data in that range