Evaluation of Agricultural Production Systems Simulator (APSIM) as yield predictor of Panicum virgatum and Miscanthus x giganteus in several US environments

Simulation models for perennial energy crops such as switchgrass (Panicum virgatum L.) and Miscanthus (Miscanthus x giganteus) can be useful tools to design management strategies for biomass productivity improvement in US environments. The Agricultural Production Systems Simulator (APSIM) is a biophysical model with the potential to simulate the growth of perennial crops. APSIM crop modules do not exist for switchgrass and Miscanthus, however, re‐parameterization of existing APSIM modules could be used to simulate the growth of these perennials. Our aim was to evaluate the ability of APSIM to predict the dry matter (DM) yield of switchgrass and Miscanthus at several US locations. The Lucerne (for switchgrass) and Sugarcane (for Miscanthus) APSIM modules were calibrated using data from four locations in Indiana. A sensitivity analysis informed the relative impact of changes in plant and soil parameters of APSIM Lucerne and APSIM Sugarcane modules. An independent dataset of switchgrass and Miscanthus DM yields from several US environments was used to validate these re‐parameterized APSIM modules. The re‐parameterized modules simulated DM yields of switchgrass [0.95 for CCC (concordance correlation coefficient) and 0 for SB (bias of the simulation from the measurement)] and Miscanthus (0.65 and 0% for CCC and SB, respectively) accurately at most locations with the exception of switchgrass at southern US sites (0.01 for CCC and 2% for SB). Therefore, the APSIM model is a promising tool for simulating DM yields for switchgrass and Miscanthus while accounting for environmental variability. Given our study was strictly based on APSIM calibrations at Indiana locations, additional research using more extensive calibration data may enhance APSIM robustness.Fil: Ojeda, Jonathan Jesus. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ciencias Agropecuarias; ArgentinaFil: Volenec, Jeffrey J.. Purdue University; Estados UnidosFil: Brouder, Sylvie M.. Purdue University; Estados UnidosFil: Caviglia, Octavio Pedro. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ciencias Agropecuarias; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Entre Ríos. Estación Experimental Agropecuaria Paraná; ArgentinaFil: Agnusdei, Mónica G.. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Sur. Estación Experimental Agropecuaria Balcarce; Argentin

The impact of conservation agriculture on smallholder agricultural yields: A scoping review of the evidence

Widespread implementation of conservation agriculture (CA) in North and South America and Australia suggests significant farmer profitability achieved through some combination of sustained or increased agronomic productivity and reduced input costs. Many believe similar agronomic benefits can accrue to smallholder farmers in sub-Saharan Africa (SSA) and South Asia (SA) for a broad array of crops and farming systems despite marked differences in biophysical and socio-economic environments across these regions. Our objectives were to characterize (1) the quality of existing research including an assessment of the relevance of previously published reviews and surveys to SSA and SA, and (2) the empirical evidence from SSA and SA for agronomic benefits derived from implementing zero tillage (ZT) including the identification of knowledge gaps. Mulching and rotation were considered as associated practices within systems. Among surveys and reviews, most syntheses of multiple, independent studies were either entirely qualitative or used overly simplistic approaches to data aggregation. Few reviews used meta-analysis or other rigorous statistics that permit assessment of outcome sensitivity to influential observations; in general, review protocol descriptions were not sufficient to ensure transparency and appropriate handling of common biases. A search and screening of peer-reviewed literature identified empirical studies on conservation tillage in SSA and SA for maize (22), rice (16), cowpea (10) and sorghum (8). In attempting to extract data for an unbiased, systematic review of CA and maize, we found few studies fully reported critical data or meta-data; most common omissions were the univariate statistics required for study use in meta-analyses and critical supporting or explanatory data on soil type, prevailing weather, and management practices including handling of crop residues. In the short-term, ZT generally resulted in lower yields than with conventional tillage (CT). Occasionally these reductions could be linked to direct effects (e.g. increased soil compaction in rice), but failure to adapt other managements (e.g. weed control) to the CA system was a common and confounding indirect effect. Sufficient maize data existed to demonstrate that negative impacts on yield ameliorated with time in some cases accompanied by higher soil water infiltration and soil organic matter, particularly when mulch was added. However, the low number of studies, the missing supporting data and the large variation in treatments made it difficult to infer general direct effects due to mulching or rotation.Well-designed long-term experiments on CA featuring sound agronomic practice and comprehensive documentation are largely missing from the literature. Future systematic reviews addressing agronomic impacts of CA interventions will require appropriate handling of within and between study variance as well as sensitivity analyses and quantitative assessments of publication bias; on-going and future empirical studies must report a minimum dataset encompassing valid statistical measures and comprehensive intervention descriptions that enable standardization and systematic approaches in syntheses. We propose a minimum dataset that is generic to competent agronomy with measurements that are increasingly low-cost and easy to achieve and should therefore be routine in field experiments quantifying and explaining crop and cropping system performance. Until a larger number of field studies provide such quantifying and explanatory data from key crops and representative cropping systems, it is not possible to make strong general conclusions about benefits of CA and ZT on yields and resource use efficiency of smallholder farmers. © 2013 Elsevier B.V.Peer Reviewe

Evaluation of Agricultural Production Systems Simulator as yield predictor of Panicum virgatum and Miscanthus x giganteus in several US environments

Simulation models for perennial energy crops such as switchgrass (Panicum virgatum L.) and Miscanthus (Miscanthus x giganteus) can be useful tools to design management strategies for biomass productivity improvement in US environments. The Agricultural Production Systems Simulator (APSIM) is a biophysical model with the potential to simulate the growth of perennial crops. APSIM crop modules do not exist for switchgrass and Miscanthus, however, re-parameterization of existing APSIM modules could be used to simulate the growth of these perennials. Our aim was to evaluate the ability of APSIM to predict the dry matter (DM) yield of switchgrass and Miscanthus at several US locations. The Lucerne (for switchgrass) and Sugarcane (for Miscanthus) APSIM modules were calibrated using data from four locations in Indiana. A sensitivity analysis informed the relative impact of changes in plant and soil parameters of APSIM Lucerne and APSIM Sugarcane modules. An independent dataset of switchgrass and Miscanthus DM yields from several US environments was used to validate these re-parameterized APSIM modules. The re-parameterized modules simulated DM yields of switchgrass [0.95 for CCC (concordance correlation coefficient) and 0 for SB (bias of the simulation from the measurement)] and Miscanthus (0.65 and 0% for CCC and SB, respectively) accurately at most locations with the exception of switchgrass at southern US sites (0.01 for CCC and 2% for SB). Therefore, the APSIM model is a promising tool for simulating DM yields for switchgrass and Miscanthus while accounting for environmental variability. Given our study was strictly based on APSIM calibrations at Indiana locations, additional research using more extensive calibration data may enhance APSIM robustness.EEA ParanáFil: Ojeda, Jonathan Jesus. Universidad Nacional de Entre Ríos. Facultad de Ciencias Agropecuarias; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Volenec, Jeffrey J. Purdue University. Department of Agronomy; Estados UnidosFil: Brouder, Sylvie M. Purdue University. Department of Agronomy; Estados UnidosFil: Caviglia, Octavio. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Paraná. Grupo Ecología Forestal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ciencias Agropecuarias; ArgentinaFil: Agnusdei, Monica Graciela. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; Argentin

Modelling stover and grain yields, and subsurface artificial drainage from long-term corn rotations using APSIM

The Agricultural Production Systems Simulator (APSIM) is a key tool to identify agricultural management practices seeking to simultaneously optimize agronomic productivity and input use efficiencies. The aims of this study were to validate APSIM for prediction of stover and grain yield of corn in four contrasting soils with varied N fertilizer applications (156–269 kg N ha−1) and to predict timing and volume from artificial subsurface drains in continuous corn and corn-soybean rotations in a silty clay loam soil at West Lafayette, IN. The APSIM validation was carried-out using a long-term dataset of corn stover and grain yields from the North Central Region of IN. The CCC (Concordance Correlation Coefficient) and SB (Simulation Bias) were used to statistically evaluate the model performance. The CCC integrates precision through Pearson’s correlation coefficient and accuracy by bias, and SB indicates the bias of the simulation from the measurement. The model demonstrated very good (CCC = 0.96; SB = 0%) and satisfactory (CCC = 0.85; SB = 2%) ability to simulate stover and grain yield, respectively. Grain yield was better predicted in continuous corn (CCC = 0.73–0.91; SB = 19–21%) than in corn-soybean rotations (CCC = 0.56–0.63; SB = 17–18%), while stover yield was well predicted in both crop rotations (CCC = 0.85–0.98; SB = 1–17%). The model demonstrated acceptable ability to simulate annual subsurface drainage in both rotations (CCC = 0.63–0.75; SB = 2–37%) with accuracy being lower in the continuous corn system than in corn-soybean rotation system (CCC = 0.61-0.63; SB = 9–12%). Daily subsurface drainage events were well predicted by APSIM during late spring and summer when crop water use was high, but under-predicted during fall, winter and early spring when evapotranspiration was low. Occasional flow events occurring in summer when soils were not saturated were not predicted by APSIM and may represent preferential flow paths currently not represented in the model. APSIM is a promising tool for simulating yield and water losses for corn-based cropping systems in north central Indiana US.Fil: Ojeda, Jonathan Jesus. Universidad Nacional de Entre Ríos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Volenec, Jeffrey J.. Purdue University; Estados UnidosFil: Brouder, Sylvie M.. Purdue University; Estados UnidosFil: Caviglia, Octavio Pedro. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Entre Ríos; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires; ArgentinaFil: Agnusdei, Mónica G.. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires; Argentin

Nitrogen Reserve Pools in Two Miscanthus × giganteus Genotypes under Contrasting N Managements

Nitrogen (N) reserves in vegetative tissues contribute N to regrowth of Miscanthus × giganteus shoots in spring, but our understanding of how N fertilization and plant genotype affect this process is incomplete. Our specific objectives were to: (1) determine how N fertilizer management impacts accumulation of dry matter and N among aboveground and belowground tissues and organs; (2) understand how changes in N management and tissue N concentration influence seasonal fluctuations in concentrations of buffer-soluble proteins and amino acids in putative storage organs including rhizomes and roots; and (3) characterize genotypic variability and genotype × N interactions for N reserve accumulation and use among Miscanthus × giganteus genotypes. Established plots of the IL Clone and Nagara-sib population were fertilized with 0–0, 0–150, 75–75, 150–0, and 150–150 kg N ha-1 where the first numeral denotes the N rate applied in 2011 (Year 1) and the second number denotes the N rate applied in 2012 (Year 2). Rhizomes, roots, stembases, and shoots were sampled at 6-week intervals between March and August and then in November at dormancy. Concentrations of N, soluble protein and amino-N increased in all tissues with fertilizer N application. With the exception of rhizome amino-N, concentrations of these N pools in roots and rhizomes declined as plants resumed growth in spring and increased sharply between August and November as growth slowed. Losses in shoot and stembase N mass between August and November were similar to total N accumulation in roots and rhizomes during this interval. Compared to the unfertilized control, specific N managements enhanced growth of above- and belowground tissues. The IL Clone generally had greater biomass yield of all organs than the Nagara-sib; the exception being shoot biomass in November when extensive leaf senescence reduce yield of the IL Clone. High biomass yields were obtained with 75 kg N ha-1 applied annually rather than semi-annual N applications of 150 kg N-1 ha that depended on N recycling from roots/rhizomes as a supplemental N source

Can crop simulation models be used to predict local to regional maize yields and total production in the U.S. Corn Belt?

Crop simulation models are used at the field scale to estimate crop yield potential, optimize current management, and benchmark input-use efficiency. At issue is the ability of crop models to predict local and regional actual yield and total production without need of site-year specific calibration of internal parameters associated with fundamental physiological processes. In this study, a well-validated maize simulation model was used to estimate yield potential for 45 locations across the U.S. Corn Belt, including both irrigated and rainfed environments, during four years (2011–2014) that encompassed diverse weather conditions. Simulations were based on measured weather data, dominant soil properties, and key management practices at each location (including sowing date, hybrid maturity, and plant density). The same set of internal model parameters were used across all site-years. Simulated yields were upscaled from locations to larger spatial domains (county, agricultural district, state, and region), following a bottom-up approach based on a climate zone scheme and distribution of maize harvested area. Simulated yields were compared against actual yields reported at each spatial level, both in absolute terms as well as deviations from long-term averages. Similar comparisons were performed for total maize production, estimated as the product of simulated yields and official statistics on maize harvested area in each year. At county-level, the relationship between simulated and actual yield was better described by a curvilinear model, with decreasing agreement at higher yields (>12Mgha−1). Comparison of actual and simulated yield anomalies, as estimated from the yearly yield deviations from the long-term actual and simulated average yield, indicated a linear relationship at county-level. In both cases (absolute yields and yield anomalies comparisons), the agreement increased with increasing spatial aggregation (from county to region). An approach based on long-term actual and simulated yields and year-specific simulated yield allowed estimation of actual yield with a high degree of accuracy at county level (RMSE≤18%), even in years with highly favorable weather or severe drought. Estimates of total production, which are of greatest interest to buyers and sellers in the market, were also in close agreement with actual production (RMSE≤22%). The approach proposed here to estimate yield and production can complement other approaches that rely on surveys, field crop cuttings, and empirical statistical methods and serve as basis for in-season yield and production forecasts