In this paper, we advanced the concept of nitrogen-use efficiency (NUE) of cropping systems by linking current crop- and soil-based approaches into a system NUE. We compared this new index to ...traditional metrics, and show how its application can yield insights about N cycling dynamics and tradeoffs.
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•Developed a novel systems NUE (sNUE) by synthesizing crop and soil-based approaches.•The maize-soybean rotation removed 87% of N inputs in grains.•45% of the N losses were due to inefficient N input use; the rest to poor N retention.•Balancing production-environmental tradeoffs resulted on a yield penalty of 5–7%.•sNUE was relatively more stable and less correlated to other metrics.
Increasing nitrogen (N)-use efficiency (NUE) is key to improving crop production while mitigating ecologically-damaging environmental N losses. Traditional approaches to assess NUE are principally focused on evaluating crop responses to N inputs, often consider only what happens during the growing season, and ignore other means to improve system efficiency, such as by tightening the cycling of soil N (e.g. with N scavenging cover crops). As the goals of improving production and environmental quality converge, new metrics that can simultaneously capture multiple aspects of system performance are needed. To fill this gap, we developed a theoretical framework that links both crop- and soil-based approaches to derive a system N-use efficiency (sNUE) index. This easily interpretable metric succinctly characterizes N cycling and facilitates comparison of systems that differ in biophysical controls on N dynamics. We demonstrated the application of this new approach and compared it to traditional NUE metrics using data generated with a process-based model (APSIM), trained and tested with experimental datasets (Iowa, USA). Modeling of maize-soybean rotations indicated that despite their high crop NUE, only 45% of N losses could be attributed to the inefficient use of N inputs, whereas the rest originated from the release of native soil N into the environment, due to the asynchrony between soil mineralization and crop uptake. Additionally, sNUE produced estimates of system efficiency that were more stable across weather years and less correlated to other metrics across distinct crop sequences and N fertilizer input levels. We also showed how sNUE allows for the examination of tradeoffs between N cycling and production performance, and thus has the potential to aid in the design of systems that better balance production and environmental outcomes.
Improved prediction of optimal N fertilizer rates for corn (
) can reduce N losses and increase profits. We tested the ability of the Agricultural Production Systems sIMulator (APSIM) to simulate ...corn and soybean (
) yields, the economic optimum N rate (EONR) using a 16-year field-experiment dataset from central Iowa, USA that included two crop sequences (continuous corn and soybean-corn) and five N fertilizer rates (0, 67, 134, 201, and 268 kg N ha
) applied to corn. Our objectives were to: (a) quantify model prediction accuracy before and after calibration, and report calibration steps; (b) compare crop model-based techniques in estimating optimal N rate for corn; and (c) utilize the calibrated model to explain factors causing year to year variability in yield and optimal N. Results indicated that the model simulated well long-term crop yields response to N (relative root mean square error, RRMSE of 19.6% before and 12.3% after calibration), which provided strong evidence that important soil and crop processes were accounted for in the model. The prediction of EONR was more complex and had greater uncertainty than the prediction of crop yield (RRMSE of 44.5% before and 36.6% after calibration). For long-term site mean EONR predictions, both calibrated and uncalibrated versions can be used as the 16-year mean differences in EONR's were within the historical N rate error range (40-50 kg N ha
). However, for accurate year-by-year simulation of EONR the calibrated version should be used. Model analysis revealed that higher EONR values in years with above normal spring precipitation were caused by an exponential increase in N loss (denitrification and leaching) with precipitation. We concluded that long-term experimental data were valuable in testing and refining APSIM predictions. The model can be used as a tool to assist N management guidelines in the US Midwest and we identified five avenues on how the model can add value toward agronomic, economic, and environmental sustainability.
Mineral-associated organic matter (MAOM) is a relatively large and stable fraction of soil organic matter (SOM). Plant litters with high rates of mineralization (high quality litters) are ...hypothesized to promote the accumulation of MAOM with greater efficiency than plant litters with low rates of mineralization (low-quality litters) because litters with high rates of mineralization maximize the synthesis of microbial products and most MAOM is microbial-derived. However, the effect of litter quality on MAOM is inconsistent. We conducted four repeated short-term incubations (46-d each) of four plant litters (alfalfa, oats, maize and soybean) in two low-carbon subsoils (sandy loam and silty loam) with and without nutrient addition. Our short-term incubations focused on the initial stage of litter decomposition during the time when litter quality has a measureable effect on mineralization rates. Plant litter quality had a much greater effect on litter-C mineralization rate and MAOM-C accumulation than did soil type or nutrient addition. Soils amended with high-quality oat and alfalfa litters had greater MAOM-C accumulation than soils amended with low-quality maize and soybean litters. However, soils amended with high-quality litters also had greater litter-C mineralization than soils amended with low-quality litters. As a result, the accumulation of MAOM-C per unit of litter-C mineralization was lower in soils amended with high-vs. low-quality litters (0.65 vs. 1.39 g MAOM-C accumulated g−1 C mineralized). Cellulose and hemicelluose indices of accumulated MAOM were greater for maize and soybean than oats and alfalfa, however, most carbohydrates in MAOM were plant-derived regardless of litter quality. At the end of the incubations, more of the accumulated MAOM-N was potentially mineralizable in soils amended with high quality litters. Nevertheless, most of the litter-C remained as residual litter; just 12% was mineralized to CO2 and 13% was transferred to MAOM. Our results demonstrate several unexpected effects of litter quality on MAOM stabilization including the direct stabilization of plant-derived carbohydrates.
•Litter-C accumulation in the mineral soil matrix was less efficient for high vs. low quality litters.•Low quality litters had greater C accumulation in the mineral soil matrix per unit of C mineralized.•Carbohydrates in the mineral soil matrix were mostly plant-derived regardless of litter quality.
Soil moisture of maize has an extremely important impact on the growth and development of maize. Failure to accurately estimate soil moisture will lead to severe reductions in maize yields and thus ...intensify the global food crisis, so it is extremely important to accurately estimate soil moisture of maize. This study proposes a new hybrid machine learning model (SVM-SWOA) that incorporates the Whale Optimization Algorithm (WOA) into sinusoidal chaotic graphs and couples it with a support vector machine (SVM). The model is with both high convergence speed and high accuracy. After using the data from two maize agricultural districts in Iowa, USA for model creation, Taylor plots and significance tests were used to enable the model for identifying input variables. To verify the performance of the model, SVM-SWOA was comprehensively evaluated with both SVM and SVM-WOA models. Results showed that SVM-SWOA was improved 14%, 13%, 41.5%, and 14% over SVM-WOA at 60 cm depth for MAE, RMSE, MAPE, and MBE, respectively, and 20%, 29.5%, 44.5%, and 38% over SVM, respectively. It implies that the SVM-SWOA meta-heuristic algorithm can provide better guidance for smart agriculture and precision irrigation.
•A novel hybrid SVM-SWOA model was proposed for estimating soil moisture at different depths of maize root zone.•The SVM-SWOA model is superior to the SVM-WOA and SVM models.•Soil moisture changes were estimated for the next 1–7 days.•The estimation accuracy of soil moisture does not always increase with soil depth.
Agricultural systems are being challenged to decrease water use and increase production while climate becomes more variable and the world's population grows. Low water use efficiency is traditionally ...characterized by high water use relative to low grain production and usually occurs under dry conditions. However, when a cropping system fails to take advantage of available water during wet conditions, this is also an inefficiency and is often detrimental to the environment. Here, we provide a systems‐level definition of water use efficiency (sWUE) that addresses both production and environmental quality goals through incorporating all major system water losses (evapotranspiration, drainage, and runoff). We extensively calibrated and tested the Agricultural Production Systems sIMulator (APSIM) using 6 years of continuous crop and soil measurements in corn‐ and soybean‐based cropping systems in central Iowa, USA. We then used the model to determine water use, loss, and grain production in each system and calculated sWUE in years that experienced drought, flood, or historically average precipitation. Systems water use efficiency was found to be greatest during years with average precipitation. Simulation analysis using 28 years of historical precipitation data, plus the same dataset with ± 15% variation in daily precipitation, showed that in this region, 430 mm of seasonal (planting to harvesting) rainfall resulted in the optimum sWUE for corn, and 317 mm for soybean. Above these precipitation levels, the corn and soybean yields did not increase further, but the water loss from the system via runoff and drainage increased substantially, leading to a high likelihood of soil, nutrient, and pesticide movement from the field to waterways. As the Midwestern United States is predicted to experience more frequent drought and flood, inefficiency of cropping systems water use will also increase. This work provides a framework to concurrently evaluate production and environmental performance of cropping systems.
ABSTRACT
We studied temporal dynamics of above‐ and belowground growth and N use in three production systems: maize (C4 annual), reconstructed prairie (a mixture of perennial C3 and C4 species) and ...fertilized reconstructed prairie. Our objectives were to fill knowledge gaps about temporal patterns of growth (especially for roots), inform further experimental research, and provide quantitative datasets for modeling. A 2‐yr field study was conducted near Boone, IA, in which above‐ and belowground plant tissues were sampled repeatedly during each growing season (n = 15 aboveground, n = 4 belowground). Dry weight and tissue N concentration were measured, and growth rates and N productivity were estimated. We found that maize produced more aboveground biomass (18.4 Mg/ha) with higher growth rates that peaked later in the season compared with the two prairie treatments (9.4–14.9 Mg/ha/year). Maize allocated a smaller proportion of its biomass belowground (10%) than both prairie treatments did (20–40%). Overall the N productivity ranged from 11 to 135 kg biomass kg–1 thermal unit–1 for maize and 34 to 344 kg biomass kg–1 thermal unit–1 for both prairie treatments. Our findings provide new, quantitative data that will be useful for predicting system‐level processes of annual and perennial crops evaluated for food and biofuel production.
•We assessed weather forecasts impacts on APSIM predictions of yield and phenology.•Observations were taken at 2 sites for 2 crops with 2 planting dates over 2 years.•Multiple forecast lengths (0,3,7 ...and 14day) were tested.•Yield prediction accuracy was inversely proportional to forecast length.•A combination of forecast lengths could be the most beneficial.
Accurately forecasting crop yield in advance of harvest could greatly benefit decision makers when making management decisions. However, few evaluations have been conducted to determine the impact of including weather forecasts, as opposed to using historical weather data (commonly used) in crop models. We tested a combination of short-term weather forecasts from the Weather Research and Forecasting Model (WRF) to predict in season weather variables, such as, maximum and minimum temperature, precipitation, and radiation at four different forecast lengths (14days, 7days, 3days, and 0days). This forecasted weather data along with the current and historic (previous 35 years) data were combined to drive Agricultural Production Systems sIMulator (APSIM) in-season corn Zea mays L and soybean Glycine max grain yield and phenology forecasts for 16 field trials in Iowa, USA. The overall goal was to determine how the inclusion of weather forecasting impacts in-season crop model predictions. We had two objectives 1) determine the impact of weather forecast length on WRF accuracy, and 2) quantify the impact of weather forecasts accuracy on APSIM prediction accuracy. We found that the most accurate weather forecast length varied greatly among the 16 treatments (2 years×2 sites×2 crops×2 management practices), but that the 0day and 3day forecasts were, on average, the most accurate when compared to the other forecast lengths. Overall, the accuracy of the in-season crop yield forecast was inversely proportional to forecast length (p=0.026), but there was variation among treatments. The accuracy of the in-season flowering and maturity forecasts were not significantly affected by inclusion of weather forecast length (p=0.065). The 14day forecast provided enough lead time to improve flowering prediction in 8 out of the 16 treatments. The fact that maximum temperature was the most accurate predicted variable by WRF was the reason for improvements in flowering predictions. Our results suggest that a weather forecast from WRF was not better than historical weather for yield prediction.
•We synthesized measured and modeling data to examine rye effects on maize systems.•Soil water deficit caused by rye transpiration affected maize yield only in drought years.•34% of the precipitation ...ended up in subsurface drainage at this site.•Rye produced 47kgha−1 of shoot biomass per each mm of water used.•APSIM simulated reduced NO3-N losses (26±26%), but not drainage (4±13%) or yield (2±6%).
Inclusion of a rye cover crop into maize-based systems can offer environmental benefits, but adoption of the practice in the US Midwest is still low. This is related to the possible risk of reduced maize yields following rye. We hypothesized that the magnitude of rye effects on maize yields and drainage water and nitrate (NO3)-N losses would be proportionally related to rye biomass. We tested this hypothesis by analyzing data from continuous maize treatments (with and without cover crop) in Iowa, US, that were fertilized following recommendations from late spring nitrate tests. Dataset included measurements (2009–2014) of soil water and temperature, drainage water and NO3-N losses, soil NO3, rye shoot and root biomass and C:N, and maize yields. We supplemented our analysis with a literature review and the use of a cropping systems model (APSIM) to calculate trade-offs in system performance characteristics. Experimentally, rye cover crop reduced drainage by 12% and NO3-N losses by 20% (or 31% per unit of N applied), and maize yields by 6%. We also found minimal effects on soil temperature, water deficits that reduced yields only during drought years (2012 and 2013), and lower NO3-N losses that were related to reduced NO3-N concentrations in drainage. Results also revealed a linear relationship between drainage and precipitation (r2=0.96), and rye transpiration and shoot biomass (r2=0.84). Model scenario analysis (4 termination dates×30years) indicated that rye cover crop decreases NO3-N losses (-25.5±26%) but does not always reduce drainage water (-3.9±13%) or grain yields (-1.84±6%), which is consistent with experimental and literature results. However, analysis of the synthesized measured and simulated dataset do not support a strong relationship between these variables and rye biomass. These results are valuable for decision-making and add new fundamental knowledge on rye water and nitrogen use.
•Biological N fixation (BNF) was maximized early in the seed filling period, achieving a maximum rate of 3 kg N ha–1 day–1.•Biomass production was a better predictor of BNF than seed protein or ...environmental factors.•The BNF rate during seed-fill was driven by crop growth rate (C supply) rather than seed N accumulation rate (N demand).•The N exported in seed exceeded the N derived from BNF, indicating a negative partial N balance for soybean production.
The rainfed USA Midwestern region has deep, fertile soils and leads the USA in soybean Glycine max, (L.) Merr. production. Biological nitrogen (N) fixation (BNF) contributes a portion of the soybean N requirement, but variability in BNF is poorly understood and estimates of BNF for this region are rare. We established experiments in Iowa, USA to gain a better understanding of BNF and increase its predictability. We collected in-season BNF measurements accompanied by high temporal resolution soil and plant growth measurements. Across two years, two locations and two planting dates, we found that BNF contributed 23–65% of total aboveground N accumulation in soybean. The BNF rate was maximized at the early seed-filling period and varied from 1 to 3 kg N ha−1day−1. During seed filling period, the rate of BNF was related to crop growth rate (carbon (C) supply) but not to N accumulation by the reproductive organs (N demand). We found that a minimum crop growth rate of 135 kg dry matter ha−1day-1 is required to sustain maximum BNF rates. In contrast to BNF, the soil inorganic N uptake rate was related to seed N demand but not to C supply. Biomass production was the best predictor of total soybean BNF (R2 > 0.83). On average, 0.013 kg N was fixed per kg biomass produced. Across all trials, the N exported via seed was greater than the N imported via BNF, which suggests that Midwest US soybeans may reduce soil organic matter. We concluded that future research efforts should focus on increasing C – rather than N – availability during the seed filling period towards improving both grain yields and environmental sustainability.
We used the Agricultural Production Systems sIMulator (APSIM) to predict and explain maize and soybean yields, phenology, and soil water and nitrogen (N) dynamics during the growing season in Iowa, ...USA. Historical, current and forecasted weather data were used to drive simulations, which were released in public four weeks after planting. In this paper, we (1) describe the methodology used to perform forecasts; (2) evaluate model prediction accuracy against data collected from 10 locations over four years; and (3) identify inputs that are key in forecasting yields and soil N dynamics. We found that the predicted median yield at planting was a very good indicator of end‐of‐season yields (relative root mean square error RRMSE of ∼20%). For reference, the prediction at maturity, when all the weather was known, had a RRMSE of 14%. The good prediction at planting time was explained by the existence of shallow water tables, which decreased model sensitivity to unknown summer precipitation by 50–64%. Model initial conditions and management information accounted for one‐fourth of the variation in maize yield. End of season model evaluations indicated that the model simulated well crop phenology (R2 = 0.88), root depth (R2 = 0.83), biomass production (R2 = 0.93), grain yield (R2 = 0.90), plant N uptake (R2 = 0.87), soil moisture (R2 = 0.42), soil temperature (R2 = 0.93), soil nitrate (R2 = 0.77), and water table depth (R2 = 0.41). We concluded that model set‐up by the user (e.g. inclusion of water table), initial conditions, and early season measurements are very important for accurate predictions of soil water, N and crop yields in this environment.
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