How does inclusion of weather forecasting impact in-season crop model predictions?

Accurately forecasting crop yield in advance of harvest could greatly benefit decision makers. However, few evaluations have been conducted to determine the effectiveness of including weather forecasts, as opposed to using historical/climatology data, into 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 (14 days, 7 days, 3 days, and 0 days). 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 forecasts of corn [Zea mays L] and soybean [Glycine max] crop yield and phenology in Iowa, USA. The overall goal of this research was to determine how the inclusion of weather forecasting impacts in-season crop model predictions. To achieve this goal we had two objectives 1) to determine the dependence of the accuracy of APSIM yield and phenology predictions on weather forecast length, and 2) the impact of weather forecasts accuracy on APSIM prediction accuracy. APSIM simulations of biomass accumulation and phenology were evaluated against bi-weekly field measurements across 16 field trials (two years, 2015 and 2016; two sites, central and northwest Iowa, USA; two crops, corn and soybean; and two planting dates; early May vs early June). We hypothesized that 1) the accuracy and variability of crop yield predictions will be inversely proportional to the weather forecast length and 2) the inclusion of an explicit weather forecast will reduce crop yield prediction uncertainty and produce a reliable estimate with more lead time relative to using historical variation alone. The accuracy of in-season yield forecasts of corn and soybean varied by treatment, but overall the accuracy was inversely proportional to forecast length (P \u3c 0.05). Our analysis indicated that the most accurate forecast length varied greatly among the 16 treatments, but that the 0 day and 3 day forecasts were, on average, the most accurate. That the 0 day forecast was most accurate meant that a weather forecast from WRF was not better than a weather forecast based on historical weather, however in these cases the difference between the accuracy of the 0 day forecast and the other forecast lengths was not enough to rule out using short-term weather forecasts. Our analysis indicated that there was not sufficient evidence to suggest forecasts of up to 14 days do not on average cause the APSIM predictions to be too inaccurate to use. This means that 14 day length forecasts could be used for management decisions that require lead time, but a combination of all of the forecast lengths should be used to make final decisions

Using SMAP and SMOS vegetation optical depth to measure crop water in vegetation

NASA\u27s Soil Moisture Active Passive (SMAP) and European Space Agency\u27s (ESA) Soil Moisture Ocean Salinity (SMOS) are two microwave remote sensing satellites. They were originally designed to measure soil moisture, but with an algorithm that already retrieves vegetation optical depth (VOD), they could also be used for vegetation measurements. VOD is the degree to which vegetation attenuates microwave radiation from the soil and may be an important product to quantify vegetation changes. SMAP and SMOS have some advantages to measure vegetation compared to existing practices. They can view the entire crop canopy as opposed to just the top layer, due to their ability to monitor soil moisture which is below the crop canopy. SMAP and SMOS also have on average a daily revisit time in the mid–latitudes. Knowing the location and amount of water in a crop canopy could be beneficial for remote sensing because as the crops grow and water becomes allocated differently, SMAP and SMOS are seeing water from many different sources(stems, leaves, ears, soil, etc.). These different sources of water will scatter radiation differently due to their varying sizes and shapes and accounting for water correctly could improve measurements of soil moisture and VOD. A challenge of using SMAP and SMOS is the need to know crop water on the ground for comparison to VOD from the satellites.Data from multiple field experiments were collected and analyzed to show where crop water is in different crop components at varying development stages. New empirical models that relate crop water to crop dry mass were also created with these in situ measurements. We will use this model to hopefully overcome the challenge of comparing satellite VOD to crop water. However, we need to verify that the model is accurate and actually telling us about crop water.To check accuracy of our new empirical model, SMAP and SMOS VOD were compared to crop water estimates from the Agricultural Integrated BIosphere Simulator (Agro-IBIS) at the South Fork SMAP Core Validation Site in Iowa. A crop model was used because it can obtain dry mass for multiple fields in the study area. This dry mass can then be converted to a crop water using our empirical model for comparison to SMAP and SMOS VOD. We find that SMAP and SMOS VOD are directly proportional to crop water. We also found the value of the proportionality constant (or b-parameter ) relating VOD to crop water at the satellite scale is about half as large as previous estimates. After finding that SMAP and SMOS VOD are directly proportional to crop water we wanted to validate SMAP and SMOS VOD with in situ data from the field campaign SMAP Validation Experiment 2016. We found that SMAPv2 VOD had the highest R2 value. The b–parameter was also shown to change over time and that other sources of water in the SMAP and SMOS pixel may need to be taken into account when calculating ab–parameter. Because L-band VOD is directly proportional to crop water at the satellite scale, and because we understand the relationship between crop water and crop dry mass, SMAP and SMOS have the potential to evaluate the large-scale performance of crop models in the Corn Belt on a near daily basis

Quantitative Assessment of Satellite L-Band Vegetation Optical Depth in the U.S. Corn Belt

Satellite L-band vegetation optical depth (L-VOD) contains new information about terrestrial ecosystems. However, it has not been evaluated against the geophysical variable that it represents, plant water, the mass of liquid water contained within vegetation tissue per ground area. We quantitatively assess the seasonal variation of three L-VOD products at the South Fork Core Validation Site in the Corn Belt state of Iowa where L-VOD is directly proportional to crop plant water. We use three satellite-scale crop plant water estimates: in situ measurements; a normalized difference water index (NDWI) calibrated with in situ measurements; and a crop model. We find that overall the L-VOD satellite products are 0.02-0.09 Np (0.4-1.7 kg · m⁻²) lower than the three estimates. We show that overestimation of L-VOD can be attributed to dynamic soil surface roughness, and hypothesize that crop plant water observations will require the incorporation of this effect into retrieval algorithms

Forecasting yields and in-season crop-water nitrogen needs using simulation models

Forecasting crop yields and water-nitrogen dynamics during the growing cycle of the crops can greatly advance in-season decision making processes. To date, forecasting approaches include the use of statistical or mechanistic simulation models, aerial images, or combinations of these to make the predictions. Different approaches and models have different capabilities, strengths, and limitations. System-level mechanistic simulation models (crop and soil models together) usually offer more prediction and explanatory power at the cost of extensive input data. In contrast, statistical approaches or aerial images can be more robust than mechanistic models but their applicability and prediction/explanatory power is limited. The combination of these technologies is viewed as a very promising tool to assist Midwestern agriculture, but in general, all of these technologies are in their initial stages of implementation and more time is needed to prove their potential. Here we present results from a pilot project that aimed to forecast weather, soil water-nitrogen status, crop water-nitrogen demand, and end-of-season crop yields in Iowa using two process-based mechanistic simulation models

Understanding the 2016 yields and interactions between soils, crops, climate and management

Several technologies to forecast crop yields and soil nutrient dynamics have emerged over the past years. These include process-based models, statistical models, machine learning, aerial images, or combinations. These technologies are viewed as promising to assist Midwestern agriculture to achieve production and environmental goals, but in general, most of these technologies are in their initial stages of implementation. In June 2016 we launched a web-tool (http://crops.extension.iastate.edu/facts/) that provided real-time information and yield predictions for 20 combinations of crops and management practices. Our project, which is called FACTS (Forecast and Assessment of Cropping sysTemS), takes a systems approach to forecast and evaluate cropping systems performance. In this paper we report FACTS yield predictions accuracy against ground-truth measurements and analyzing factors responsible for achieving 200-240 bu/acre corn yield and 55-75 bu/acre soybean yields in the FACTS plots in 2016

Maize and soybean root front velocity and maximum depth in Iowa, USA

Quantitative measurements of root traits can improve our understanding of how crops respond to soil and weather conditions, but such data are rare. Our objective was to quantify maximum root depth and root front velocity (RFV) for maize (Zea mays) and soybean (Glycine max) crops across a range of growing conditions in the Midwest USA. Two sets of root measurements were taken every 10–15 days: in the crop row (in-row) and between two crop rows (center-row) across six Iowa sites having different management practices such as planting dates and drainage systems, totaling 20 replicated experimental treatments. Temporal root data were best described by linear segmental functions. Maize RFV was 0.62 ± 0.2 cm d−1 until the 5th leaf stage when it increased to 3.12 ± 0.03 cm d−1 until maximum depth occurred at the 18th leaf stage (860 °Cd after planting). Similar to maize, soybean RFV was 1.19 ± 0.4 cm d−1 until the 3rd node when it increased to 3.31 ± 0.5 cm d−1 until maximum root depth occurred at the 13th node (813.6 °C d after planting). The maximum root depth was similar between crops (P \u3e 0.05) and ranged from 120 to 157 cm across 18 experimental treatments, and 89–90 cm in two experimental treatments. Root depth did not exceed the average water table (two weeks prior to start grain filling) and there was a significant relationship between maximum root depth and water table depth (R2 = 0.61; P = 0.001). Current models of root dynamics rely on temperature as the main control on root growth; our results provide strong support for this relationship (R2 \u3e 0.76; P \u3c 0.001), but suggest that water table depth should also be considered, particularly in conditions such as the Midwest USA where excess water routinely limits crop production. These results can assist crop model calibration and improvements as well as agronomic assessments and plant breeding efforts in this region

Maize and soybean root front velocity and maximum depth in Iowa, USA

Quantitative measurements of root traits can improve our understanding of how crops respond to soil and weather conditions, but such data are rare. Our objective was to quantify maximum root depth and root front velocity (RFV) for maize (Zea mays) and soybean (Glycine max) crops across a range of growing conditions in the Midwest USA. Two sets of root measurements were taken every 10–15 days: in the crop row (in-row) and between two crop rows (center-row) across six Iowa sites having different management practices such as planting dates and drainage systems, totaling 20 replicated experimental treatments. Temporal root data were best described by linear segmental functions. Maize RFV was 0.62 ± 0.2 cm d−1 until the 5th leaf stage when it increased to 3.12 ± 0.03 cm d−1 until maximum depth occurred at the 18th leaf stage (860 °Cd after planting). Similar to maize, soybean RFV was 1.19 ± 0.4 cm d−1 until the 3rd node when it increased to 3.31 ± 0.5 cm d−1 until maximum root depth occurred at the 13th node (813.6 °C d after planting). The maximum root depth was similar between crops (P \u3e 0.05) and ranged from 120 to 157 cm across 18 experimental treatments, and 89–90 cm in two experimental treatments. Root depth did not exceed the average water table (two weeks prior to start grain filling) and there was a significant relationship between maximum root depth and water table depth (R2 = 0.61; P = 0.001). Current models of root dynamics rely on temperature as the main control on root growth; our results provide strong support for this relationship (R2 \u3e 0.76; P \u3c 0.001), but suggest that water table depth should also be considered, particularly in conditions such as the Midwest USA where excess water routinely limits crop production. These results can assist crop model calibration and improvements as well as agronomic assessments and plant breeding efforts in this region

Predicting crop yields and soil‐plant nitrogen dynamics in the US Corn Belt

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

Using SMAP and SMOS vegetation optical depth to measure crop water in vegetation

NASA's Soil Moisture Active Passive (SMAP) and European Space Agency's (ESA) Soil Moisture Ocean Salinity (SMOS) are two microwave remote sensing satellites. They were originally designed to measure soil moisture, but with an algorithm that already retrieves vegetation optical depth (VOD), they could also be used for vegetation measurements. VOD is the degree to which vegetation attenuates microwave radiation from the soil and may be an important product to quantify vegetation changes. SMAP and SMOS have some advantages to measure vegetation compared to existing practices. They can view the entire crop canopy as opposed to just the top layer, due to their ability to monitor soil moisture which is below the crop canopy. SMAP and SMOS also have on average a daily revisit time in the mid–latitudes. Knowing the location and amount of water in a crop canopy could be beneficial for remote sensing because as the crops grow and water becomes allocated differently, SMAP and SMOS are "seeing" water from many different sources(stems, leaves, ears, soil, etc.). These different sources of water will scatter radiation differently due to their varying sizes and shapes and accounting for water correctly could improve measurements of soil moisture and VOD. A challenge of using SMAP and SMOS is the need to know crop water on the ground for comparison to VOD from the satellites.Data from multiple field experiments were collected and analyzed to show where crop water is in different crop components at varying development stages. New empirical models that relate crop water to crop dry mass were also created with these in situ measurements. We will use this model to hopefully overcome the challenge of comparing satellite VOD to crop water. However, we need to verify that the model is accurate and actually telling us about crop water.To check accuracy of our new empirical model, SMAP and SMOS VOD were compared to crop water estimates from the Agricultural Integrated BIosphere Simulator (Agro-IBIS) at the South Fork SMAP Core Validation Site in Iowa. A crop model was used because it can obtain dry mass for multiple fields in the study area. This dry mass can then be converted to a crop water using our empirical model for comparison to SMAP and SMOS VOD. We find that SMAP and SMOS VOD are directly proportional to crop water. We also found the value of the proportionality constant (or "b-parameter") relating VOD to crop water at the satellite scale is about half as large as previous estimates. After finding that SMAP and SMOS VOD are directly proportional to crop water we wanted to validate SMAP and SMOS VOD with in situ data from the field campaign SMAP Validation Experiment 2016. We found that SMAPv2 VOD had the highest R2 value. The b–parameter was also shown to change over time and that other sources of water in the SMAP and SMOS pixel may need to be taken into account when calculating ab–parameter. Because L-band VOD is directly proportional to crop water at the satellite scale, and because we understand the relationship between crop water and crop dry mass, SMAP and SMOS have the potential to evaluate the large-scale performance of crop models in the Corn Belt on a near daily basis.</p

How does inclusion of weather forecasting impact in-season crop model predictions?

Accurately forecasting crop yield in advance of harvest could greatly benefit decision makers. However, few evaluations have been conducted to determine the effectiveness of including weather forecasts, as opposed to using historical/climatology data, into 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 (14 days, 7 days, 3 days, and 0 days). 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 forecasts of corn [Zea mays L] and soybean [Glycine max] crop yield and phenology in Iowa, USA. The overall goal of this research was to determine how the inclusion of weather forecasting impacts in-season crop model predictions. To achieve this goal we had two objectives 1) to determine the dependence of the accuracy of APSIM yield and phenology predictions on weather forecast length, and 2) the impact of weather forecasts accuracy on APSIM prediction accuracy. APSIM simulations of biomass accumulation and phenology were evaluated against bi-weekly field measurements across 16 field trials (two years, 2015 and 2016; two sites, central and northwest Iowa, USA; two crops, corn and soybean; and two planting dates; early May vs early June). We hypothesized that 1) the accuracy and variability of crop yield predictions will be inversely proportional to the weather forecast length and 2) the inclusion of an explicit weather forecast will reduce crop yield prediction uncertainty and produce a reliable estimate with more lead time relative to using historical variation alone. The accuracy of in-season yield forecasts of corn and soybean varied by treatment, but overall the accuracy was inversely proportional to forecast length (P < 0.05). Our analysis indicated that the most accurate forecast length varied greatly among the 16 treatments, but that the 0 day and 3 day forecasts were, on average, the most accurate. That the 0 day forecast was most accurate meant that a weather forecast from WRF was not better than a weather forecast based on historical weather, however in these cases the difference between the accuracy of the 0 day forecast and the other forecast lengths was not enough to rule out using short-term weather forecasts. Our analysis indicated that there was not sufficient evidence to suggest forecasts of up to 14 days do not on average cause the APSIM predictions to be too inaccurate to use. This means that 14 day length forecasts could be used for management decisions that require lead time, but a combination of all of the forecast lengths should be used to make final decisions.</p