Green Canopy Cover Research Articles

Experimental and producer-reported data quantify the value of foliar fungicide to winter wheat and its dependency on genotype and environment in the U.S. central Great Plains

• We quantified the yield benefits of fungicide to wheat using plot and survey data. • Fungicide yield benefits (7.8 ± 15 %) were greater in wetter growing seasons. • Susceptible genotypes benefited more from fungicides (17 %) than resistant ones (6 %). • Fungicides improved stability and modified environment-adaptability of genotypes. • Fungicide yield benefits were greater (31 %) in the survey data. Foliar fungicides can account for a large portion of the yield gap in winter wheat ( Triticum aestivum L.); however, their impacts on yield have been inconsistent in rainfed environments. We compiled a database of replicated field experiments and producer-reported fungicide and yield data from commercial fields to quantify the effects of fungicide application on winter wheat yield and yield stability. The database of field experiments (i.e., canopy level) included 56 non-inoculated environments spanning 12 growing seasons in eight Kansas locations, and was restricted to field experiments with direct comparisons between a foliar fungicide between Zadoks 40–55 and a side-by-side untreated control, resulting in 393 mean yield comparisons resulting from 3226 yield observations. The producer survey included genotype and fungicide management data from 654 commercial Kansas wheat fields cultivated across three growing seasons. Grain yield and weather conditions in the experimental and producer-reported database were similar, with seasonal precipitation ranging from ˜150 to 1035 mm and average grain yield of ˜3900 kg ha −1 with a ˜7000 kg ha −1 range. Foliar fungicide application resulted in 7.8 % average yield gain in the canopy-level data, ranging from −27 % to +97 %. Yield differences due to fungicide were strongly related to precipitation and to the ratio of precipitation and reference evapotranspiration (WS:WD) during the spring in the experimental data. Grain yield responsiveness to fungicide associated with the responsiveness of green canopy cover, kernel weight, biomass, and harvest index. Analysis of covariance suggested that grain yield usually decreased with increases in disease susceptibility in the absence of foliar fungicides; however, yield-disease relationships were either neutral or positive in the presence of fungicides. Average yield gain for resistant varieties was ˜166 kg ha −1 (5.6 %), which was lower than for intermediate (˜199 kg ha −1 ; 6.9 %) or susceptible genotypes (˜598 kg ha −1 ; 16.9 %). Foliar fungicides increased yield stability across genotypes at the canopy level and at the commercial field level. In the commercial-field level data, 53 % of the fields received foliar fungicide, with higher frequency in growing seasons with greater WS:WD (which were also higher yielding). The use of foliar fungicides was associated with improved yields and interacted with genotype’s resistance level to stripe rust and with growing seasons’ WS:WD. This work quantified and explained the yield benefits of foliar fungicide, and characterized its dependency on genotype-specific disease resistance and environmental conditions both at the canopy- and commercial field-levels.

Influence of integrated agronomic and weed management practices on soybean canopy development and yield

AbstractThe role of weed suppression by the cultivated crop is often overlooked in annual row cropping systems. Agronomic practices such as planting time, row spacing, tillage and herbicide selection may influence the time of crop canopy closure. The objective of this research was to evaluate the influence of the aforementioned agronomic practices and their interaction with the adoption of an effective preemergence (PRE) soil residual herbicide program on soybean canopy closure and yield. A field experiment was conducted in 2019 and 2020 in Arlington, WI, as a 2×2×2×2 factorial in a randomized complete block design, including early (late April) and standard (late May) planting time, narrow (38 cm) and wide (76 cm) row spacing, conventional tillage and no-till, and soil-applied PRE herbicide (yes and no; flumioxazin 150 g ai ha−1 + metribuzin 449 g ai ha−1 + pyroxasulfone 190 g ai ha−1). All plots were maintained weed-free throughout the growing season. In both years, early planted soybeans reached 90% green canopy cover (T90) before (7 to 9 d difference) and yielded more (188 to 902 kg ha−1 difference) than the standard planted soybeans. Narrow-row soybeans reached T90 earlier than wide-row soybeans (4 to 7 d difference), but yield was similar between row spacing treatments. Conventional tillage resulted in a higher yield compared to a no-till system (377 kg ha−1 difference). The PRE herbicide slightly delayed T90 (4 d or less) but had no impact on yield. All practices investigated herein influenced the time of soybean canopy closure but only planting time and tillage impacted yield. Planting soybeans earlier and reducing their row spacing expedites the time to canopy closure. The potential delay in canopy development and yield loss if soybeans are allowed to compete with weeds early in the season would likely outweigh the slight delay in canopy development by an effective PRE herbicide.

شبیه‌سازی رشد و نمو گوجه‌فرنگی (Lycopersicon esculentum Mill.) در شرایط تنش خشکی: 1- محتوای رطوبت خاک، تبخیر وتعرق و پوشش کانوپی

Introduction: The modeling approach for the simulation of the growth and development of tomatoes in Iran has been overlooked. Calibrated crop simulation models, therefore, are increasingly being used as an alternative means for the rapid assessment of water-limited crop yield over a wide range of environmental and management conditions. AquaCrop is a multi-crop model that simulates the water-limited yield of herbaceous crop types under different biophysical and management conditions. It requires a relatively small number of explicit and mostly intuitive parameters to be defined compared to other crop models and has been validated and applied successfully for multiple crop types across a wide range of environmental and agronomic settings. This study was conducted as a two-year field experiment with the aim of the simulation of soil water content, evapotranspiration, and green canopy cover of tomato using AquaCrop model under different irrigation regimes at two growth stages in Mashhad climate conditions. Materials and Methods: A field experiment was conducted over two consecutive seasons (2016-2017) in the experimental field of Ferdowsi University of Mashhad, located in Khorasan Razavi province, North East of Iran. The experiment was laid out in a split-plot design with different irrigation regimes at the vegetative and at the reproductive stage as the main and subplot factors, replicated thrice. In total, 27 plots of 4.5×3 m (13.5 m2) were used at a planting density of 2.7 plants per m2. Seedlings were planted in a zigzag pattern into twin rows, with a distance of 1.5 m between them, so there were four twin rows of three meters in each plot. The distance between tomato plants within each twin-row was 0.5 meters. A buffer zone spacing of 3 and 1.5 m was provided between the main plots and subplots, respectively. The following experimental factors were studied: three irrigation regimes (100= 100% of water requirement, 75= 75% of water requirement, 50= 50% of water requirement) and two crop growth stages (V= vegetative stage and R= Reproductive stage). The drip irrigation method was used for irrigation. The tomato water requirement was calculated using CROPWAT 8.0 software. The irrigation water was supplied based on total gross irrigation and obtained irrigation schedule of CROPWAT. Model accuracy was evaluated using statistical measures, e.g., R2, normalized root means square error (NRMSE), model efficiency (E.F.), and d-Willmott. The 2016 and 2017 measured soil and canopy data sets were used for calibration and validation of the AquaCrop model, respectively. Results and Discussion: For a water-driven model, such as AquaCrop, it is important to evaluate its effectiveness in simulating soil water content. During calibration (2016), the model simulated the soil water content with good accuracy. The simulated soil water content values were close to the observed values during calibration (2016) for all treatments with R2 ranging from 0.90 to 0.97, NRMSE in range of 8.47 to 17.96%, d varying from 0.76 to 0.99, and M.E. ranging from 0.87 to 0.96. Validation results indicated the good performance of the model in simulating soil water content for most of the treatments (0.79 0.87), good E.F. (>0.61), low estimation errors (RMSE, ranging from only 4.5 to 9.2) and high d values (>0.92). Conclusion: The AquaCrop model (version 6.1) was calibrated and validated for modeling soil water content, evapotranspiration, and green canopy cover for tomatoes under drought stress conditions. In general, soil water content, evapotranspiration, and green canopy cover of tomato were simulated by AquaCrop model with acceptable accuracy in both calibration and validation stages. However, the model performance was more accurate in no and/or moderate stress conditions than in severe water stress environments. In conclusion, the AquaCrop model could be calibrated to simulate the growth and soil water content of tomatoes under temperate conditions reasonably well and become a very useful tool to support the decision on when and how much irrigate. For R2, values > 0.90 were considered very well, while values between 0.70 and 0.90 were considered good. Values between 0.50 and 0.70 were considered moderately well, while values less than 0.50 were considered poor. Root mean square error ranges from 0 to positive infinity and expresses in the units of the studied variable. An RMSE approaching 0 indicates good model performance.

Using Canopy Measurements to Predict Soybean Seed Yield

Predicting soybean [Glycine max (L.) Merr.] seed yield is of interest for crop producers to make important agronomic and economic decisions. Evaluating the soybean canopy across a range of common agronomic practices, using canopy measurements, provides a large inference for soybean producers. The individual and synergistic relationships between fractional green canopy cover (FGCC), photosynthetically active radiation (PAR) interception, and a normalized difference vegetative index (NDVI) measurements taken throughout the growing season to predict soybean seed yield in North Dakota, USA, were investigated in 12 environments. Canopy measurements were evaluated across early and late planting dates, 407,000 and 457,000 seeds ha−1 seeding rates, 0.5 and 0.8 relative maturities, and 30.5 and 61 cm row spacings. The single best yield predictor was an NDVI measurement at R5 (beginning of seed development) with a coefficient of determination of 0.65 followed by an FGCC measurement at R5 (R2 = 0.52). Stepwise and Lasso multiple regression methods were used to select the best prediction models using the canopy measurements explaining 69% and 67% of the variation in yield, respectively. Including plant density, which can be easily measured by a producer, with an individual canopy measurement did not improve the explanation in yield. Using FGCC to estimate yield across the growing season explained a range of 49% to 56% of yield variation, and a single FGCC measurement at R5 (R2 = 0.52) being the most efficient and practical method for a soybean producer to estimate yield.

Responses of canopy characteristics and water use efficiency to ammoniated straw incorporation for summer maize (Zea mays L.) in the Loess Plateau, China

Straw application has a wide range of environmental benefits that make it an effective practice for sustainable agriculture. However, little is well known about the impacts of ammoniated straw addition on soil water and crop canopy characteristics in maize cultivation systems. A 3-year field experiment of summer maize (Zea mays L.) in Northwest China was conducted to evaluate the effects of different straw applications on soil water storage, maize canopy growth, grain yield and water use efficiency (WUE). The three treatments were: (i) no straw (traditional tillage, CK), (ii) straw mulch (traditional straw returning, T1), and (iii) ammoniated straw incorporated into the soil (optimized straw returning, T2). The T2 treatment increased average WUE by 14.8%, 7.8%, and 16.1% in 2016, 2017, and 2018, respectively, relative to the CK treatment. The 3-year average grain yield of the T2 treatment was 13.5% and 8.3% greater than grain yield of the CK and T1 treatments, respectively. Harvest index and 100-kernel weight of summer maize in the T2 treatment were higher than observed for the CK and T1 treatments. Across the three growing years, the T2 treatment was superior over the T1 and CK treatments in improving average LAI, spatial density of leaf area, green canopy cover and aboveground biomass. Hence, our study recommends that using ammoniated straw incorporation could be a promising application for synergistically improving maize growth, WUE and grain yield within this semi-arid region.

Assessment of Maize Growth and Development with High- and Medium-Resolution Remote Sensing Products

The availability of a great amount of remote sensing data for precision agriculture purposes has set the question of which resolution and indices, derived from satellites or unmanned aerial vehicles (UAVs), offer the most accurate results to characterize vegetation. This study focused on assessing, comparing, and discussing the performances and limitations of satellite and UAV-based imagery in terms of canopy development, i.e., the leaf area index (LAI), and yield, i.e., the dry aboveground biomass (DAGB), for maize. Three commercial maize fields were studied over four seasons to obtain the LAI and DAGB. The normalized difference vegetation index (NDVI) and visible atmospherically resistant index (VARI) from satellite platforms (Landsat 5TM, 7 ETM+, 8OLI, and Sentinel 2A MSI) and the VARI and green canopy cover (GCC) from UAV imagery were compared. The remote sensing predictors in addition to the growing degree days (GDD) were assessed to estimate the LAI and DAGB using multilinear regression models (MRMs). For LAI estimation, better adjustments were obtained when predictors from the UAV platform were considered. The DAGB estimation revealed similar adjustments for both platforms, although the Landsat imagery offered slightly better adjustments. The results obtained in this study demonstrate the advantage of remote sensing platforms as a useful tool to estimate essential agronomic features.

Development of Sprayable Sodium Alginate-Seaweed Agricultural Mulches with Nutritional Benefits for Substrates and Plants

Sprayable agricultural mulch suspensions were prepared from the combination of sodium alginate (Alg) and three different concentrations of the seaweed Undaria pinnatifida (A) microparticles: 0.5, 1, and 2 wt%. Growth parameters were measured in tomato plants cultivated under each type of mulches, including polyethylene (PE) as a control treatment. The aerial fresh and dry weights (FW and DW, respectively), chlorophyll (Chl) content, green canopy cover, and the anthocyanin content were determined in tomato plants at the end of the bioassays. The soil temperature and moisture were measured during the experiment. Besides, the impact of the different mulch treatments on soil microorganisms was assayed by measuring the relative abundance of bacteria, fungi, and actinomycetes. Results indicated that the percentage of A in the mulch formulation was crucial to confer it with biostimulant properties. Alg with 1 wt% of A provided a significant increment in FW and DW and the Chl level of tomato plants, demonstrating that the developed formulations represent a useful material for horticulture production.

Individual and Combined Effects of Planting Date, Seeding Rate, Relative Maturity, and Row Spacing on Soybean Yield

Planting date (PD), seeding rate (SR), relative maturity (RM) of cultivars, and row spacing (RS) are primary management factors affecting soybean (Glycine max (L.) Merr.) yield. The individual and synergistic effects of PD, SR, RM, and RS on seed yield and agronomic characteristics in North Dakota were herein investigated. Early and late PD, early and late RM cultivars, two SR (408,000 and 457,000 seed ha−1), and two RS (30.5 and 61 cm) were evaluated in four total environments in 2019 and 2020. Maximizing green canopy cover prior to the beginning of flowering improved seed yield. Individual factors of early PD and narrow RS resulted in yield increase of 311 and 266 kg ha−1, respectively. The combined factors of early PD, late RM, high SR, and narrow RS improved yield by 26% and provided a $350 ha−1 partial profit over conventional practices. Canopy cover and yield had relatively weak relationships with r2 of 0.36, 0.23, 0.14, and 0.21 at the two trifoliolate, four trifoliolate, beginning of flowering, and beginning of pod formation soybean growth stages, respectively. Producers in the most northern soybean region of the USA should combine early planting, optimum RM cultivars, 457,000 seed ha−1 SR, and 31 cm RS to improve yield and profit compared to current management practices.

Lentil yield and crop growth rate are coupled under stress but uncoupled under favourable conditions

Growth and yield can be decoupled in lentil whereby excessive vegetative growth leads to self-shading, reduced pod and seed set, low harvest index and higher risk of disease and lodging. We evaluated the degree of coupling between growth and yield in 20 lentil lines grown in eight environments varying in water and photothermal conditions returning a 10-fold yield range, from 21 to 221 g m−2. Calibration curves between shoot biomass and canopy cover measured with NDVI and green canopy cover measured with Canopeo app were improved with canopy height as a multiplication factor returning a 3-D trait. Calibration curves were used to phenotype shoot biomass and calculate crop growth rate.For the pooled data, yield correlated non-linearly with crop growth rate, with an x-intercept of 0.09 g m−2 [oCd]-1, suggesting a minimum plant size for reproduction, and an inflection point at 0.43 g m−2 [oCd]-1. Yield correlated with biomass and crop growth rate in the more stressful conditions (yield ≤ 107 g m−2) and was decoupled in higher yielding conditions (yield ≥ 170 g m−2). Yield associated with harvest index at all yield levels, but more strongly in high-yielding conditions. Biomass and harvest index correlated in environments with yield ≤ 107 g m−2, and decoupled under more favourable conditions (yield ≥ 170 g m−2). Yield associated with phenology under stress but not in favourable conditions. For the combination of lines and environments in this study, broad sense heritability was 0.96 for flowering time, 0.93 for harvest index, 0.89 for yield, 0.87 for crop growth rate and 0.39 for biomass. Selection for harvest index would improve yield across environments whereas selection for growth rate could further improve yield under stress. Agronomic practices to improve the coupling of yield and growth under favourable conditions need to be explored; for example, using precision seeding to reduce rectangularity of crop arrangement and favour penetration of radiation into the canopy and pod set.

Using NDVI for the assessment of canopy cover in agricultural crops within modelling research

The fraction of green canopy cover (CC) is an important feature commonly used to characterize crop growth and for calibration of crop and hydrological models. It is well accepted that there is a relation between CC and NDVI through linear or quadratic models, however a straight-forward empirical approach, to derive CC from NDVI observations, is still lacking. In this study, we conducted a meta-analysis of the NDVI-CC relationships with data collected from 19 different studies (N = 1397). Generic models are proposed here for 13 different agricultural crops, and the associated degree of uncertainty, together with the magnitude of error were quantified for each model (RMSE around 6–18% of CC). We observed that correlations are adequate for the majority of crops as R2 values were above 75% for most cases, and coefficient estimates were significant for most of the linear and quadratic models. Extrapolation to conditions different than those found in the studies may require local validation, as obtained regressions are affected by non-sampling errors or sources of systematic error that need further investigation. In a case study with wheat, we tested the use of NDVI as a proxy to estimate CC and to calibrate the AquaCrop model. Simulation outcomes were validated with field data collected from three growing seasons and confirmed that the NDVI-CC relationship was useful for modelling research. We highlight that the overall applicability of these relationships to modelling is promising as the RMSE are in line with acceptable levels published in several sensitivity analyses.

Detecting Biophysical Characteristics and Nitrogen Status of Finger Millet at Hyperspectral and Multispectral Resolutions

Finger millet (Eleusine coracana Gaertn L.) is an important grain crop for small farmers in many countries. Reliable estimates of crop parameters, such as crop growth and nitrogen (N) content, through remote sensing techniques can improve in-season management of finger millet. This study investigated the relationships of hyperspectral reflectance with canopy height, green canopy cover, leaf area index (LAI), and N concentrations of finger millet using an optimal waveband selection procedure with partial least square regression (PLSR). Predictive performance of 13 vegetation indices (VIs) computed from the original hyperspectral data as well as synthesized Landsat-8 and Sentinel-2 data were evaluated and compared for estimating various crop parameters with simple linear regression (SLR) and multilinear regression (MLR) models. The optimal wavebands determined by PLSR were mostly concentrated within 1,000–1,100 nm for both LAI and dry biomass but were scattered for other canopy parameters. The SLR statistics resulted in the simple ratio pigment index (SRPI) and red/green index (RGI) performing best when predicting LAI (R2v = 0.53–0.59) and canopy cover (R2v = 0.72–0.76). The blue/green index (BGI1) was strongly related to canopy height (R2v = 0.65–0.78), dry biomass (R2v = 0.42–0.49), and N concentration (R2v = 0.70–0.83) of finger millet, regardless of spectral resolutions. The MLR approach, using four maximum VIs as input variables, improved the prediction accuracy of N concentration by 14% compared to both SLR and waveband selection methods. VIs computed from synthesized Landsat-8 and Sentinel-2 satellite data resulted in similar or greater prediction accuracy than hyperspectral data for various canopy parameters of finger millet, indicating publicly accessible multispectral data could serve as alternative to hyperspectral data for improved crop management decisions via precision agriculture.

Evaluating winter malting barley grain yield with fractional green canopy cover

AbstractBecause of growth in the craft brewing industry, farmers in the eastern United States are planting winter malting barley (Hordeum vulgare L.) to meet demands for locally sourced grain. However, given that barley is a relatively new crop in this region, basic agronomic information relating to stand assessment is needed. This is particularly relevant in this region, as climatic variability from extreme temperature fluctuations during the winter and spring can reduce a barley stand, creating the need for farmers to estimate grain yield potential. The objective of the research was to evaluate the relationship between spring stem counts, fractional green canopy cover (FGCC), and normalized difference vegetation index (NDVI) and barley grain yield. Trials were established at five site‐years in Ohio, where seeding rate treatments of 0.75, 1.0, 1.5, 2.0 and 2.5 million seeds acre−1 were used to simulate a range of poor to excellent plant stands. All barley stand assessment methods were conducted in the spring at the Feekes 5 growth stage. Stem counts were correlated with FGCC and NDVI measurements (r = .76 and .74, respectively). Stem counts (R2 = .67) and FGCC (R2 = .65) measurements accounted for the greatest variability in barley grain yield. Specifically, FGCC ≤5% corresponded to yield between 27 and 39 bu acre−1, whereas 5 to 10% corresponded to yield between 60 and 89 bu acre−1. Fractional green canopy cover should be considered as a stand and yield assessment tool, as it reduces labor compared with stem counting techniques.

Leaf area index estimation using top-of-canopy airborne RGB images

Leaf Area Index (LAI) is one of the most important biophysical properties of a crop, used in detecting long-term water stress, estimating biomass, and identifying crop growth stage. Remote sensing based LAI estimation techniques perform well for early growth stages but tend to produce high error during the crop reproductive stage due to canopy closure. Moreover, estimation of the true LAI from individual leaf measurements remains a challenge. Consequently, two alternate methods have been developed and compared for estimating the LAI of a maize crop using top-of-canopy RGB images collected throughout the growing season using a hexacopter. Both methods used the RGB images to estimate the canopy height and the green-canopy cover together with a ‘vertical leaf area distribution factor’ (VLADF) from allometric relations (using crop height from RBG images and days after sowing). The first method used an empirical approach to estimate the LAI from training a linear function of the above inputs to Licor canopy analyser values of LAI. The method was trialled for a research farm located in a semi-arid area of southern peninsula India and found to have validation results with an R2 of 0.84 and RMSE of 0.36 for the unused portion of the Rabi (post-monsoon) season data of 2018–19, and R2 of 0.77 and RMSE of 0.45 for the Rabi 2019–20 season data when compared with Licor LAI values. While seemingly acceptable, the Licor canopy analyser gives a foliage area index and so the accuracy of this model was very low (R2 of 0.56 and RMSE of 1.34) when evaluated with true LAI from manual measurements of the leaf area. Consequently, a refinement was introduced using only VLADF, green-canopy cover estimates from the RBG images, and a field measured top leaf angle. The output derived from this conceptual model had an R2 of ~0.6 and RMSE of 0.73 when compared with the true LAI values. Importantly, the LAI from this conceptual model was found to be unaffected by canopy closure during the reproductive stage of the crop.

Yield Increases in Burrowing Nematode-infested Anthurium with Fluopyram and Trifloxystrobin Applications

Burrowing nematode (Radopholus similis) causes severe stunting and yield reduction in anthurium (Anthurium andraeanum) cut flower production. Two field trials were conducted at commercial grower farms to test the efficacy of fluopyram or fluopyram + trifloxystrobin for managing burrowing nematodes. Nematode population densities in roots and cinder media were evaluated during the trial in addition to cut flower yield and canopy cover. In the first trial, the nematode population in roots was reduced by 57% after two applications of fluopyram 3 months apart. As plant health improved, the increasing anthurium root weight supported higher nematode populations. After 14 months, fluopyram-treated plots had 43% more green canopy cover and a 53% increase in flower production compared with the untreated control plots. At a second location, population densities of burrowing nematode were reduced in roots after one application of fluopyram + trifloxystrobin and remained low with quarterly applications. Nematode populations were initially reduced in fluopyram-treated plots followed by a resurgence as demonstrated in the other trial. Ten months after the initial treatment, flower yield was greater in fluopyram + trifloxystrobin-treated plots with more large and extra-large flowers produced. Canopy cover was 45% and 22% greater with fluopyram + trifloxystrobin and fluopyram applications, respectively. Fluopyram shows potential for management of burrowing nematodes in anthurium by improving plant vigor and cut flower production.

Modeling and Evaluation of AquaCrop for Maize (zea mays L.) under Full and Deficit Irrigation in Semi-Arid Tropics

FAO AquaCrop is a simulation model that predicts the effects of soil, climate, water and crop growth on water productivity, yield and its attributes of various crops. In the present study, performance evaluation of AquaCrop model for maize was assessed for rabi maize during 2015 at Water Technology Centre, College of Agriculture, Rajendranagar, Hyderabad. The experiment was laid in a randomized block design with eight treatments in three replications. The treatments comprised of surface and drip irrigation schedules based on Epan viz., surface irrigation at 0.6 IW/CPE ratio (T1), 0.8 IW/CPE ratio (T2), 1.0 IW/CPE ratio (T3), 1.2 IW/CPE ratio (T4), drip irrigation at 0.6 Epan (T5), 0.8 Epan (T6), 1.0 Epan (T7) and 1.2 Epan (T8). The model was evaluated using crop data resulted from the experiment under varying water application methods and levels. Simulation performance was assessed with statistical parameters viz., statistical co-efficient of determination (R2), prediction error (Pe), model efficiency (E), root mean square error (RMSE) and mean absolute error (MAE). The model results are in quite agreement with practical values for grain yield, biomass and water productivity with model efficiency of 0.99, 0.92 and 0.71, coefficient of determination (R2) of 0.90, 0.91 and 0.93 with an RMSE of 0.24, 0.10 and 0.05, respectively. The model prediction errors in simulation of grain yield, biomass and water productivity under all treatments ranged from 1.4% to 11.9%, 1.4% to 16.1% and 4.85% to 25.9%, respectively. The highest and lowest prediction accuracy for grain yield, biomass and water productivity were in drip irrigation at 1.2 Epan and surface irrigation at 0.6 IW/CPE ratios. It is inferred that FAO AquaCrop model is suitable for predicting grain yield, biomass, water productivity and green canopy cover with acceptable range of under and over predictions for maize in semi-arid tropical climate.

Combining Crop Growth Modeling and Statistical Genetic Modeling to Evaluate Phenotyping Strategies.

Genomic prediction of complex traits, say yield, benefits from including information on correlated component traits. Statistical criteria to decide which yield components to consider in the prediction model include the heritability of the component traits and their genetic correlation with yield. Not all component traits are easy to measure. Therefore, it may be attractive to include proxies to yield components, where these proxies are measured in (high-throughput) phenotyping platforms during the growing season. Using the Agricultural Production Systems Simulator (APSIM)-wheat cropping systems model, we simulated phenotypes for a wheat diversity panel segregating for a set of physiological parameters regulating phenology, biomass partitioning, and the ability to capture environmental resources. The distribution of the additive quantitative trait locus effects regulating the APSIM physiological parameters approximated the same distribution of quantitative trait locus effects on real phenotypic data for yield and heading date. We use the crop growth model APSIM-wheat to simulate phenotypes in three Australian environments with contrasting water deficit patterns. The APSIM output contained the dynamics of biomass and canopy cover, plus yield at the end of the growing season. Each water deficit pattern triggered different adaptive mechanisms and the impact of component traits differed between drought scenarios. We evaluated multiple phenotyping schedules by adding plot and measurement error to the dynamics of biomass and canopy cover. We used these trait dynamics to fit parametric models and P-splines to extract parameters with a larger heritability than the phenotypes at individual time points. We used those parameters in multi-trait prediction models for final yield. The combined use of crop growth models and multi-trait genomic prediction models provides a procedure to assess the efficiency of phenotyping strategies and compare methods to model trait dynamics. It also allows us to quantify the impact of yield components on yield prediction accuracy even in different environment types. In scenarios with mild or no water stress, yield prediction accuracy benefitted from including biomass and green canopy cover parameters. The advantage of the multi-trait model was smaller for the early-drought scenario, due to the reduced correlation between the secondary and the target trait. Therefore, multi-trait genomic prediction models for yield require scenario-specific correlated traits.

Determining yield of forage crops using the Canopeo mobile phone app

One of the biggest challenges in pastoral systems is accurately determining forage availability. Destructive sampling is the most accurate method to use but it is very time-consuming, so non-destructive methods are usually preferred. This study compared data on the percentage of green canopy cover (%GCC) obtained using a recently developed image-analysis mobile phone app (Canopeo®) with crop biomass, light interception and NDVI measurements. Data were obtained for a range of crop species from two dryland experiments of lucerne (E1) and winter forage crops (E2). Linear and quadratic regression models were constructed to evaluate the data obtained using Canopeo® to estimate biomass accumulation, light interception and NDVI. For lucerne, positive linear relationships were found between %GCC and biomass (0.77 and 0.79 for Spring + Summer and Autumn + Winter, respectively; P<0.05), and between %GCC and light interception (R2=0.83; P<0.05). For winter forage crops, there was a linear relationship between biomass and %GCC (R2=0.81; P<0.05), and a linear relationship between %GCC and light interception (R2=83; P<0.05). NDVI data obtained using the GreenSeeker® crop sensing system was linearly related to %GCC data from Canopeo® in both experiments (R2=0.84 in E1 and R2=0.93 in E2; P<0.05). The Canopeo® app proved to be a fast and reliable method to estimate biomass accumulation and light interception of forage crops in this study and has potential wider applications. However, it does not distinguish between crops and weeds.

AquaCrop Calibration and Validation for Faba Bean (Vicia faba L.) under Different Agronomic Managements

Faba bean (Vicia faba L.) is an important pulse crop known for its nitrogen-fixing characteristics and as a disease-break crop in crop rotations. Sowing time, scheduling of supplemental irrigation, and sowing rate are some of the agronomic managements which affect faba bean growth and yield. The effect of these on faba bean yield can be evaluated using calibrated models. The Food and Agriculture Organization (FAO) AquaCrop model was calibrated and tested using two-year experimental data of different watering regimes, sowing dates, and sowing rates in a semiarid environment of South-Eastern Australia. AquaCrop adequately simulated the green canopy cover (CC), biomass development, grain yield, and soil water dynamics under different agronomic management conditions. AquaCrop simulated faba bean yield with 3% deviation, root mean square error (RMSE) of 0.49 t ha−1, normalised root mean square error (NRMSE) of 12.4%, index of agreement (d) of 0.95, and R2 of 0.86. The CC was simulated with RMSE of 14.1%, R2 of 0.85, and d of 0.90. The above-ground dry matter was predicted with RMSE of 2.6 t ha−1, R2 of 0.95, and d of 0.93. Except for end-of-season values, the total soil water was also adequately simulated at RMSE of 21 mm, R2 of 0.89, and d of 0.87. The response of faba bean to supplemental irrigation, sowing time, and sowing rate was adequately simulated by the calibrated model. AquaCrop is a valuable decision support tool for predicting faba bean growth, yield, and soil water dynamics under different agronomic managements.

FAO AquaCrop Model Performance: in Green Canopy Cover, Soil Moisture and Production of Maize at Middle and Lower Reaches Plain of Yangtze River of China

Crop water productivity models are valuable tools for investigate the crop responses to the water-management strategies and ameliorate the efficiency of water use in agriculture. The objective of this study was to evaluate the performance of AquaCrop model for maize crop (Zea mays L.) under full, excessive and deficit applications of drip irrigation in semi-humid region of China. The evaluation results emphasized the model accuracy in simulations CC, SWC, B*, Y and WUE with satisfactory performance in full irrigation, and moderate water stressed treatment T2 of 75% of full irrigation. This accuracy declines in circumstances of excessive irrigation T1 of 125% of full irrigation, and high water stressed T3 of 50% of full irrigation. The RMSEs and NRMSEs in simulated CC, and SWC for full irrigation, T1, T2 and T3 treatments were 7.3–8.6, 6.3–7.2, 5.4–6.3 and 4.9–5.6% CC, respectively, and 9.4–4.8, 16.1–8.1, 16.4–9.3 and 20.9–13.4% SWC, respectively. Whereas, the D-index and R2 of CC varied between 0.72 to 0.89 and 0.99 to 1.0, while in the SWC varied between 0.54 to 0.83 and 0.48 to 0.96 for the four irrigation treatments. The differences (S.D) in final biomass and grain yield were within the range of 0.23 to 4.45% and 0.30 to 1.46% between the measurement and simulation. Simulated WUEs of biomass and yield under different irrigation treatments ranged between 6.07 to 6.52 kg/m3 and 3.07 to 3.16 kg/m3, AquaCrop’s performance trends to underestimate the WUEs of biomass and yield, and emphasizes that the yield WUE increased linearly with water stress condition increased.

Growth and yield response of faba bean to soil moisture regimes and sowing dates: Field experiment and modelling study

In semiarid environments, faba bean (Vicia faba L.) production shows high year to year variability. In order to implement measures that minimize this variability, it is important to understand how abiotic and management factors affect its growth and yield. The aim of this study is to determine supplemental irrigation strategy and sowing date that maximize yield and water productivity of faba bean. A field experiment and simulation modelling were conducted on a mid-flowering faba bean cultivar under different sowing dates and watering regimes in south-eastern Australia. Drip irrigation, with two laterals plot, was used. At sowing, 20 kg P ha−1 of single-phosphate was applied. Four watering regimes were applied: not irrigated (NI), fully irrigated (FI = 245.1 mm), vegetative-stage irrigated (VI = 60.1 mm), reproductive-stage irrigated (RI = 106.4 mm). Soil water tension was measured monitored using gypsum blocks at 15, 45 and 75 cm depths. The Agricultural Production Systems sIMulator (APSIM) was used to simulate grain yield. Sowing dates were 22 April (SD1) and 20 May (SD2). There was no significant difference between the yields of SD1-fully irrigated (5.24 t ha−1), SD1-reproductive stage irrigated (5.85 t ha−1), SD2-fully irrigated (5.52 t ha−1), and SD2-reproductive stage irrigated (5.24 t ha−1) treatments. The highest grain water use efficiencies, 1.65 kg m-3 and 1.63 kg m-3, were obtained from the reproductive stage irrigated SD1 and SD2, respectively. Harvest index and seed weight were significantly affected by sowing dates while the number of pods per plant and grain water efficiencies were significantly affected by irrigation. There was positive linear relationship between grain yield and post-flowering, rather than pre-/during flowering, green canopy cover. APSIM simulated faba bean yield with RMSE of 468 t ha−1. Simulation modelling showed that supplemental irrigation applied from mid-September to the end of October increased grain yield by 23% relative to the rainfed treatment while supplemental irrigation from mid-August to the end of September resulted in 13% increase. Sowing in the first week of May or in the first week of June, instead of in the first week of April, decreased grain yield by 26% and 52%, respectively.