Simple Crop Growth Model Research Articles

Establishing a water-use boundary function for potato through crop modeling

The yield to water-use boundary function is a valuable method for estimating attainable yield and yield gap (the difference between attainable yield and actual yield) in water-limited regions. However, the water-use boundary function for potato has not been clearly defined yet. In current research, we established a water-use boundary function for fresh potato tubers in a climatic transition zone (Longzhong Plateau, China) using a simple crop growth model. We validated this function using extensive long-term experimental data collected from field experiments and the literature. The results showed that the crop model accurately simulated the weight of fresh potato tubers with a relative root mean square error (RRMSE) of less than 30 %, a coefficient of determination (d) greater than 0.7, and a root mean square error (RMSE) of 5426 kg ha−1. The established water-use boundary function for fresh potato tubers is defined as: yield (kg ha−1) = 167 × (water use [mm] - 121), with a plateau yield of 48,082 kg ha−1 when water use exceeds 410 mm. Furthermore, the water-use boundary function was found to be highly applicable to water use-yield data collected from the literature and precipitation-yield data for the potato growing season from long-term observations. Utilizing precipitation data for the potato growing season from 1988 to 2018, the water-use boundary function was employed to calculate the average attainable yield for fresh potato tubers, ranging from 10,000 to 48,082 kg ha−1 in the Longzhong Plateau. The water-use boundary function for potato could aid farmers in water-limited regions in identifying the most profitable crop management systems.

Calibration and Evaluation of the SIMPLE Crop Growth Model Applied to the Common Bean under Irrigation

Bean production is at risk due to climate change, declining water resources, and inadequate crop management. To address these challenges, dynamic models that predict crop growth and development can be used as fundamental tools to generate basic and applied knowledge such as production management and decision support. This study aimed to calibrate and evaluate the SIMPLE model under irrigation conditions for a semi-arid region in north-central Mexico and to simulate thermal time, biomass (Bio), and grain yield (GY) of common beans cv. ‘Pinto Saltillo’ using experimental data from four crop evapotranspiration treatments (ETct) (I50, I75, I100, and I125) applied during the 2020 and 2021 growing seasons. Both experiments were conducted in a randomized complete block design with three replicates. Model calibration was carried out by posing and solving an optimization problem with the differential-evolution algorithm with 2020 experimental data, while the evaluation was performed with 2021 experimental data. For Bio, calibration values had a root-mean-square error and Nash and Sutcliffe’s efficiency of <0.58 t ha−1 and >0.93, respectively, while the corresponding evaluation values were <1.80 t ha−1 and >0.89, respectively. The I50 and I100 ETct had better fit for calibration, while I50 and I75 had better fit in the evaluation. On average, the model fitted for the predicted GY values had estimation errors of 37% and 22% for the calibration and evaluation procedures, respectively. Therefore, an empirical model was proposed to estimate the harvest index (HI), which produced, on average, a relative error of 6.9% for the bean-GY estimation. The SIMPLE model was able to predict bean biomass under irrigated conditions for these semi-arid regions of Mexico. Also, the use of both crop Bio and transpiration simulated by the SIMPLE model to calculate the HI significantly improved GY prediction under ETct. However, the harvest index needs to be validated under other irrigation levels and field experiments in different locations to strengthen the proposed model and design different GY scenarios under water restrictions for irrigation due to climate change.

Estimating crop biomass using leaf area index derived from Landsat 8 and Sentinel-2 data

The availability of Landsat 8 and Sentinel-2 has led to a steady increase in both temporal and spatial resolution of satellite data, offering new opportunities for large-scale crop condition monitoring and crop yield mapping. This study investigated the potential of using Landsat 8 and Sentinel-2 data from the harmonized Landsat 8 and Sentinel-2 (HLS) products for crop biomass estimation for six crops in Manitoba, Canada. Crop biomass was estimated using remotely sensed leaf area index (LAI) to reparametrize a simple crop growth model. The results showed that the LAI of six different crops can be estimated using a generic relationship between LAI and red-edge based vegetation indices (VIs, e.g., modified simple ratio red-edge (MSRRE) and red-edge normalized difference VI (NDVIRE)) for the Multispectral Instrument (MSI) of Sentinel-2. For the Operational Land Imager of Landsat 8 without the red-edge band, LAI can be best estimated using a VI derived from Near-infrared (NIR) and short-wave infrared (SWIR) bands (Normalized Difference Water Index, NDWI1). Above-ground dry biomass of these six crops was more accurately estimated from the assimilation of LAI derived from both satellites (R2 (the coefficient of determination) = 0.81, RMSE (the root-mean-square-error) = 135.4 g/m2, nRMSE (the normalized RMSE) = 37.9%, RPD (the ratio of percent deviation) = 2.26) than that of LAI derived from MSI-data (R2 = 0.80, RMSE = 136.7 g/m2, nRMSE = 38.3%, RPD = 2.23) or that from LAI derived from OLI-data (R2 = 0.68, RMSE = 191.0 g/m2, nRMSE = 53.5%, RPD = 1.16). Further analysis showed that these three assimilation cases (MSI and OLI; MSI alone; OLI alone) with a different number of LAI observations resulted in differences in parameter optimization, particularly the parameters relevant to crop phenology and biomass partitioning. Both crop growth stage (e.g., the emergence date for crop growth) and leaf dry biomass estimated from the assimilation of LAI derived from MSI and OLI, or MSI alone, produced the most accurate estimates. These results are likely attributed to the improved temporal coverage associated with Sentinel-2 and the availability of a red-edge band on this sensor.

Linking process-based potato models with light reflectance data: Does model complexity enhance yield prediction accuracy?

Data acquisition for parameterization is one of the most important limitations for the use of potato crop growth models. Non-destructive techniques such as remote sensing for gathering required data could circumvent this limitation. Our goal was to analyze the effects of incorporating ground-based spectral canopy reflectance data into two light interception models with different complexity. A dynamic- hourly scale- canopy photosynthesis model (DCPM), based on a non-rectangular hyperbola applied to sunlit and shaded leaf layers and considering carbon losses by respiration, was implemented (complex model). Parameters included the light extinction coefficient, the proportion of light transmitted by leaves, the fraction of incident diffuse photosynthetically active radiation and leaf area index. On the other hand, a simple crop growth model (CGM) based on daily scale of light interception, light use efficiency (LUE) and harvest index was parameterized using either canopy cover (CGMCC) or the weighted difference vegetation index (CGMWDVI). A spectroradiometer, a chlorophyll meter and a multispectral camera were used to derive the required parameters. CGMWDVI improved yield prediction compared to CGMCC. Both CGMWDVI and DCPM showed high degree of accuracy in the yield prediction. Since large LUE variations were detected depending on the diffuse component of radiation, the improvement of simple CGM using remotely sensed data is contingent on an appropriate LUE estimation. Our study suggests that the incorporation of remotely sensed data in models with different temporal resolution and level of complexity improves yield prediction in potato.

An approach to bridging yield gaps, combining response to water and other resource inputs for wheat in northern India, using research trials and farmers’ fields data

This paper deals with growth and yield of wheat with respect to varying agronomic and resource management practices. Variability of inputs like fertilizer and irrigation as well as differential response of cultivars to these inputs and other agri-management practices coupled with various biotic and abiotic stresses need to be understood in view of current yield stagnation of wheat in the north India, the wheat belt on the country. The reduction in yield arising due to delay in sowing, limited inputs of water and nitrogen, interaction among various biotic and abiotic stresses are discussed on the basis of historic datasets or by using simulation models. Agri-production functions to assess the grain yield of wheat under various biotic and abiotic stresses were developed, which may subsequently help in developing simple crop growth model. Simulated datasets of biomass production and yield indicate that the water production functions based on seasonal evapo-transpiration and transpiration are rather site specific and do not reflect inter-seasonal weather variability. The variabilities in terms of inputs and agronomic management practices and yield of wheat in farmers’ fields in selected locations in north India have been quantified. The possibility of using the wheat growth simulator WTGROWS and InfoCrop has been explored to understand the cultivar diversity of wheat in the study areas. Impact of climatic variability, mainly the variations in the winter rains and abrupt changes in the temperatures during critical growth stages, are different in different production environments. There is a need to collate the information of growth response behaviour for Indian wheat, so that the productivity can be enhanced either by breaking the yield barriers through evolving suitable ideotypes or adopting suitable resource and agronomic management practices.

A dynamical ammonia emission parameterization for use in air pollution models

A parameterization of the temporal variation of ammonia (NH3) emission into the atmosphere is proposed. The parameterization relies on several simple submodels reflecting emission from stores and barns, agricultural practice in application of manure, and emission from grown crops. Some of the submodels depend on a simple crop growth model, which again depends on temperature variations throughout the year. The parameterization reflects the differences in agricultural practices, differences in the climate due to latitude/longitude, and differences in meteorological conditions between years. Measured as well as modeled meteorology can be applied by the parameterization, which is developed for use down to single farm level as well as in large‐scale physical/statistical Eulerian and Lagrangian air pollution models. The parameterization is based on simple principles and is applied for northwestern Europe. The simple principles ensure that the parameterization may be adapted to other climatic conditions. The proposed parameterization is considered as a large improvement compared to previous simpler models with fixed seasonal variation.

Implementing a dynamical ammonia emission parameterization in the large‐scale air pollution model ACDEP

Currently, very simple parameterizations for the seasonal variation of the ammonia (NH3) emissions are used in most air pollution models. This limits the capability to reproduce observed seasonal variations in the NH3 concentrations in rural regions with agricultural activity. A dynamical parameterization of the temporal variation of NH3 emission for application into air pollution models has therefore been proposed. The obtained improvements are demonstrated by implementing a dynamical NH3 emission parameterization into the large‐scale air pollution model ACDEP. Meteorology, information about agricultural practice, and a simple crop growth model are the basis for the parameterization. Fifteen additive functions are used to describe the different parts of the NH3 emissions from agriculture, and a 16th function is used to describe NH3 emission from catalysts used in road traffic. The new parameterization is tested by comparison of model results with NH3 measurements from Danish monitoring stations situated in agricultural areas during the years 1999–2001. Improvements in correlation coefficient of calculated diurnal mean concentrations from 0.42 to 0.70 have been achieved, and a monthly correlation coefficient up to 0.98 has been obtained for the Tange station in 2000. This validation shows that the new parameterization gives a substantial improvement in the model calculations of the NH3 concentrations in areas with high agricultural activities. Finally, the validation showed that optimum results are obtained by including a temperature correction on the gauss functions representing the emission sources.

Evaluation of seed yield determining factors of winter oilseed rape ( Brassica napus L.) by means of crop growth modelling

A simple crop growth model of winter oilseed rape ‘LINTUL-BRASNAP’, is briefly described and evaluated. The model simulates crop growth and development under optimum growth conditions and calculates 1) light reflection and absorption by the green canopy and flower layer, 2) total dry matter production based on light absorption and light-use efficiency by the green canopy 3) seed density (seeds m −2), 4) partitioning of dry matter to the seeds based on whether seed growth is limited by source or sink, 5) accumulation and mobilization of reserve carbohydrates and 6) crop phenological development. Two hypotheses on the duration of a critical period (CP) for seed set in a crop, (1) the duration of the flowering period and (2) a period of 350°Cd since onset of flowering, were tested by means of the model. Variation in seed density was better simulated with the second hypothesis. When the model was tested with data used for parameterization (cultivar Jet Neuf) and with other data (cultivar Victor), highly significant correlations were found between simulated and experimental data of various crop characteristics such as total dry matter production at various stages of crop development, seed density, pod density and seed yield. The differences between sowing dates were simulated better than the differences between years per sowing date. LINTUL-BRASNAP appears to be a useful tool for identifying the main crop characteristics responsible for variation in winter oilseed rape yield such as leaf area development, density of the flower layer and subsequent light absorption during the critical period of seed set. It can be used to explore options for improving the seed yield of winter oilseed rape.

The potential of hemp (Cannabis sativa L.) for sustainable fibre production: a crop physiological appraisal

Summary.Hemp (Cannabis sativa L.) fibre can be used as a raw material for paper and textile production. A comprehensive research programme in the Netherlands has concluded that fibre hemp is a potentially profitable crop, having the right profile to fit into sustainable farming systems. This paper presents an appraisal of the crop physiological characteristics and the agronomic potential of hemp. Parameter values of basic crop physiological characteristics such as light interception potential, radiation use efficiency and dry matter partitioning coefficients are given. The effect of crop management decisions such as cultivar choice, sowing date, plant density, and harvest date on the value of these parameters is discussed. A simple crop growth model was used to assess the yield potential of hemp for the climate of the Netherlands. Calculations made for a non‐stressed late‐flowering hemp crop sown on 15 April and harvested on 15 September give a stem dry matter yield of 17.1 t ha‐1. The effects of advancing or delaying sowing or harvest date on stem yield were calculated. Crop physiological characteristics of hemp are compared to those of kenaf (Hibiscus cannabinus L.). Radiation use efficiency and dry matter partitioning coefficients of the two crops are similar. Base temperatures for development and growth are lower in hemp than in kenaf. In a temperate climate with cool springs, canopy establishment will be more rapid in hemp than in kenaf. Hemp seems an excellent candidate to fill the niche for an annual fibre crop in a temperate climate.

Potential dry matter production of Miscanthus sinensis in The Netherlands

The perennial C 4-grass Miscanthus sinensis cv. ‘Giganteus’ is being advocated as a new biomass or fibre crop for north-western Europe. Above-ground yields of up to 40 tonnes of dry matter/ha/year have been claimed. Crop parameters of a fully-established M. sinensis stand were measured during the 1991 growing season. On October 7, above-ground dry matter yield was 21.8 t/ha. A simple crop growth model was used to compare the above-ground growth and yield of M. sinensis with that of silage maize ( Zea mays). The efficiency of conversion of intercepted PAR to above-ground dry matter of the two crops did not differ. In M. sinensis canopy establishment was much faster than in Z. mays. Yield potentials of Z. mays and of a fully established M. sinensis crop were estimated at 16.6 and 2.49 t of dry matter/ha, respectively. The major cause of the higher potential yield of M. sinensis is the larger amount of light this crop intercepts during the growing season. The prospects for M. sinensis as a new crop in The Netherlands are discussed within the framework of the present cropping system.

Radiation utilization efficiency and the growth of soybeans exposed to ozone: a comparative analysis

Effects of ozone (O 3) on field-planted soybeans ( Glycine max (L.) Merr. ‘Davis’) were examined using a simple crop-growth model that resolves the rate of increase in crop dry weight into the total amount of radiation intercepted by a crop canopy and the efficiency of conversion of this energy into dry matter (ϵ, termed utilization efficiency). The objectives were : (1) to examine the effects of O 3 on light interception and ϵ, and compare these results with a similar study by Unsworth et al. to determine whether the effects of O 3 are consistent from year to year, and (2) to determine whether ϵ is more sensitive to O 3 during reproductive than vegetative growth and whether allocation of aboveground biomass to seed is affected by O 3. Five O 3 concentrations (seasonal means of 30, 52, 57, 76 and 99 ppb) were maintained in open-top chambers over the growing season. All treatments were irrigated. Leaf, stem and pod weights and leaf area were measured weekly throughout the season. Total incident solar radiation and total radiation transmission through the canopy were measured with tube solarimeters and electronic integrating devices. Increased O 3 concentrations were accompanied by depressed leaf area expansion and earlier leaf senescence, but did not affect total light interception over the entire season. Utilization efficiency (ϵ) was reduced in high O 3 treatments and was the most important factor in the reduction of yield; ϵ for the whole season declined from 0.89 g MJ −1 in the 30-ppb treatment to a low of 0.68 g MJ −1 in the 99-ppb treatment. Efficiencies were higher during reproductive than vegetative growth, but were not more sensitive to O 3. Seed yield decreased from 450 g m −2 in the 30-ppb treatment to 320 g m −2 in the 99-ppb treatment. Fractional allocation of aboveground dry weight to seed was not affected by O 3. Reduced ϵ values with increasing O 3 concentrations were consistent with an earlier study by Unsworth et al. however, ϵ values for the vegetative phase of growth in this study were lower at all O 3 concentrations. Lower ϵ values were attributed to canopy light saturation because of higher incident radiation and water stress caused by a shallow rooting zone during a hot, dry growing season.

Irrigation Planning 2: Choosing Optimal Acreages within a Season

Three problems involved in planning and operating an irrigation system are (1) the short‐run problem of allocating a quantity of water in time over an irrigation season, (2) the intermediate‐run problem of deciding what area of crop to plant at the beginning of an irrigation season, and (3) the long‐run problem of deciding what area of land to develop for irrigation. This paper concerns the intermediate‐run problem. Crop growth is simulated with a simple crop growth model over a number of years on a large acreage supplied with water from a reservoir subject to stochastic inflows; the demand for water by the crop also being stochastic. Irrigation decision rules at each of a number of decision points in time are taken from the output of a simulation‐dynamic programing model used to solve the short‐run problem. The approach used allows for the abandonment for the rest of the season of portions of the area initially irrigated but does not allow for temporary abandonment, and reinstatement during the season, which is a limitation. The results show the best irrigated acreage to plant to be an approximately linear function of the reservoir contents at the beginning of the season. The function is found to be highly sensitive to changes in the profitability of the best dryland alternative enterprise. The cost of planting suboptimal acreages is found to be high.