Crop Growth Simulation Research Articles

A VI-based phenology adaptation approach for rice crop monitoring using UAV multispectral images

Accurate monitoring of crop biophysical parameters such as the plant area index (PAI) is essential for regional crop growth simulations and crop management. Many efforts have been made to estimate PAI by vegetation index (VI)-based methods, such as the widely used single empirical regression method and the piecewise relationship method. However, model structure error is inherent to the single-relationship method since it neglects the influence of phenology, and the discontinuity of the piecewise method can result in abrupt changes in estimates in the stage-transition period. Here we introduce a generalized and more accurate approach, termed the VI-based phenology adaptation (VPA) method, to estimate PAI of rice (Oryza sativa L.) using unmanned aerial vehicle (UAV)-based multispectral data. The VPA method automatically adapted the VI-PAI relationship at each observation time by bridging the VI, phenology and PAI into a continuous model. Moreover, the capability to monitor aboveground biomass of rice via the VPA method was evaluated. Intensive aerial and ground experiments were conducted in the controlled experimental plots and the randomly selected farmer-managed plots during two consecutive years (one year for calibration and another year for testing). The trajectory of the anchor-point that linked the discrete VI-PAI/biomass relationship for each phenological stage was developed and parameterized. The estimates of the single-relationship method were scattered when PAI was high, and this method failed to estimate PAI for the entire growing season. Significant underestimation was observed at the flowering stage (BBCH 64) for the piecewise methods. In contrast, the VPA approach outperformed the other methods for both controlled plots (R2 of 0.799 and RMSE of 0.536) and farmer-managed plots (R2 of 0.543 and RMSE of 0.671). Moreover, the VPA method exhibited superior generality for estimating other crop biophysical parameters such as biomass. Compared with the piecewise methods, the proposed method best estimated aboveground biomass, with 93% of biomass variation captured. The results demonstrated the steady performance of the VPA method for estimating various biophysical parameters throughout the entire growing season, indicating the promising potential of this method for cross-year and cross-site crop status monitoring.

Climate change impacts on crop water productivity and net groundwater use under a double-cropping system with intensive irrigation in the Haihe River Basin, China

Facing the high possibility of future severe water resources scarcity, the Haihe River Basin in the North China Plain is an important region for climate change studies. This study employed an improved SWAT model with a Management Allowed Depletion (MAD) auto-irrigation method to assess the impacts of future climate change on water cycle and crop growth of irrigated winter wheat and summer maize in the Haihe River Basin, China. A total of 27 Global Climate Models (GCMs) from the latest Coupled Model Intercomparison Project 6 (CMIP6) and two Shared Socioeconomic Pathways (SSP2–4.5 and SSP5–8.5) were used to drive the SWAT-MAD model to simulate crop growth and water balance components for two future periods of 2041–2070 (2050s) and 2071–2100 (2080s). Data from almost all GCMs showed an increase in annual mean maximum air temperature, minimum air temperature, total precipitation, and solar radiation across the Haihe River Basin. Winter wheat yields increased by 15.6% (SSP245) and 16.3% (SSP585) by 2050s, and by 17.1% (SSP245) and 10.0% (SSP585) by 2080s, respectively. Summer maize yields increased by 8.9% (SSP245) and 9.6% (SSP585) by 2050s, and by 12.6% (SSP245) and 6.1% (SSP585) by 2080s, respectively. The change of leaf area index (LAI) indicated an advance in phenology of crops, which might be related to the increase in temperature. In the four future climate scenarios, precipitation, percolation, and surface runoff increased by 19.16–57.25 mm, 7.26–22.18 mm, and 1.45–4.87 mm during winter wheat growing season, 125.41–205.24 mm, 51.03–96.37 mm, and 33.97–68.69 mm during summer maize growing season. However, the irrigation amounts decreased by 18.82–83.74 mm and 35.71–50.92 mm during wheat and maize growing seasons, respectively. The annual Net Groundwater Use (NGU) decreased with time and the crop water productivity (CWP) increased in the future, which have positive effects on the sustainable development in this double-cropping system with intensive irrigation management.

Analysing potato response to subsurface drip irrigation and nitrogen fertigation regimes in a temperate environment using the Daisy model

Growing potatoes in temperate regions often leads to nitrogen (N) leaching, however this could be significantly reduced with drip irrigation and nitrogen fertigation if the timing and amounts of the applications matched the crop demand. Determination of when and how much to apply is not straightforward, particularly because the N mineralisation during the potato-growing season is unknown. The present study aimed at providing a solution by the means of a well-calibrated crop model. A comprehensive dataset from a two year experiment on subsurface drip irrigated and N-fertigated potato was used to calibrate and validate the Daisy model with focus on potato crop parameters. The experiment included two irrigation regimes combined with five different N fertigation levels. The calibrated model simulated LAI evolution, crop growth, N uptake and final tuber dry matter and N yield satisfactorily with discrepancies between the simulated and measured variables when simulations were evaluated treatment by treatment. The root mean squared error (RMSE) of the simulated tuber dry matter yield were 0.57 and 0.69 t DM ha-1 for the Folva cultivar in 2013 and Sava cultivar in 2014, respectively. For tuber nitrogen yield, RMSE of the model simulations were 9.7 and 12.8 kg N ha-1 in 2013 and 2014, respectively. Results demonstrated that the calibrated Daisy model could be used as a DSS tool for the optimal irrigation and fertigation of Folva and Sava potato cultivars. Compared to the experimental data the same tuber dry matter yield was simulated with substantial savings 30–55% and 35–50% of irrigation water and N fertiliser, respectively. Results indicated an overestimation of bare soil evaporation, and inconsistences in the simulated effects of water and N stresses on the crop.

Calibration and validation of the FAO AquaCrop water productivity model for cassava (Manihot esculenta Crantz)

FAO’s water-driven crop growth simulation model, AquaCrop, was calibrated and validated for cassava (Manihot esculenta Crantz). Existing datasets, used in similar published works, were shared covering several years and regions (Colombia, Nigeria and Togo). Different varieties were tested for the case of Colombia and a single variety (TME-419) for Nigeria and Togo. Overall calibrated biomass simulations resulted in an R2 of 0.96 and a RMSE of 1.99 tonne DM/ha. As for dry tuber yield estimates, it was not possible to find a single harvest index for the ensembled varieties given their varying characteristics and limited data per variety. However, for the TME-419 variety (Nigeria and Togo) calibrated root tuber simulations yielded and R2 of 0.94 and a RMSE of 2.37 tonne DM/ha. A single crop-file was developed for different cassava varieties and agro-ecological regions, which can be applied with confidence to further study cassava related food security, water productivity, improved agronomic practices, etc.

Effect of projected climate scenarios on the yields of potato crop and agronomic adaptation options as evaluated by crop growth model

Potato is the most important non-cereal crop in the world and the most prominent winter season crop in India. Growth and yield of potato crop is very much sensitive to higher temperatures and the moisture stress. Hence, the anticipated increase of temperature due to global warming and climatic variability will have anadverse impact on potato production. Keeping this in view, a research work was carried out with the objectives to assess the impact of climate change on potato production and evaluating agronomic adaptation options through a crop growth simulation model (CGSM). Field experiments were carried out to prepare the minimum dataset for calibration and validation of one CGSM, namely InfoCrop. After validation, the model was used to predict the future tuber yield of ten selected stations situated under different agroclimatic regions of the State. In the future scenario 2050, the simulated yield for mid November planted crop likely to be about 11% less than the present level of mean yield. If the crop is planted in December, the percentage of yield reduction may be around 25%.The projected yield reduction, for the stations of higher latitude, is found to be negligible. Three possible agronomic adaptation options, viz., adjustment of date of planting, increase of seed rate and varying sprout length of seed tubers, have been tried as adaptation strategies to combat the adverse effects of climate change. It is concluded that the mid-November planting and longer sprout length will be the best adaptation option. However, the enhanced seed rate is not a viable adaptation option.

BioCro II: a software package for modular crop growth simulations

Abstract The central motivation for mechanistic crop growth simulation has remained the same for decades: to reliably predict changes in crop yields and water usage in response to previously unexperienced increases in air temperature and CO2 concentration across different environments, species and genotypes. Over the years, individual process-based model components have become more complex and specialized, increasing their fidelity but posing a challenge for integrating them into powerful multiscale models. Combining models is further complicated by the common strategy of hard-coding intertwined parameter values, equations, solution algorithms and user interfaces, rather than treating these each as separate components. It is clear that a more flexible approach is now required. Here we describe a modular crop growth simulator, BioCro II. At its core, BioCro II is a cross-platform representation of models as sets of equations. This facilitates modularity in model building and allows it to harness modern techniques for numerical integration and data visualization. Several crop models have been implemented using the BioCro II framework, but it is a general purpose tool and can be used to model a wide variety of processes.

Bias in Isimip Downscaled Climate Data and its Effect on Crop Growth Simulations for Sub-Saharan Africa

Bias in Isimip Downscaled Climate Data and its Effect on Crop Growth Simulations for Sub-Saharan Africa

Integration of Crop Growth Models and Genomic Prediction.

Crop growth models (CGMs) consist of multiple equations that represent physiological processes of plants and simulate crop growth dynamically given environmental inputs. Because parameters of CGMs are often genotype-specific, gene effects can be related to environmental inputs through CGMs. Thus, CGMs are attractive tools for predicting genotype by environment (G×E) interactions. This chapter reviews CGMs, genetic analyses using these models, and the status of studies that integrate genomic prediction with CGMs. Examples of CGM analyses are also provided.

Modeling impacts of climate change on spring wheat in northern India

This study attempts to quantitatively understand the impact of changes in meteorological drivers due to climate change on spring wheat in northern India using numerical experiments with the Simple and Universal CRop growth Simulator (SUCROS) model.The model was calibrated and evaluated for spring wheat cultivar HD2967 using observed crop and meteorological data from a field site at the Indian Agricultural Research Institute, New Delhi. Sensitivity studies were performed with the SUCROS model by incrementally changing the meteorological drivers to understand the underlying processes through which each meteorological driver affects spring wheat crop growth. The effect of climate change on spring wheat growth was estimated by conducting numerical experiments where the SUCROS model was driven with bias-corrected projections of future climate from six climate models for two scenarios for mid and end century. Results show that competitive/synergistic interactions between meteorological drivers lead to a slight increase in growth at the beginning of the growing season, and a strong decrease of about 50 per cent during the later stage. Apart from improving our understanding of crop growth processes, this study has also policy implications for agriculture and food security in the context of climate change.

Improving sugarcane growth simulations by integrating multi-source observations into a crop model

Accurate crop growth simulations and yield estimation play a crucial role in agricultural development and food security. Incorporating multi-source observations into crop growth models could reduce the prediction uncertainty propagated from input data and model parameters. However, the value of different data sources varies. Incorporating redundant data into models not only increases computational cost, but also introduces additional prediction uncertainties. The objective of this study is to investigate the value of three common agronomy variables (plant height, PH; leaf area index, LAI; and soil moisture, SM) for sugarcane growth simulations and explore which variable(s) have the largest information content and hence should be included in data assimilation system. The measurements of PH, LAI and SM data are collected through a two-year sugarcane experiment (in 2016–2017) at Chongzuo station (Guangxi, China). Results show that the value of SM is the lowest among all three variables for sugarcane yield estimation if the spatial heterogeneity of water and nutrient both exist. When sugarcane plots have relatively homogeneous cultivation density, it is preferable to incorporate PH data into the model. In contrast, assimilation of LAI might be more suitable when the cultivation density and tiller number contain strong spatial variability. Moreover, compared with traditional LAI & SM fusion strategy, the fusion of LAI and PH data is recommended to obtain more robust sugarcane simulation results. Furthermore, observations during the elongation period provide the most valuable information for sugarcane growth simulation and yield estimation, while those in the emergence and tillering period are less informative.

A modified DSSAT‐CERES model for simulating summer maize growth under film mulching

AbstractPlastic film mulching is an effective agronomic measure for increasing crop yield, and the decision support system for agricultural technology transfer (DSSAT) model is an effective tool for simulating crop growth and water management. However, the DSSAT model does not have a specific soil evaporation module for plastic film mulching. In this paper, a method for estimating soil evaporation capacity under film mulching conditions based on the small hole effect was developed and embedded in the DSSAT model. Then, considering the compensation effect of soil temperature on air temperature under the condition of film mulching, the DSSAT model was modified accordingly. The results show that the average temperature compensation coefficient for 2 yr from sowing to emergence was 0.695 °C, and 0.305 °C from emergence to jointing. The improved DSSAT model can simulate the growth of summer maize (Zea mays L.) and changes in soil water content. Compared with the original model, the average relative RMSE of the simulated and observed soil water content of the modified DSSAT model improved by 2.26 and 1.63%, respectively. The modified model can improve the simulation accuracy, which shows that the method proposed in this paper can provide an effective way for the simulation of crop growth process under film mulching conditions.

Satellite rainfall bias assessment for crop growth simulation – A case study of maize growth in Kenya

In this research, the performance of satellite rainfall estimates (SREs) for crop growth simulation was investigated. Rainfall products selected were CHIRPS 2.0, CMORPH 1.0, MSWEP 2.2, and RFE 2.0. In-situ rainfall from 20 stations within the Lake Victoria basin in Kenya served as reference. Rainfall products were evaluated for onset days, rainfall depths, dry spells, and rainfall occurrence for four crop growth stages. Assessment was on a daily time step for the period 2012–2018 and on a point-to-pixel basis. Results showed that SREs exhibit large variation in timing of rainfall arrival. SREs exhibited largest interannual and spatial spreads in representing dry spell length during the flowering stage with CMORPH and CHIRPS showing best and weakest results, respectively. Bias of SREs in representing dry spells was smaller during early growth stages. Detecting rainfall occurrence by the SREs weakened as the growing season progressed. MSWEP, followed by RFE2, produced the best results in detecting rainfall events, while falsely detected rainfall was frequent in CHIRPS, particularly in later growth stages. SREs generally performed better during a wet than a dry growing season. SREs indicated less bias in rainfall depths during the early stages of crop growth but deteriorated at later stages. MSWEP and CMORPH exhibited the least and highest interannual spread in relative bias, respectively. In associating biases to severe and extreme water stress, based on crop water requirement satisfaction index, effects were more prevailing in the ripening than flowering stages. Findings of this study suggest that SREs can serve as input to crop growth modelling, but validation of SREs with rain gauge observed counterparts is essential.

Simulation of maize crop growth using an improved crop model considering the disintegrated area of biodegradable film

• An improved crop growth model (FCM) under biodegradable film mulching was developed. • FCM model performed well in capturing the temporal trends in crop growth dynamics. • Biodegradable film mulching results in a slight decrease of crop yield. • An irrigation depth of 22.5 mm as optimal treatment was recommended in arid region. • Disintegrated rate should not exceed 63 % and 38 % in 2018 and 2019, respectively. Biodegradable film is recognized as a promising alternative to plastic film within crop mulching. This is because biodegradable film disintegrates into water and carbon dioxide through soil microorganisms-facilitated catalytic decomposition during the crop growth season. However, biodegradable film mulching (BFM) also has some disadvantages, such as the water and heat conservation being poor during nighttime period. Crop growth and yield is reduced by a suboptimal distribution of soil water and temperature. Therefore, there is value in assessing the effects of BFM on crop growth during daytime and nighttime periods, and particularly under different irrigation levels. This study developed a crop growth model (FCM) considering the disintegrated area of biodegradable film mulching to capture crop growth dynamics under different soil water contents and mulching areas. The disintegrated area of biodegradable film was determined by an image method. The model was firstly calibrated and validated using observed data collected over two years (2018 and 2019) under three mulching treatments [plastic film mulching (PFM), biodegradable film mulching (BFM), and no film mulching (NFM)] and three irrigation levels (irrigation depths of 30, 22.5, and 15 mm). The model was then used to evaluate crop growth rate ( k ), crop yield ( CY ), and water use efficiency (WUE) during daytime and nighttime periods. The results showed that the simulation accuracy of the FCM model was sufficiently high under different treatments, with a root mean square error ( RMSE) of 284.6 kg ha –1 . The benefits of BFM for crop growth were similar to that of PFM under a low range of effective accumulated soil temperature (EAST). However, differences in crop growth variables between PFM and BFM increased with an increase in EAST. Average leaf area index ( LAI ) and aboveground dry biomass ( AB ) during both years increased by 16.9 % and 39.2 % under PFM compared to that under BFM, respectively. In general, the harvest index ( HI ), CY , and water use efficiency (WUE) under BFM were lower than that under PFM. There were comparable variations in crop growth under different irrigation levels. The highest WUE occurred in the BFM 22.5 treatment at 44.5 kg ha –1 mm –1 . Therefore, this study recommends an irrigation depth of 22.5 mm as an optimal irrigation treatment in an arid region.

Evaluating APSIM-and-DSSAT-CERES-Maize Models under Rainfed Conditions Using Zambian Rainfed Maize Cultivars

Crop model calibration and validation is vital for establishing their credibility and ability in simulating crop growth and yield. A split–split plot design field experiment was carried out with sowing dates (SD1, SD2 and SD3); maize cultivars (ZMS606, PHB30G19 and PHB30B50) and nitrogen fertilizer rates (N1, N2 and N3) as the main plot, subplot and sub-subplot with three replicates, respectively. The experiment was carried out at Mount Makulu Central Research Station, Chilanga, Zambia in the 2016/2017 season. The study objective was to calibrate and validate APSIM-Maize and DSSAT-CERES-Maize models in simulating phenology, mLAI, soil water content, aboveground biomass and grain yield under rainfed and irrigated conditions. Days after planting to anthesis (APSIM-Maize, anthesis (DAP) RMSE = 1.91 days; DSSAT-CERES-Maize, anthesis (DAP) RMSE = 2.89 days) and maturity (APSIM-Maize, maturity (DAP) RMSE = 3.35 days; DSSAT-CERES-Maize, maturity (DAP) RMSE = 3.13 days) were adequately simulated, with RMSEn being <5%. The grain yield RMSE was 1.38 t ha−1 (APSIM-Maize) and 0.84 t ha−1 (DSSAT-CERES-Maize). The APSIM- and-DSSAT-CERES-Maize models accurately simulated the grain yield, grain number m−2, soil water content (soil layers 1–8, RMSEn ≤ 20%), biomass and grain yield, with RMSEn ≤ 30% under rainfed condition. Model validation showed acceptable performances under the irrigated condition. The models can be used in identifying management options provided climate and soil physiochemical properties are available.

Comparisons among four different upscaling strategies for cultivar genetic parameters in rainfed spring wheat phenology simulations with the DSSAT-CERES-Wheat model

Cropping system models are widely used to assess the impacts of and adaptation practices to climate change on agricultural production. However, crop growth simulations at large scales have often lacked consideration of variation in crop cultivars, which were represented by different sets of genetic coefficients in crops models. In this study, taking the phenology of spring wheat (Triticum aestivum L.) as an example, we compared four different strategies for upscaling genetic parameters in phenology simulations at large scales with two experimental datasets. The first dataset was from field experiments comprising 40 different spring wheat cultivars at Altay (2014) and Yangling (2015–2017) station; the second dataset was historical (2010–2014) observed phenology records from 57 national agro-meteorological observation stations in China. The four strategies were the representative cultivar estimated at a single site (SSPs), the representative cultivar estimated at the 57 sites (NRPs), the various representative cultivars estimated at different agro-ecological zones (RRPs), and the virtual cultivars generated from the posterior distributions (VCPs). The posterior distributions aforesaid were established based on the calibrated parameter values of the 40 different spring wheat cultivars planted in Yangling. Then, 1000 sets of VCPs were randomly sampled from the posterior distributions. The results indicated that both the SSPs and NRPs strategy obtained large errors and uncertainties in spring wheat phenology simulations in China since only one representative cultivar was used. The RRPs strategy achieved the second high and the highest accuracy in anthesis and maturity data simulations. The VCPs strategy obtained the highest accuracy in anthesis simulation but relative larger errors in maturity simulation. The VCPs strategy can be directly used in large-scale crop growth simulations without tedious process of calibration. Hence, this strategy is recommended in areas where observations are scarce and for model users who not good at model parameter estimation.

Simulating the Leaf Area Index of Rice from Multispectral Images

Accurate estimation of the leaf area index (LAI) is essential for crop growth simulations and agricultural management. This study conducted a field experiment with rice and measured the LAI in different rice growth periods. The multispectral bands (B) including red edge (RE, 730 nm ± 16 nm), near-infrared (NIR, 840 nm ± 26 nm), green (560 nm ± 16 nm), red (650 nm ± 16 nm), blue (450 nm ± 16 nm), and visible light (RGB) were also obtained by an unmanned aerial vehicle (UAV) with multispectral sensors (DJI-P4M, SZ DJI Technology Co., Ltd.). Based on the bands, five vegetation indexes (VI) including Green Normalized Difference Vegetation Index (GNDVI), Leaf Chlorophyll Index (LCI), Normalized Difference Red Edge Index (NDRE), Normalized Difference Vegetation Index (NDVI), and Optimization Soil-Adjusted Vegetation Index (OSAVI) were calculated. The semi-empirical model (SEM), the random forest model (RF), and the Extreme Gradient Boosting model (XGBoost) were used to estimate rice LAI based on multispectral bands, VIs, and their combinations, respectively. The results indicated that the GNDVI had the highest accuracy in the SEM (R2 = 0.78, RMSE = 0.77). For the single band, NIR had the highest accuracy in both RF (R2 = 0.73, RMSE = 0.98) and XGBoost (R2 = 0.77, RMSE = 0.88). Band combination of NIR + red improved the estimation accuracy in both RF (R2 = 0.87, RMSE = 0.65) and XGBoost (R2 = 0.88, RMSE = 0.63). NDRE and LCI were the first two single VIs for LAI estimation using both RF and XGBoost. However, putting more than one VI together could only increase the LAI estimation accuracy slightly. Meanwhile, the bands + VIs combinations could improve the accuracy in both RF and XGBoost. Our study recommended estimating rice LAI by a combination of red + NIR + OSAVI + NDVI + GNDVI + LCI + NDRE (2B + 5V) with XGBoost to obtain high accuracy and overcome the potential over-fitting issue (R2 = 0.91, RMSE = 0.54).

Improving winter wheat biomass and evapotranspiration simulation by assimilating leaf area index from spectral information into a crop growth model

Data assimilation, a state-of-the-art method that merges remote sensing data with a dynamic model to improve model performance, has been widely used in land surface process modeling. Application of data assimilation under various water conditions can provide insight of crop response to different water supply rates, which is useful for agricultural water management in arid and semi-arid regions. For this purpose, we developed a generic data assimilation methodology by integrating both the Shuffled Complex Evolution (SCE) and the Ensemble Kalman Filter (EnKF) algorithms into the Simple Algorithm For Yield and Evapotranspiration (SAFYE) model to provide improved simulation of winter wheat biomass and yield, and simulation of evapotranspiration (ET) under different water-supply scenarios. An experiment with nine irrigation scenarios was conducted during the 2013—2015 growing cycles. Field spectral data were employed to retrieve the leaf area index (LAI), which was then used as a single state variable to determine other parameters in the SAFYE model using a global optimization algorithm. Time-series LAI was eventually assimilated in the SAFYE model based on the EnKF algorithm to improve overall model simulation. The results showed that the simulated crop growth dynamics followed the measurements well in most cases when the estimated LAI was assimilated. The accuracy of simulated biomass at the daily step was improved, with the maximum RMSE decreased from 199.4 to 123.8 g m−2 and from 466.6 to 393.4 g m−2 for the 2013–2014 and 2014–2015 growing seasons respectively. A good agreement was also achieved between the estimated and field measured grain yield (R2 = 0.901, RMSE= 31.9 g m−2, RRMSE=6.55%) for both growing seasons. The simulation of soil water content in the top 0—20 cm soil layer was better (RMSE: 3.3—5.0 mm) than that of 0—100 cm layer (RMSE: 11.7—29.6 mm). Accuracy of the simulated ET under early-stage water deficit scenarios was lower than that under other scenarios, with a positive mean relative error of 14% (3.4—24.3%) during two growing seasons. This study demonstrates the great potential of coupling remote sensing data to improve the performance of SAFYE model in modeling winter wheat growth.

Effect of different soil moisture regimes on plant growth and water use efficiency of Sunflower: experimental study and modeling

BackgroundClimate change and increasing demand in non-agricultural sectors profoundly affect the availability and quality of water resources for irrigated agriculture. The FAO AquaCrop simulation model provides a sound theoretical framework to investigate crop yield response to environmental stress. This model has successfully simulated crop growth and yield as influenced by varying soil moisture environments for crops. Integrating crop models that simulate the effects of water on crop yield with targeted experimentation can facilitate the development of irrigation strategies for high yield procurement and improving farm level water management and water use efficiency (WUE) under climatic condition of District Hyderabad, Sindh, Pakistan.ResultsThis study was based on completely randomized block design with three treatments including T1 (30% soil moisture depletion), T2 (50% soil moisture depletion) and T3 (70% soil moisture depletion) with three replicates. In order to determine the crop water requirements under desired treatments, the gypsum blocks were used for computing the daily soil moisture depletion. The result shows that total volume of water applied to crop under T1, T2 and T3 was 9689, 5200 and 2045 m3 ha−1, respectively. As a result, the grain yield under T1, T2 and T3 was 13.2, 12.1 and 14.3 t ha−1, respectively. These results advocate that total yield of crop under T1 and T2 was less as compared to T3. The T3 gave higher yield and WUE compared than other treatments. On the other hand, results revealed that the simulated sunflower yields showed a good agreement with their measured under T3. The simulated grain yield was 15.5 t ha−1, while the measured yield varied from 12.1 to 14.3 t ha−1. This study suggested that WUE under T3 was more as compared to T1 and T2. The results showed that the T3 gave the highest crop yield in relation to WUE and optimize yield of sunflower crop under water scarcity.ConclusionThe Aquacrop model could very well predict crop yield and WUE at T3 under experiential region for sunflower production.

A recommended nitrogen application strategy for high crop yield and low environmental pollution at a basin scale

Mitigating environmental pollution and sustaining grain production have been foundational issues in sustainable development, however, ascertaining the optimal balance remains poorly investigated. This study used the Soil and Water Assessment Tool (SWAT) model to simulate crop growth and nitrogen loss, established the mapping relationship between nitrogen input to yield and water quality, and proposed a general method to determine a nitrogen application strategy for high yield and low pollution at a basin scale. Lake Xiaoxingkai basin, which is the primary maize producing area in China as well as an internationally important wetland distribution area, was used as a case study. First, we designed application scenarios for 10 base fertilizers (B1–B10) and 10 topdressing fertilizers (T1–T10) and evaluated their combined effects of maize growth to identify the critical nitrogen fertilizer rates determined under fixed and dynamic base/topdressing ratios. Then, the critical base and topdressing fertilizer rates were determined. Based on the mapping relationship between nitrogen fertilizer rate and nitrogen loss, we then revealed water quality at the basin outlet under the critical base and topdressing fertilizer rates. Finally, we proposed alternative nitrogen application strategies for high yield and low pollution while considering the different preferences of decision-makers for the economy, agriculture, and environment. We found that adjusting the ratio of base to topdressing fertilizer may create a win-win situation for agriculture and the environment, which will provide a scientific basis for sustainable development.

A simulation of variable rate nitrogen application in winter wheat with soil and sensor information - An economic feasibility study

CONTEXTVariable rate nitrogen (N) management strategies are often based on information about soil texture or information from canopy sensors, mounted on ground-based vehicles or satellites. However, disentangling the effect of each information type on N management strategy with experimental studies is often difficult, as results are only valid for the specific experimental conditions as well as the weather conditions for specific years. An alternative to this is to use deterministic crop growth models to generate a wider range of weather x treatment combinations. OBJECTIVEThis study examines if ‘static’ soil profile information or ‘dynamic’ canopy sensor type information provide a better basis for decision making concerning N-application at the subfield level. METHODSThe DAISY model was used to simulate crop growth in a five-year crop rotation on six soil profiles found in a heterogeneous sandy loam field. A range of management descriptions and simulations were made using 5 × 500 years of synthetic weather data with each crop in a five-year rotation set at the first year of the five parallel simulations. Simulated growth variables were used as proxies for a ‘dynamic’ canopy sensor information system. The differential gross margin was then calculated for a range of price relations between fertilizer (model input) and wheat yield (model output), including wheat price adjustments according to protein content. From regressions and backward induction analysis, the N application that maximizes the expected grain revenue minus fertilizer expenditure was estimated for four information cases; Case 1) Uniform application, assuming no prior information, Case 2) application based on soil type information, Case 3) application based on canopy sensor information and Case 4) application based on combined soil and canopy sensor information. RESULTS AND CONCLUSIONSFindings from this study indicated that decisions with soil information alone provide an annual differential gross margin of variable rate application (without considering cost of information and technology) between 3.88 and 13.30 € ha−1 across price and soil variation. This margin approximately doubled with applications based on canopy sensor information and further doubled again with applications based on both soil and canopy sensor information. SIGNIFICANCEThus, knowledge of the soil has the potential to improve interpretation of sensor signals for fertilization planning. The results may guide developers to decide on what type of information should be included in their decision support systems.