Crop Growth Model Research Articles

Study on the Piecewise Inverse Model of Accumulated Temperature Based on Skewness-Distribution Parameters of Canopy Images in Pepper

The crop leaf color is tightly connected with its meteorological environment. Color gradation skewness-distribution (CGSD) parameters can describe the information of leaf color more accurately, systematically, and comprehensively from five dimensions. We took photographs of pepper growing in the greenhouse at a fixed time every day and observed the meteorological factors. The results showed that the CGSD parameters were significantly correlated with meteorological factors, especially with the accumulated temperature, which showed the strongest correlation. Since the relationship between canopy leaf color and accumulated temperature is nonlinear, the piecewise inversion models were constructed by taking the stationary point of the high-order response model of Gskewness to accumulated temperature as the point of demarcation. The rate of outliers had decreased by 57.72%; moreover, the overall inversion accuracy had increased by 3.31% compared with the linear model directly constructed by the stepwise regression. It was observed that the pepper in the greenhouse had a different response to the same meteorological environmental stimulus before and after the stationary point. This study will provide a new method for constructing crop growth models in future research.

Performance evaluation of AquaCrop and DSSAT-SUBSTOR-Potato models in simulating potato growth, yield and water productivity under various drip fertigation regimes

Water and fertilizer are two important factors affecting crop growth and yield. Crop growth models are powerful tools for making irrigation and fertilization scheduling and predicting crop yield. The performance of crop models on potato under different irrigation amounts along with different N, P and K rates has been rarely evaluated, especially under drip fertigation. In this study, the accuracy of AquaCrop and DSSAT-SUBSTOR-Potato models in simulating potato growth, yield and water productivity under different drip fertigation regimes was compared. For model calibration and validation, a two-year field experiment with three irrigation levels (W1, 60% ETC; W2, 80% ETC and W3, 100% ETC, where ETC was the crop evapotranspiration) and four fertigation (N-P2O5-K2O) rates (F1: 100–40–150 kg ha−1, F2: 150–60–225 kg ha−1, F3: 200–80–300 kg ha−1 and F4: 250–100–375 kg ha−1) was carried out in a sandy region of northwest China in 2018 and 2019. The statistical indicators showed that the AquaCrop model gave satisfactory predictions of canopy cover (R2≥ 0.92, NRMSE ≤ 14.7%, d ≥ 0.96 and RMSE ≤ 10.8%), total dry matter (R2≥ 0.92, NRMSE ≤ 25.0%, d ≥ 0.94 and RMSE ≤ 2.2 t ha−1), final dry tuber yield (R2≥ 0.57, NRMSE ≤ 12.21%, d ≥ 0.82 and RMSE ≤ 0.83 t ha−1) and fresh tuber yield (R2≥ 0.53, NRMSE ≤ 12.02%, d ≥ 0.81 and RMSE ≤ 4.91 t ha−1) in the four fertigation treatments (F1-F4) under W3 and W2 during the two potato growing seasons. The final total dry matter and tuber yield under W1 were over-estimated. The simulated water productivity and soil water content also agreed well with the observations (R2≥ 0.91, NRMSE ≤ 5.51%, d ≥ 0.94 and RMSE ≤ 0.48 kg m−3, and R2≥ 0.40, NRMSE ≤ 14.8%, d ≥ 0.77 and RMSE ≤ 11.5 mm). The simulation accuracy of DSSAT-SUBSTOR-Potato model was lower than that of AquaCrop model, which only showed a goodness of fit between simulated total dry matter, soil water content, tuber yield, water productivity and observations under high irrigation amount with high fertilization rate (W3F4). Since the observed tuber yield increased with the increase of irrigation level (60–100% ETC) under the same fertigation rate, 120%ETC and 40%ETC were further simulated using the AquaCrop model to test the changes in tuber yield with further increased or decreased irrigation levels. The scenarios simulation showed that the tuber yield was only increased by 0.19%−1.52% under 120%ETC compared with that under 100% ETC, but 18.0% more irrigation water was consumed. Comprehensively considering water resources and tuber yield, the irrigation level of 100% ETC (W3) along with fertigation (N-P2O5-K2O) rate of 200–80–300 kg ha−1 (F3) was recommended for the sustainable potato production in the study region. The results of this study can provide guidance for the application of crop models in drip-fertigated potato.

Machine learning versus crop growth models: an ally, not a rival.

The rapid increases of the global population and climate change pose major challenges to a sustainable production of food to meet consumer demands. Process-based models (PBMs) have long been used in agricultural crop production for predicting yield and understanding the environmental regulation of plant physiological processes and its consequences for crop growth and development. In recent years, with the increasing use of sensor and communication technologies for data acquisition in agriculture, machine learning (ML) has become a popular tool in yield prediction (especially on a large scale) and phenotyping. Both PBMs and ML are frequently used in studies on major challenges in crop production and each has its own advantages and drawbacks. We propose to combine PBMs and ML given their intrinsic complementarity, to develop knowledge- and data-driven modelling (KDDM) with high prediction accuracy as well as good interpretability. Parallel, serial and modular structures are three main modes can be adopted to develop KDDM for agricultural applications. The KDDM approach helps to simplify model parameterization by making use of sensor data and improves the accuracy of yield prediction. Furthermore, the KDDM approach has great potential to expand the boundary of current crop models to allow upscaling towards a farm, regional or global level and downscaling to the gene-to-cell level. The KDDM approach is a promising way of combining simulation models in agriculture with the fast developments in data science while mechanisms of many genetic and physiological processes are still under investigation, especially at the nexus of increasing food production, mitigating climate change and achieving sustainability.

Rice yield estimation using a CNN-based image-driven data assimilation framework

The accurate prediction and estimation of grain yield for smallholder farms are crucial for agricultural management . In this study, we propose an image-driven data assimilation framework that enables smallholder farmers to estimate crop yield. In the framework, a convolutional neural network was trained using label distribution learning to estimate the probability distribution of rice crop states from images. The mean and variance of the estimated probability were regarded as observed values and variances, which were then assimilated into the ORYZA2000 crop growth model to estimate the final yield. Compared with the accuracy (R 2 = 0.410, RMSE = 1.059 t ha −1 ) of yield estimation by assimilating field sampling observations, the proposed method significantly improved accuracy, with R 2 = 0.646 and RMSE = 0.679 t ha −1 . The results demonstrated the feasibility of driving a crop growth model using easily available camera images. Additionally, the probability distribution estimation method made it possible to quantify the error for each observation. The method provides a new perspective for quantifying the observation error, thereby indicating a potential research direction for data assimilation. After that, we used numerous simulation cases to determine the most valuable observation combination for final grain yield estimation, the final yield prediction ability at different development stages, and the model performance affected by number of images. Finally, a series of factors that may affect the applicability of the framework was discussed. This study establishes an intelligent data assimilation framework for smallholder farmers that can absorb crop phenotyping data from digital cameras with a low cost and simple implementation.

Identification of agro-physiological traits of lentil that reduce risks of drought.

Ideotype breeding is an essential approach for selection of desired combination of plant traits for testing in crop growth model for potential yield gain in specific environments and management practices. Here we parameterized plant traits for untested lentil cultivars for the APSIM-lentil model in phenology, biomass, and seed yield. We then tested these against independent data and applied the model in an extrapolated analysis (i) to assess the impact of drought on productivity across different rainfall environments; (ii) to identify impactful plant traits and (iii) to design new lentil ideotypes with a combination of desirable traits that mitigate the impact of drought, in the context of various agronomic practices across a wide range of production environments. Desirable phenological and physiological traits related to yield were identified with RUE having the greatest effect on yield followed by HI rate. Leaf size significantly affected seed yield (p< 0.05) more than phenological phases. The physiological traits were integrated into four ideotype designs applied to two baseline cultivars (PBA Hallmark XT and PBA Jumbo2) providing eight ideotypes. We identified a combination of genetic traits that promises a yield advantage of around 10% against our current cultivars PBA Hallmark XT and PBA Jumbo2. Under drought conditions, our ideotypes achieved 5 to 25% yield advantages without stubble and 20 to 40% yield advantages with stubble residues. This shows the importance of genetic screening under realistic production conditions (e.g., stubble retention in particular environments). Such screening is aided by the employment of biophysical models that incorporate both genetic and agronomic variables that focus on successful traits in combination, to reduce the impact of drought in the development of new cultivars for various environments. Stubble retention was found to be a major agronomic contributor to high yield in water-limiting environments and this contribution declined with increasing growing season rainfall. In mid- and high-rainfall environments, the key drivers of yield were time of sowing, physiological traits and soil type. Overall, the agronomic practices, namely, early sowing, residue retention and narrow row spacing deceased the impact of drought when combined with improved physiological traits of the ideotypes based on long term climate data.

Analysis and evaluation of a dynamic model for greenhouse lettuce growth

Aim of study: We analyzed and evaluated a nonlinear dynamic crop growth model called NICOLET B3, which can predict the dry and fresh matter content of lettuce in greenhouses. Area of study: Calibration was performed using experimental data obtained from the literature. The experiment was carried out in Saltillo, Mexico, and in a greenhouse in Beijing, China. Material and methods: We identified and discussed the feasibility of the studied model with multi-dimensional evaluation criteria. Meanwhile, a sensitivity analysis of input variables was conducted. After that, the least square identification method was used to calibrate the most sensitive parameter values to improve the robustness of the model. Main results: Results demonstrate that: i) the NICOLET B3 model is able to predict the fresh and dry matter production of lettuce with satisfactory accuracy verified (R2 = 0.9939 for fresh matter and R2 = 0.9858 for dry matter); ii) temperature has the most obvious impact on the model performance, compared with photosynthetically active radiation and CO2 concentration; iii) the model could perform well with only two inputs. Research highlights: Simulation results of evaluated NICOLET B3 model have a perfect goodness-of-fit. A method of calibrating parameters of the model and sensitivity analysis of three input variables of the model can facilitate its application.

Exploring avenues for agricultural intensification: A case study for maize-soybean in the Southern US region

CONTEXTCrop intensification is a key aspect for achieving global food security and exploring options for optimizing land use and environmental resources. Following this rationale, a maize (Zea mays L.) – soybean (Glycine max L.) double cropping was tested to intensify current agricultural systems in the Southern region of the United States (US). OBJECTIVEThe aims of this study were to i) assess the feasibility of cultivating maize-soybean double crop in the Southern US, ii) explore the best scenario of maize-soybean crops integrating field data with APSIM crop growth model via calculation of the land equivalent ratio (LER), and iii) evaluate the risk of crop failure for each crop combination. METHODSFor this case study, one year field research and in-silico experiments using 30-years of weather data were analyzed to evaluate the different lengths of growth cycle for both crops for yield, LER, and percentage of success. The feasibility of the maize-soybean sequence was determined by increasing yield gain for the soybean crop while maintaining similar yield for maize crop. RESULTS AND CONCLUSIONSSimulated LER values ranged from 1.1 to 1.3 with the maize hybrid 110 comparative relative maturity followed by a soybean maturity group 3.0 as the best combination for maximum LER. Lastly, long-term simulations confirmed the low risk associated with maize-soybean double crop sequences across sowing dates. SIGNIFICANCEExpanding the implementation of this farming system could produce additional benefits not only on productivity and land use efficiency but in overall economic return for farmers in the Southern US region.

Pear Tree Growth Simulation and Soil Moisture Assessment Considering Pruning

Few studies deal with the application of crop growth models to fruit trees. This research focuses on simulating the growth process, yield and soil moisture assessment of pear trees, considering pruning with a modified WOrld FOod Studies (WOFOST) model. Field trials (eight pruning treatments) were conducted in pear orchards in Alaer and Awat in Xinjiang, China and data were measured to calibrate and evaluate the modified model. In two pear orchards, the simulated total dry weight of storage organs (TWSO) and leaf area index (LAI) were in good agreement with the field measurements of each pruning intensity treatment, indicating that the R2 values of TWSO ranged from 0.899 to 0.976, and the R2 values of LAI ranged from 0.849 to 0.924. The modified model also showed high accuracy, with a normalized root mean square error (NRMSE) ranging from 12.19% to 26.11% for TWSO, and the NRMSE values for LAI were less than 10%. The modified model also had a good simulation performance for the soil moisture (SM) under all eight pruning intensity treatments, showing good agreement (0.703 ≤ R2 ≤ 0.878) and low error (NRMSE ≤ 7.47%). The measured and simulated results of different pruning intensities showed that the highest yield of pear trees was achieved when the pruning intensity was about 20%, and the yield increased and then decreased with the increase in pruning intensity. In conclusion, the modified WOFOST model can better describe the effects of summer pruning on pear tree growth, yield and soil moisture than the unmodified model, providing a promising quantitative analysis method for the numerical simulation and soil moisture assessment of fruit tree growth.

An Optimal Decision Support System Based on Crop Dynamic Model for N-Fertilizer Treatment.

The efficient handling of nitrogen has become a critical issue in modern agriculture, from a financial standpoint, as well as in regard to reducing the environmental impacts of using an excessive amount of nitrogen fertilizer. Manure compost is useful for maintaining or raising soil chemical levels without excessive accumulation; however, for the best grain yield, it should be combined with N fertilizer. Via this study, we aimed to develop an optimal decision support system that indicates when to initiate fertilization based on nitrogen-limited (N-limited) crop growth dynamics. An optimal nitrogen fertilizer (N-fertilizer) management system increases crop yield while maintaining a balance between fertilizer supply and crop demand. This study used the N-limited crop growth model (LINTUL3) to develop an optimal decision support system. In this work, we formulated and resolved two optimization challenges: (i) maximization of biomass growth; and (ii) maximization of growth with the least cost paid on N-fertilizer and its application. Furthermore, two case studies were developed based on the number of fields: (i) optimization for a single field, and (ii) optimization for multiple fields. In the case of multiple fields, it is hypothesized that a fertilizer treatment for one field can leak to other fields and affect the nitrogen dynamics of different fields. Finally, numerical simulations were carried out supporting the theory developed in the paper. The simulations showed that when the proposed work was employed to achieve the goal of optimal nitrogen management for a crop, a to increase in biomass growth under certain scenarios was attained.

Using genomic prediction with crop growth models enables the prediction of associated traits in wheat.

Crop growth models (CGM) can predict the performance of a cultivar in untested environments by sampling genotype-specific parameters. As they cannot predict the performance of new cultivars, it has been proposed to integrate CGMs with whole genome prediction (WGP) to combine the benefits of both models. Here, we used a CGM-WGP model to predict the performance of new wheat (Triticum aestivum) genotypes. The CGM was designed to predict phenology, nitrogen, and biomass traits. The CGM-WGP model simulated more heritable GSPs compared with the CGM and gave smaller errors for the observed phenotypes. The WGP model performed better when predicting yield, grain number, and grain protein content, but showed comparable performance to the CGM-WGP model for heading and physiological maturity dates. However, the CGM-WGP model was able to predict unobserved traits (for which there were no phenotypic records in the reference population). The CGM-WGP model also showed superior performance when predicting unrelated individuals that clustered separately from the reference population. Our results demonstrate new advantages for CGM-WGP modelling and suggest future efforts should focus on calibrating CGM-WGP models using high-throughput phenotypic measures that are cheaper and less laborious to collect.

The development of a new crop growth model SwitchFor for yield mapping of switchgrass.

Switchgrass is a promising energy crop has the potential to mitigate global warming and energy security, improve local ecology and generate profit. Its quantitative traits, such as biomass productivity and environmental adaptability, are determined by genotype-by-environment interaction (GEI) or response of genotypes grown across different target environments. To simulate the yield of switchgrass outside its original habitat, a genotype-specific growth model, SwitchFor that captures GEI was developed by parameterising the MiscanFor model. Input parameters were used to describe genotype-specific characteristics under different soil and climate conditions, which enables the model to predict the yield in a wide range of environmental and climate conditions. The model was validated using global field trail data and applied to estimate the switchgrass yield potentials on the marginal land of the Loess Plateau in China. The results suggest that upland and lowland switchgrass have significant differences in the spatial distribution of the adaptation zone and site-specific biomass yield. The area of the adaption zone of upland switchgrass was 4.5 times of the lowland ecotype's. The yield difference between upland and lowland ecotypes ranges from 0 to 34 Mg ha-1. The weighted average yield of the lowland ecotype (20 Mg ha-1) is significantly higher than the upland type (5Mg ha-1). The optimal yield map, generated by comparing the yield of upland and lowland ecotypes based on 1km2 grid locations, illustrates that the total yield potential of the optimal switchgrass is 61.6-106.4Tg on the marginal land of the Loess Plateau, which is approximately twice that of the individual ecotypes. Compared with the existing models, the accuracy of the yield prediction of switchgrass is significantly improved by using the SwitchFor model. This spatially explicit and cultivar-specific model provides valuable information on land management and crop breeding and a robust and extendable framework for yield mapping of other cultivars.

Modeling drought stress impacts under current and future climate for peanut in the semiarid pampas region of Argentina

In the semiarid pampas region of Argentina, peanut ( Arachis hypogaea L.) crop undergoes frequent and unpredictable drought stress periods, with high probability of occurrence under future climate change projections. However, the overall frequency of occurrence and timing of different drought stress patterns under current and future climates has not been well investigated for the main peanut region in central Argentina. The aims of this study were to: (i) define the main peanut drought stress patterns and their occurrence (frequency) during the peanut growing season, via a crop growth modeling approach, (ii) test seed yield stability of the formerly defined (in i) drought stress patterns under future climate scenarios, and (iii) analyze the effect of sowing dates on peanut seed yield as a management strategy to mitigate the impact of future climate scenarios for peanut seed yield in the semiarid pampas region of Argentina. The APSIM-peanut growth model was calibrated and validated for local genotypes and production environments. Our study simulated seasonal drought stress patterns at five representative locations across the region under current and future climate scenarios, and tested six sowing dates as a practice to mitigate the impact of climate change. Clustering analysis identified two contrasting environment types (ETs). The high stress ET showed greater frequency of occurrence (>50%) at the southern locations. Future climate conditions increased the frequency of high stress ET by roughly 6% and reduced peanut seed yield by 12%, as average across locations. For both current and future climates, earlier sowing dates maximized peanut seed yield, regardless the locations. Changing sowing date was not an effective practice to mitigate the negative impact of climate change on peanut seed yield. The defined ETs allowed identifying a target population of environments (TPE) having implications for optimizing peanut breeding and management strategies. The projected increase in the frequency of drought stress for all tested locations provides a challenging scenario for sustaining peanut productivity in this region. • The aims were to determine drought patterns under current and future climate, and to test sowing date as a mitigation tool • The defined drought stress patterns changed the frequency across locations, identifying a target population of environments • ·Future climate increased the frequency of drought stress by ~6% and reduced peanut seed yield by ~12% • Sowing date did not mitigate climate change impact. • This study provides insights to optimize breeding and management, and assists policy makers to address climate change.

Regional modeling of winter wheat yield and water productivity under water-saving irrigation scenarios

Abstract Wheat is the major agricultural crop in Iran. Using reliable tools to estimate wheat grain yield may help the regional planners and stakeholders to employ proper cultivation approaches to increase grain yield in different regions. The process-based crop growth models are the essential analytical tools for representing the main interactions between the environment, crop, resources, and yield production. For these reasons, the crop growth models have been used as an important component in productive farming systems for assessing and improving the crop production at field and regional scales. In this study, the parameterized AquaCrop model was employed to simulate the four major elements that affect the crop growth process, such as soil water content, canopy cover, biomass, and grain yield, of three irrigated winter wheat cultivars. The field experiments were conducted in four locations with different climatic conditions in the south and southwest of Iran during three consecutive growing seasons of 2016–2017, 2017–2018, and 2018–2019. Results showed that AquaCrop had reliable simulation of soil water content with normalized root mean squared error (NRMSE) and Nash–Sutcliffe coefficient (CNS) ranging from 0.05 to 0.15 and 0.69 to 0.85, respectively, for the four study locations. The NRMSE and CNS ranged from 0.10 to 0.21 and 0.83 to 0.97 for canopy cover, respectively. The NRMSE and CNS values were 0.11–0.26 and 0.80–0.97 for biomass, respectively. These results indicate that the parameterized AquaCrop model has high skill in the simulation of soil water content, canopy cover, biomass, and grain yield of different winter wheat cultivars in the vast regions in the southern part of Iran. The model was then used to simulate wheat grain yield and irrigation water productivity (WP) for 324 scenarios, including nine sowing dates, three irrigation levels, and three climatic conditions. AquaCrop showed that it was skillful to simulate grain yield and WP of different irrigated winter wheat cultivars in different regional climatic conditions, deficit irrigation levels, and sowing dates. Overall, the AquaCrop model could be used as a reliable decision-making tool for the field managers and stakeholders.

Vulnerability of crop water footprint in rain-fed and irrigation agricultural production system under future climate scenarios

Agriculture is the sector most sensitive to climate change. It is of great significance to explore the response characteristics of crop water footprint (WF) in rain-fed and irrigation agriculture to climate change for guiding agricultural production. This study coupled and downscaled the meteorological data from 31 global climate models (GCMs) under two Representative Concentration Pathways (RCPs) in China, and used crop growth model AquaCrop in large scale to quantify the changes in yield and WF of maize in China from the 2030s to the 2090s. The Monte Carlo Method was used to calibrate the crop parameters in AquaCrop model, which improved the accuracy and spatiotemporal resolution of the crop model. Results showed that precipitation and temperature would increase in most regions in China, which was more obvious in the RCP8.5 scenario. Meanwhile, climate change resulted in larger crop evapotranspiration (ETc) for maize in the north than south. In the future, the increase of ETc in Southwest China would be relatively larger, while that in Northwest China would decrease. Maize yield would generally increase in most regions except for central China, where illustrated that increases in the north was greater than in the south. In terms of irrigation strategies, full irrigation could mitigate the reduction in maize yield caused by climate change. However, increasing irrigation would have fewer benefits to maize yield in humid regions compared with arid and semiarid regions in China. In addition, increasing irrigation could not greatly reduce the WF of maize in humid regions. Due to different climatic and hydraulic soil characteristics in each region, the effects of climate change and irrigation strategies on WF of maize would vary in different regions. This study provided irrigation strategies guidance to mitigate the adverse effects of climate change on agricultural production and its water use.

Integrating an attention-based deep learning framework and the SAFY-V model for winter wheat yield estimation using time series SAR and optical data

Information on the spatial distribution of yields can be obtained over a large area by using remote sensing (RS) data. Combining Synthetic Aperture Radar (SAR), being sensitive to above ground biomass and soil moisture in all weather conditions, and optical data can improve the usability of RS data and provide a basis for pixel-based crop yield estimation (YE). In this study, an Upscaled Convolutional Gated Recurrent Unit model incorporated an attention mechanism (UpSc-AConvGRU model) was proposed to improve the estimation accuracy of the winter wheat growth parameter, Leaf Area Index (LAI). Gap filling the time series of optical data was done with backscatter coefficients, local incidence angles and polarimetric decomposition information from Sentinel-1 SAR imagery. The time series LAI estimated by the UpSc-AConvGRU model and Vegetation Temperature Condition Index (VTCI) retrieved from Sentinel-3 optical imagery were then used as state variables of the SAFY-V model to estimate winter wheat yield. The results showed that the proposed UpSc-AConvGRU model incorporated the Convolutional Block Attention Module (CBAM) can effectively improve the accuracy of LAI estimation, with RMSEs ranging from 0.413 to 0.699 m2 m2 for LAI estimated within main growth stages (MGSs) of winter wheat. The correlation between estimated LAI and Sentinel-3 retrieved LAI was generally higher at irrigated farmland compared to rain-fed farmland. The estimated LAI was closest to Sentinel-3 retrieved LAI at the green-up and late heading-filling stage of winter wheat, followed by the jointing and early heading-filling stage, and finally the milk maturity stage. There was good agreement between the SAFY-V model estimated and field measured winter wheat yields (R2 = 0.546, RMSE = 0.757 t ha−1), and the estimated yields at the pixel scale in the Guanzhong Plain, PR China were satisfactory. This study combined deep learning and crop growth modeling, proposed a new pixel scale winter wheat YE method.

A Comparison between Variable Deficit Irrigation and Farmers’ Irrigation Practices under Three Fertilization Levels in Cotton Yield (Gossypium hirsutum L.) Using Precision Agriculture, Remote Sensing, Soil Analyses, and Crop Growth Modeling

The major global challenge for the coming decades will be increasing crop production with less water consumption. Precision agriculture (PA) and variable deficit irrigation (VDI) are management strategies that help farmers to improve crop production, fertilizer’s efficiency, and water use efficiency (WUE). The effects of irrigation (IR1 = variable deficit irrigation; IR2 = farmers’ irrigation common practices) under three fertilization (Ft1, Ft2, Ft3) treatments were studied on a cotton yield, on various indicators for more efficient water and fertilizer use, and on plant growth characteristics by applying a number of new agrotechnologies (such as TDR sensors; soil moisture (SM); PA; remote-sensing NDVI (Sentinel-2 satellite sensors); soil hydraulic analyses; geostatistical models; and SM root-zone modelling 2D GIS mapping). The reference evapotranspiration was computed based on the F.A.O. Penman–Monteith method. The crop (ETc) and actual (ETa) evapotranspiration were computed using crop coefficients obtained from the remote-sensing NDVI vegetation index (R2 = 0.9327). A daily soil–water–crop–atmosphere (SWCA) balance model and a depletion model were developed using sensor data (climatic parameters’ sensors, as well as soil and satellite sensors) measurements. The two-way ANOVA statistical analysis results revealed that irrigation (IR1 = best) and fertilization treatments (Ft2 = best) significantly affected the cotton yield, the plant height, the plant stem, the boll weight, the above-ground dry matter, nitrogen and fertilizer efficiency, and WUE. VDI, if applied wisely during critical growth stages, could result in a substantial improvement in the yield (up to +28.664%) and water savings (up to 24.941%), thus raising water productivity (+35.715% up to 42.659%), WUE (from farmers’ 0.421–0.496 kg·m−3 up to a VDI of 0.601–0.685 kg·m−3), nitrogen efficiency (+16.888% up to +22.859%), and N-P-K fertilizer productivity (from farmers’ 16.754–23.769 up to a VDI of 20.583–27.957).

Spatial Rice Yield Estimation Using Multiple Linear Regression Analysis, Semi-Physical Approach and Assimilating SAR Satellite Derived Products with DSSAT Crop Simulation Model

Accurate and consistent information on the area and production of field crops is vital for national and state planning and ensuring food security in India. Satellite-based remote sensing offers a suitable and cost-effective technique for regional- and national-scale crop monitoring. The use of remote sensing data for crop yield estimation has been demonstrated using a semi-physical approach with reasonable success. Assimilating remote sensing data with the DSSAT model and spectral indices-based regression analysis are promising methods for spatially estimating rice crop yields. Rice area and yield in the Cauvery delta zone of Tamil Nadu, India was estimated during samba (August–January) season in the years 2020–2021 using Sentinel 1A Synthetic Aperture Radar satellite data with three different spatial yield estimation methods, namely a spectral indices-based regression analysis, semi-physical approach, and integrating remote products with DSSAT crop growth model. A rice area map was generated for the study area using a rule-based classifier approach utilizing parameterization with a classification accuracy of 94.5% and a kappa score of 0.89. The total classified rice area in Cauvery Delta Region was 379,767 ha, and the Start of Season (SoS) maps for samba season revealed that the major planting period for rice was between 22 September and 9 November in 2020. The study also aimed to identify promising spatial yield estimation techniques for optimal rice yield prediction over large areas. Regression models resulted in rice yields of 3234 to 3905 kg ha−1 with a mean of 3654 kg ha−1. The net primary product was computed using the periodical PAR, fAPAR, Wstress, Tstress, and maximum radiation use efficiency in a semi-physical approach. The resultant rice yields ranged between 2652 and 3438 kg ha−1 with the mean of 3076 kg ha−1. During the integration of remote sensing products with crop growth models, LAI values were extracted from dB images and utilized to simulate rice yields in the range of 3684 to 4012 kg ha−1 with the mean of 3855 kg ha−1. When compared to the semi-physical approach, both integrating remote sensing products with the DSSAT crop growth model and spectral indices-based regression analysis had R2 greater than 0.80, NRMSE of less than 10%, and agreement of more than 90%, indicating that these two approaches could be used for spatial rice yield estimation.

Impacts of droughts on agricultural and ecological systems based on integrated model in shallow groundwater area

Agricultural and ecological developments are increasingly paid attention in light of climate change. It is expected to comprehensively macroscopically control agricultural and ecological developments in shallow groundwater area that ecology is closely related to agricultural development. This study synthesizes multi models, including process-based crop growth model, water status model of shelterbelt, and vegetation-soil-groundwater interactions to identify responses of crops, shelterbelt, and natural vegetation to different level droughts. Obtained results show that the rank of vulnerabilities to droughts is: maize (the most vulnerable crop) > wheat > sunflower > shelterbelt (the least vulnerable vegetation). Under three drought scenarios, the natural vegetation biomass is all in stable states that fluctuations of groundwater salinity and precipitation cannot largely change the natural vegetation biomass. The natural vegetation biomass in equilibrium states is decreased by 4.08 % and 9.19 % respectively when available irrigation water is reduced to 70 % and 50 % levels. The results indicate that controlling and decreasing initial soil salt content before crop growth may be an effective way to avoid the decrease in the yield loss caused by salinization. It is suggested that vulnerable crops and the possible evolution of natural vegetation towards degradation in extreme droughts should be concerned and taken certain measures. This study also implies that optimizing planting structure is a possible strategy to improve adaptabilities to droughts and decrease economic losses. The established model contributes to provide an effective approach to handle large-scale decision-making problems about comprehensively developing agriculture and ecology in changing environments. Obtained results can provide reference strategies for managers to achieve sustainable development.

Use of 3D modeling to refine predictions of canopy light utilization: A comparative study on canopy photosynthesis models with different dimensions.

Canopy photosynthesis integrates leaf functional and structural traits in space and time and correlates positively with yield formation. Many models with different levels of architectural details ranging from zero-dimensional (0D) to three-dimensional (3D) have been developed to simulate canopy light interception and photosynthesis. Based on these models, a crop growth model can be used to assess crop yield in response to genetic improvement, optimized practices, and environmental change. However, to what extent do architectural details influence light interception, photosynthetic production, and grain yield remains unknown. Here, we show that a crop growth model with high-resolution upscaling approach in space reduces the departure of predicted yield from actual yield and refines the simulation of canopy photosynthetic production. We found crop yield predictions decreased by 12.0-48.5% with increasing the resolution of light simulation, suggesting that a crop growth model without architectural details may result in a considerable departure from the actual photosynthetic production. A dramatic difference in light interception and photosynthetic production of canopy between cultivars was captured by the proposed 3D model rather than the 0D, 1D, and 2D models. Furthermore, we found that the overestimation of crop yield by the 0D model is caused by the overestimation of canopy photosynthetically active radiation (PAR) interception and the RUE and that by the 1D and 2D model is caused by the overestimated canopy photosynthesis rate that is possibly related to higher predicted PAR and fraction of sunlit leaves. Overall, this study confirms the necessity of taking detailed architecture traits into consideration when evaluating the strategies of genetic improvement and canopy configuration in improving crop yield by crop modeling.

Novel models for simulating maize growth based on thermal time and photothermal units: Applications under various mulching practices

Maize (Zea mays L.) is one of the three major food crops and an important source of carbohydrates for maintaining food security around the world. Plant height (H), stem diameter (SD), leaf area index (LAI) and dry matter (DM) are important growth parameters that influence maize production. However, the combined effect of temperature and light on maize growth is rarely considered in crop growth models. Ten maize growth models based on the modified logistic growth equation (Mlog) and the Mitscherlich growth equation (Mit) were proposed to simulate the H, SD, LAI and DM of maize under different mulching practices based on experimental data from 2015–2018. Either the accumulative growing degree-days (AGDD), helio thermal units (HTU), photothermal units (PTU) or photoperiod thermal units (PPTU, first proposed here) was used as a single driving factor in the models; or AGDD was combined with either accumulative actual solar hours (ASS), accumulative photoperiod response (APR, first proposed here) or accumulative maximum possible sunshine hours (ADL) as the dual driving factors in the models. The model performances were evaluated using seven statistical indicators and a global performance index. The results showed that the three mulching practices significantly increased the maize growth rates and the maximum values of the growth curves compared with non-mulching. Among the four single factor-driven models, the overall performance of the MlogPTU Model was the best, followed by the MlogAGDD Model. The MlogPPTU Model was better than the MlogAGDD Model in simulating SD and LAI. Among the 10 models, the overall performance of the MlogAGDD–APR Model was the best, followed by the MlogAGDD–ASS Model. Specifically, the MlogAGDD–APR Model performed the best in simulating H and LAI, while the MlogAGDD–ADL and MlogAGDD–ASS models performed the best in simulating SD and DM, respectively. In conclusion, the modified logistic growth equations with AGDD and either APR, ASS or ADL as the dual driving factors outperformed the commonly used modified logistic growth model with AGDD as a single driving factor in simulating maize growth.