Determination of Upland Rice Cultivar Coefficient Specific Parameters for DSSAT (Version 4.7)-CERES-Rice Crop Simulation Model and Evaluation of the Crop Model under Different Temperature Treatments conditions

To develop basis for strategic or arranged decision making towards crop yield improvement in Thailand, a new method in which crop models could be used is essential. Therefore, the objective of this study was to measure cultivar specific parameters by using DSSAT (v4.7) Cropping Simulation Model (CSM) with five upland rice genotypes namely Dawk Pa-yawm, Mai Tahk, Bow Leb Nahng, Dawk Kha 50 and Dawk Kahm. Experiment was laid out in a Completely Randomized Design (CRD) with split plot design. Results showed that five upland rice genotypes had significantly affected each other by different temperature treatments (28°C, 30°C, 32°C) with grain yield, tops weight, harvest index, flowering, and maturity date. At the same time, all the phenological traits had highly significant variation with the genotypes. The cultivar specific parameters obtained by using a temperature tolerant cultivar (Basmati 385) with five upland genotypes involved in the DSSAT4.7-CSM. Model evaluation results indicated that utilizing the estimated cultivar coefficient parameters, model simulated well with varying temperature treatments as indicated by the agreement index (d-statistic) closer to unity. Hence, it was estimated that model calibration and evaluation was realistic in the limits of test cropping seasons and that CSM fitted with cultivar specific parameters can be used in simulation studies for investigation, farm managing or decision making. This electronic document is a “live” template. The various components of your paper [title, text, heads, etc.] are already defined on the style sheet, as illustrated by the portions given in this document.

A simulation of winter wheat crop responses to irrigation management using CERES-Wheat model in the North China Plain

To improve efficiency in the use of water resources in water-limited environments such as the North China Plain（NCP）, where winter wheat is a major and groundwater-consuming crop, the application of water-saving irrigation strategies must be considered as a method for the sustainable development of water resources. The initial objective of this study was to evaluate and validate the ability of the CERES-Wheat model simulation to predict the winter wheat grain yield, biomass yield and water use efficiency（WUE） responses to different irrigation management methods in the NCP. The results from evaluation and validation analyses were compared to observed data from 8 field experiments, and the results indicated that the model can accurately predict these parameters. The modified CERES-Wheat model was then used to simulate the development and growth of winter wheat under different irrigation treatments ranging from rainfed to four irrigation applications（full irrigation） using historical weather data from crop seasons over 33 years（1981–2014）. The data were classified into three types according to seasonal precipitation： 〈100 mm, 100–140 mm, and 〉140 mm. Our results showed that the grain and biomass yield, harvest index（HI） and WUE responses to irrigation management were influenced by precipitation among years, whereby yield increased with higher precipitation. Scenario simulation analysis also showed that two irrigation applications of 75 mm each at the jointing stage and anthesis stage（T3） resulted in the highest grain yield and WUE among the irrigation treatments. Meanwhile, productivity in this treatment remained stable through different precipitation levels among years. One irrigation at the jointing stage（T1） improved grain yield compared to the rainfed treatment and resulted in yield values near those of T3, especially when precipitation was higher. These results indicate that T3 is the most suitable irrigation strategy under variable precipitation regimes for stable yield of winter wheat with maximum water savings in the NCP. The application of one irrigation at the jointing stage may also serve as an alternative irrigation strategy for further reducing irrigation for sustainable water resources management in this area.

DYNAMIC MODEL OF CROP GROWTH SYSTEM AND NUMERICAL SIMULATION OF CROP GROWTH PROCESS UNDER THE MULTI-ENVIRONMENT EXTERNAL FORCE ACTION

According to the biomechanic theory and method, the dynamic mechanism of crop growth under the external force action of multi_environment factors (light, temperature,soil and nutrients etc.) was comprehensively explored.Continuous_time Markov(CTM) approach was adopted to build the dynamic model of the crop growth system and the simulated numerical method. The growth rate responses to the variation of the external force and the change of biomass saturation value were studied. The crop grew in the semiarid area was taken as an example to carry out the numerical simulation analysis, therefore the results provide the quantity basis for the field management. Comparing the dynamic model with the other plant growth model, the superiority of the former is that it displays multi_dimension of resource utilization by means of combining macroscopic with microcosmic and reveals the process of resource transition. The simulation method of crop growth system is advanced and manipulated. A real simulation result is well identical with field observational results.

Studies on crop growth modelling and simulation models in China

There is a close relationship between agricultural production and environmental meteorological conditions. In the study of the correlation between them, the simulation models are paid more attention to the crop growth. In this paper the development of the studies on the crop growth dynamic simulation model in China is briefly reviewed. The relationships between meteorological conditions and each process of crop growth (such as photosynthesis, respiration, accumulation and distribution of assimilation products and growth of leaf area) are studied and simulated basing on the results from field experiments. Preliminary models for rice, wheat, maize and soybean have been developed, and some investigations about modelling methods, procedures and parameters in simulation models are made.

Quantifying Responses of Winter Wheat Physiological Processes to Soil Water Stress for Use in Growth Simulation Modeling

A deep understanding of crop-water eco-physiological relations is the basis for quantifying plant physiological responses to soil water stress. Pot experiments were conducted to investigate the winter wheat crop-water relations under both drought and waterlogging conditions in two sequential growing seasons from 2000 to 2002, and then the data were used to develop and validate models simulating the responses of winter wheat growth to drought and waterlogging stress. The experiment consisted of four treatments, waterlogging (keep 1 to 2 cm water layer depth above soil surface), control (70%-80% field capacity), light drought (40%-50% field capacity) and severe drought (30%-40% field capacity) with six replicates at five stages in the 2000-2001 growth season. Three soil water content treatments (waterlogging, control and drought) with two replicates were designed in the 2001-2002 growth season. Waterlogging and control treatments are the same as in the 2000-2001 growth season. For the drought treatment, no water was supplied and the soil moisture decreased from field capacity to wilting point. Leaf net photosynthetic rate, transpiration rate, predawn leaf water potential, soil water potential, soil water content and dry matter weight of individual organs were measured. Based on crop-water eco-physiological relations, drought and waterlogging stress factors for winter wheat growth simulation model were put forward. Drought stress factors integrated soil water availability, the sensitivity of different development stages and the difference between physiological processes (such as photosynthesis, transpiration and partitioning). The quantification of waterlogging stress factor considered different crop species, soil water status, waterlogging days and sensitivity at different growth stages. Data sets from the pot experiments revealed favorable performance reliability for the simulation sub-models with the drought and waterlogging stress factors.

Growth simulation and yield prediction for perennial jujube fruit tree by integrating age into the WOFOST model

Mathematical models have been widely employed for the simulation of growth dynamics of annual crops,thereby performing yield prediction,but not for fruit tree species such as jujube tree(Zizyphus jujuba).The objectives of this study were to investigate the potential use of a modified WOFOST model for predicting jujube yield by introducing tree age as a key parameter.The model was established using data collected from dedicated field experiments performed in 2016-2018.Simulated growth dynamics of dry weights of leaves,stems,fruits,total biomass and leaf area index(LAI) agreed well with measured values,showing root mean square error(RMSE) values of 0.143,0.333,0.366,0.624 t ha^-1 and 0.19,and R2 values of 0.947,0.976,0.985,0.986 and 0.95,respectively.Simulated phenological development stages for emergence,anthesis and maturity were 2,3 and 3 days earlier than the observed values,respectively.In addition,in order to predict the yields of trees with different ages,the weight of new organs(initial buds and roots) in each growing season was introduced as the initial total dry weight(TDWI),which was calculated as averaged,fitted and optimized values of trees with the same age.The results showed the evolution of the simulated LAI and yields profiled in response to the changes in TDWI.The modelling performance was significantly improved when it considered TDWI integrated with tree age,showing good global(R2≥0.856,RMSE≤0.68 t ha^-1) and local accuracies(mean R2≥0.43,RMSE≤0.70 t ha^-1).Furthermore,the optimized TDWI exhibited the highest precision,with globally validated R2 of 0.891 and RMSE of 0.591 t ha^-1,and local mean R2 of 0.57 and RMSE of 0.66 t ha^-1,respectively.The proposed model was not only verified with the confidence to accurately predict yields of jujube,but it can also provide a fundamental strategy for simulating the growth of other fruit trees.

Simulation and Validation of Rice Potential Growth Process in Zhejiang Province of China by Utilizing WOFOST Model

A crop growth model of WOFOST was calibrated and validated through rice field experiments from 2001 to 2004 in Jinhua and Hangzhou, Zhejiang Province. For late rice variety Xiushui 11 and hybrid Xieyou 46, the model was calibrated to obtain parameter values using the experimental data of years 2001 and 2002, then the parameters were validated by the data obtained during 2003. For single hybrid rice Liangyoupeijiu, the data recorded in 2004 and 2003 were used for calibration and validation, respectively. The main focus of the study was as follows： the WOFOST model is good in simulating rice potential growth in Zhejiang and can be used to analyze the process of rice growth and yield potential. The potential yield obtained from the WOFOST model was about 8100 kg/ha for late rice and 9300 kg/ha for single rice. The current average yield in Jinhua is only about 78% （late rice） and 70% （single rice） of their potential yield. The results of the simulation also showed that the currant practice of management at the middle and late growth stages of rice should be reexamined and improved to reach optimal rice growth.

Linking a farmer crop selection model(FCS) with an agronomic model(EPIC) to simulate cropping pattern in Northeast China

In this paper, authors established a farmer crop selection model（FCS） for the three provinces of Liaoning, Jilin and Heilongjiang of the Northeast China. With linking to the environmental policy integrated climate model（EPIC）, the simulated results of FCS model for maize, rice and soybean were spatialized with 1 km×1 km grids to obtain cropping pattern. The reference map of spatial distribution for the three staple crops acquired by remote sensing imageries was applied to validate the simulated cropping pattern. The results showed that（1） the total simulation accuracy for the study area was 78.62%, which proved simulation method was applicable and feasible;（2） simulation accuracy for Jilin Province was the highest among the three provinces with a rate of 82.45% since its simple cropping system and not complex topography;（3） simulation accuracy for maize was the best among the three staple crops with a ratio of 81.14% because the study area is very suitable for maize growth. We hope this study could provide the reference for cropping pattern forecasting and decision-making.

Using CropSyst to Simulate Spring Wheat Growth in Black Soil Zone of Northeast China

Available water and fertilizer have been the main limiting factors for yields of spring wheat, which occupies a large area of the black soil zone in northeast China; thus, the need to set up appropriate models for scenario analysis of cropping system models has been increasing. The capability of CropSyst, a cropping system simulation model, to simulate spring wheat growth of a widely grown spring cultivar, 'Longmai 19', in the black soil zone in northeast China under different water and nitrogen regimes was evaluated. Field data collected from a rotation experiment of three growing seasons (1992-1994) were used to calibrate and validate the model. The model was run for 3 years by providing initial conditions at the beginning of the rotation without reinitializing the model in later years in the rotation sequence. Crop input parameters were set based on measured data or taken from CropSyst manual. A few cultivar-specific parameters were adjusted within a reasonable range of fluctuation. The results demonstrated the robustness of CropSyst for simulating evapotranspiration, aboveground biomass, and grain yield of 'Longmai 19' spring wheat with the root mean square errors being 7%, 13% and 13% of the observed means for evapotranspiration (ET), grain yield and aboveground biomass, respectively. Although CropSyst was able to simulate spring production reasonably well, further evaluation and improvement of the model with a more detailed field database was desirable for agricultural systems in northeast China.

Simulating deforestation of Nepal by area production model

Based on the growth rates of population, Gross Domestic Products (GDP) and agriculture productivity, the areas of deforestation were predicted in Jutp ani village, Chitwan district, Nepal by Area Production Model (APM). Through the APM simulation in this study, all of forestland will be transferred into agricu ltural land in 2030 at the rate of 24% per year on the current productivity. And if the productivity of subsistence food crop is assumed to increase at the rate of 1%, the productivity of market crop and export crop increase at the rate of 2% annually, deforestation rate will decrease to 17% per year, but only 124 hm2 forest land will be left till 2038. The agriculture productivity is a very impor tant factor for the deforestation, so intensification of agriculture management is more important.

Dynamic modeling of mineral contents in greenhouse tomato crop

Tomato is one the most important vegetables worldwide and mineral nutrition in tomato crops is considered as the second most important factor in crop management after water availability. Mathematical modeling techniques allow us to design strategies for nutrition management. In order to generate the necessary information to validate and calibrate a dynamic growth model, two tomato crop cycles were developed. Several mineral analyses were performed during crop development to determine the behavior of N, P, K, Ca, Mg and S in different organs of the plant. Regression models were generated to mimic the behavior of minerals in tomato plants and they were included in the model in order to simulate their dynamic behavior. The results of this experiments showed that the growth model adequately simulates leaf and fruit weight (EF > 0.95 and Index > 0.95). As for harvested fruits and harvested leaves, the simulation was less efficient (EF < 0.90 and Index < 0.90). Simulation of minerals was suitable for N, P, K and S as both, the EF and the Index, had higher values than 0.95. In the case of Ca and Mg, simulations showed indices below 0.90. These models can be used for planning crop management and to design more appropriate fertilization strategies.

CropSyst作物模型在松嫩平原典型黑土区的校正和验证

通过对CropSyst作物模拟模型进行修订和验证,应用该模型对松嫩平原黑土区主要作物的生产潜力进行了模拟,并对作物生产力模拟的有效方法进行了初步探索。模拟结果表明,对于主要作物的经济产量、全生育期蒸散量、收获时的地上生物量,模拟值与实测值较为接近。模拟值和实测值的均方根误差RMSE为3.59%(小麦地上生物量)～8.02%(小麦产量),模拟性能指数EF最小为0.76(玉米蒸散量),最大为0.90(小麦产量)。

AquaCrop作物模型应用研究进展

Aqua Crop是FAO于2009年研发的一款新型作物模型,它以输入参数少、界面简单等优点被广泛应用于生产实践中。论文基于Aqua Crop模型原理和特点,深入探讨了Aqua Crop模型国内外应用研究进展。当前,Aqua Crop模型在灌溉策略、气候变化下的情景模拟以及与其他模型联合应用等方面取得了显著进展。但是,该模型在应用过程中还存在若干缺陷。一是模型在保守参数缺少验证的情况下,会使得模拟精度不稳定;二是由于土壤空间变异性的客观存在,模型在由点位向面上扩展时应用效果不佳;三是当前对雨养区作物生长模拟研究还很少,且其非保守参数难以准确确定;四是目前该模型生理、养分和水养互作模块尚不够完善,未考虑作物病虫害和品种遗传差异,当作物生长遭受水分、盐分或温度等严重胁迫时会导致模拟精度下降。今后在模型应用时,可利用多年数据对保守参数进行校正,将区域同一站点多年数据和多站点相关数据相结合调试模型非保守参数;其次,应加强雨养地区模拟研究,从而扩大模型应用范围。开发者应进一步完善Aqua Crop模型子模块,为提高模拟精度和拓宽应用范围提供支撑。

Spatial-time continuous changes simulation of crop growth parameters with multi-source remote sensing data and crop growth model

本文将遥感信息与作物模型同化实现作物生长参数的时空域连续模拟,进而监测生长参数的时空域变化。首先将作物模型WOFOST(World food studies)与冠层辐射传输模型PROSAIL耦合构建WOPROSAIL模型,利用微粒群算法(PSO)通过最小化从CCD数据获取的土壤调节植被指数观测值SAVI(soil adjusted vegetation index)与耦合模型得到的模拟值SAVI’之间差值优化作物模型初始参数。通过MODIS数据反演实现参数的区域化,并将区域参数作为优化后作物模型输入参数驱动模型逐像元计算生长参数,实现生长参数的时空域连续模拟与监测,最终建立区域尺度遥感-作物模拟同化框架模型RS-WOPROSAIL。结果表明:同化模型解决了作物模型模拟空间域和遥感信息时间域的不连续问题。模型模拟的叶面积指数(LAI)、穗重(WSO)、地上总生物量(TAGP)等生长参数较好地体现了水稻生长状况时空域变化,研究区水稻模拟产量与实际产量的误差为27.4%。

应用ARID CROP模型对中国黄淮海地区冬小麦气候生产力的数值模拟研究

应用ＡＲＩＤＣＲＯＰ模型对黄淮海地区冬小麦气候生产力进行了数值模拟研究，给出了该地区冬小麦气候生产力Ｙｑ分布图，继而研究了水分保持最适状况时的光温生产力Ｙｗ分布状况，在此基础上给出了水分增产力Ｑ（Ｑ＝（Ｙｗ－Ｙｑ）／Ｙｑ）分布图。研究表明，黄淮海地区冬小麦气候生产力变幅在３７５０～９７５０ｋｇ／ｈｍ２之间，总的趋势北低南高，黑龙港地区出现了一个３７５０ｋｇ／ｈｍ２的低值区。水分是黄淮海北部地区冬小麦气候生产力的一个重要限制因子，当水分完全适宜时，南部淮河流域冬小麦气候生产力仅可提高５％～１０％，而黄淮海北部地区气候生产力则可提高７５％～１００％。用黄淮海地区冬小麦高产记录与生产力模拟值进行了对比分析，表明用ＡＲＩＤＣＲＯＰ模型对该地区冬小麦气候生产力进行研究是可行的，该研究为引黄灌溉提供了一定的理论依据。

Crop Yield Prediction Using Machine Learning Approaches on a Wide Spectrum

The exponential growth of population in developing countries likeIndia should focus on innovative technologies in the Agricultural processto meet the future crisis. One of the vital tasks is the crop yield predictionat its early stage;because it forms one of the most challenging tasks inprecision agriculture as it demands a deep understanding of the growth patternwith the highly nonlinear parameters. Environmental parameters like rainfall,temperature, humidity, and management practices like fertilizers, pesticides,irrigation are very dynamic in approach and vary from field to field. In theproposed work, the data were collected from paddy fields of 28 districts in widespectrum of Tamilnadu over a period of 18 years. The Statistical model MultiLinear Regression was used as a benchmark for crop yield prediction, whichyielded an accuracy of 82% owing to its wide ranging input data. Therefore,machine learning models are developed to obtain improved accuracy, namelyBack Propagation Neural Network (BPNN), Support Vector Machine, andGeneral Regression Neural Networks with the given data set. Results showthat GRNN has greater accuracy of 97% (R2 = 0.97) with a normalizedmean square error (NMSE) of 0.03. Hence GRNN can be used for crop yieldprediction in diversified geographical fields.

Modelling Impacts of Climate Change on Maize (<i>Zea mays</i>L.) Growth and Productivity: A Review of Models, Outputs and Limitations

The use of crop modelling in various cropping systems and environments to project and upscale agronomic decision-making under the facets of climate change has gained currency in recent years. This paper provides an evaluation of crop models that have been used by researchers to simulate maize growth and productivity. Through a systematic review approach, a comprehensive assessment of 186 published articles was carried out to establish the models and parameterization features, simulated impacts on maize yields and adaptation strategies in the last three decades. Of the 23 models identified, CERES-maize and APSIM models were the most dominant, representing 49.7% of the studies undertaken between 1990 and 2018. Current research shows projected decline in maize yields of between 8% - 38% under RCP4.5 and RCP8.5 scenarios by the end of the 21st century, and that adaptation is essential in alleviating the impacts of climate change. Major agro-adaptation options considered in most papers are changes in planting dates, cultivars and crop water management practices. The use of multiple crop models and multi-model ensembles from general circulation models (GCMs) is recommended. As interest in crop modelling grows, future work should focus more on suitability of agricultural lands for maize production under climate scenarios.

Research in Agrometeorolgy on Fodder Crops in Central India—An Overview

Livestock rearing is one of the major occupations in India and is making significant contribution to the country GDP. The regional and seasonal variations in the teperature and rainfall distribution have been the major factors influencing the economy of a region. It is a matter of serious concern that out of 11 districts of central India, 9 districts are showing increasing trend in maximum temperature with a rate of 0.01°C to 0.15°C/year. A significant long-term decreasing trend (Slope = -4.26) was found in annual rainfall series at Jhansi. At Jhansi, moderate to severe drought occurs once in five years. But in the last decade, 7 years experienced moderate to disastrous drought in Jhansi region, wherein rainfall deficiency ranged between 40% and 60% from normal value. Of special mention was the year, 2006, which experienced a worst drought ever recorded for this region. Studies related to crop simulation model was carried out for fodder sorghum and its application for agronomic management and assessing the impact of climate change. Crop modeling studies on forage sorghum (C4) and cowpea (C3) showed increased dry matter biomass by 3% in sorghum but more prominent in cowpea by 46% under elevated CO2 from 330 ppm to 770 ppm. The interaction study of enhanced CO2 and temperature showed prominent negative impact on yields of both the crops. Evapotranspiration and crop coefficient (Kc) of several fodder crops i.e. berseem, lucerne, oat, sorghum, teosinte, maize + cowpea, guinea + berseem were worked out. In berseem, the highest Kc (1.81) was found during 2nd cutting followed by 3rd and 4th cuts. Estimates on irrigation scheduling for the guinea grass + berseem showed that the cropping system requires 7 irrigations at an interval ranging from 13 to 30 days to fulfill the 567.6 mm of water per season as net irrigation under mar soil (black) type whose actual water holding capacity (AWHC) is 175 mm. Similarly, if the cropping system is grown under kabar (AHWC = 140 mm) soil, then it requires nine irrigation with a total water requirement of 591.5 mm at an interval ranging from 10 to 24 days. For integrated pest management (IPM) scheme of lucerne, degree day based model was developed to monitor the lucerne weevil population in central region.

不同矿化度水源膜下滴灌棉田土壤水盐动态和作物生长模拟

为构建适用于干旱区膜下滴灌条件的土壤水盐动态分布和棉花生长模型,基于2020—2021年的田间试验,经过对SWAP模型的土壤、土壤水力功能和作物生长等模块进行率定和验证,对灌溉水矿化度为1、2、3、4、5、6 g·L^(-1)时的土壤水盐分布特征、作物生长过程和干物质累积分配进行数值模拟。结果表明:(1)土壤含水率与土壤含盐量的模拟精度以20~100 cm土层较好,0~20 cm土层模拟精度较差,其中土壤含水量的模拟效果优于土壤含盐量;随着灌溉水源矿化度的增加,土壤含水率和含盐量的模拟误差逐渐变小。(2)不同矿化度水源膜下滴灌棉花叶面积指数模拟效果较好(R2=90.72%,RMSE=0.35 cm^(2)·cm^(-2),NRMSE=8.73%,IOA=0.98)。(3)不同矿化度水源膜下滴灌棉花茎干物质累积量模拟效果较好(R2=89.08%,RMSE=6.12 g,NRMSE=23.16%,IOA=0.96)。研究结果表明,SWAP模型可以较好地对不同矿化度水源膜下滴灌的土壤水盐动态分布和棉花生长过程进行模拟。

基于随机森林的作物模型光温产量潜力模拟优化方法

为有效降低作物模拟所需数据量,提高计算效率,基于机器学习建立冬小麦光温产量潜力估算模型。以中国冬麦区129个农业气象站点1980—2009年光温产量潜力为研究对象,选择影响光温产量潜力模拟较大的温度、日照时数、经纬度等构建特征变量。选择生长季与月份2个时间范围,基于WheatGrow模型输入输出数据建立生长季变量的随机森林模型(RF_GS)与月份变量的随机森林模型(RF_Mon),最后利用均方根误差(RMSE)评价随机森林模型的性能。结果表明,随机森林模型可在保证模拟精度的前提下降低数据需求量,且RF_GS精度优于RF_Mon;变量重要性检验与部分依赖图分析结果表明,纬度、生长季日照时数、5月日照时数、3月最低温度对光温产量潜力模拟影响较大;若模型验证数据的范围超出训练数据的范围,利用随机森林模型无法保证建模精度。