Evaluation of CRU-JRA gridded meteorological dataset for modeling of wheat production systems in Iran.

Meteorological variables are essential inputs for agricultural simulation models and the lack of measured data is a big challenge for the application of these models in many agricultural zones. Studies indicated that gridded meteorological datasets can be proper replacements for measured data. This paper aimed to examine a new gridded meteorological dataset namely CRU-JRA for crop modeling intents. The CRU-JRA is a 6-hourly dataset with a spatial resolution of 0.5° × 0.5° that was primarily constructed for modeling purposes. The CERES-Wheat model in the Decision Support System for Agrotechnology Transfer (DSSAT) was used for the simulation of irrigated and rainfed wheat production systems in Iran. Results showed that the CRU-JRA maximum and minimum temperature values had a relatively fine accuracy with a normalized root mean square error (NRMSE) of 14% for the simulated grain yield. The performance of the CRU-JRA solar radiation values for the simulation of grain yield was similar with a NRMSE of 14.4%. The weakest performance was found for the CRU-JRA precipitation values with a NRMSE of 18.9%. Overall, the CRU-JRA dataset performed comparatively acceptable and similar to existing gridded meteorological datasets for crop modeling purposes in the study area, however further calibrations can improve the accuracy of the next versions of this dataset. More research is necessary for the investigation of the CRU-JRA dataset for agricultural modeling purposes across diverse climates.

Effects of climate change and deep fertilization on the growth and yield of winter wheat in the Loess Plateau of China.

BACKGROUND: Global temperature is projected to rise continuously under climate change, negatively impacting the growth and yield of winter wheat. Optimizing traditional agricultural measures is necessary to mitigate potential winter wheat yield losses caused by future climate change. This study aims to explore the variations in winter wheat growth and yield on the Loess Plateau of China under future climate change, identify the key meteorological factors affecting winter wheat growth and yield, and analyze the differences in winter wheat yield and root characteristics under different fertilization depths. RESULTS: Meteorological data from 20 General Circulation Models were applied to drive the Decision Support System for Agrotechnology Transfer model, simulating the future growth characteristics of winter wheat under various fertilization depths. The Random Forest model was used to determine the relative importance of meteorological factors influencing winter wheat yield, root length density and leaf area index. The results showed that temperature and high emission concentration were primary factors influencing crop yield under future climate change. The temperature increase projected from 2021 to 2100 would be anticipated to shorten the phenology period of winter wheat by 2-16 days and reduce grain yield by 2.9-12.7% compared to the period from 1981 to 2020. Conversely, the root length density and root weight of winter wheat would increase by 1.2-10.9% and 0.2-24.1%, respectively, in the future, and excessive allocation of root system resources was identified as a key factor contributing to the reduction in winter wheat yield. Compared with the shallow fertilization treatment (N5), the deep fertilization treatments (N15 and N25) increased the proportion of roots in the deep soil layer (30-60 cm) by 2.7-10.2%. Because of the improvement in root structure, the decline in winter wheat yield under deep fertilization treatments in the future is expected to be reduced by 1.2% to 6.5%, whereas water use efficiency increases by 1.1% to 2.4% compared to the shallow fertilization treatment. CONCLUSION: The deep fertilization treatment can enhance the root structure of winter wheat and increase the proportion of roots in the deep soil layer, thereby effectively mitigating the decline in winter wheat yield under future climate change. Overall, optimizing fertilization depth effectively addresses the reduced winter wheat yield risks and agricultural production challenges under future climate change. © 2024 Society of Chemical Industry.

CRISPR-Cas9 screening identified lethal genes enriched in necroptosis pathway and of prognosis significance in osteosarcoma.

BACKGROUND: The present study aimed to identify indispensable genes associated with tumor cell viability according to the clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) datasets, which may support new therapeutic targets for patients with osteosarcoma. METHODS: The transcriptome patterns between tumor and normal tissues, which were obtained from the Therapeutically Applicable Research to Generate Effective Treatments dataset, were overlapped with the genomics associated with cell viability screened by CRISPR-Cas9 technology. Kyoto Encyclopedia of Genes and Genomes and Gene Ontology analyses were employed to determine enrichment pathways related to lethal genes. Least absolute shrinkage and selection operator (LASSO) regression was employed to construct a risk model related to lethal genes for predicting clinical outcomes of osteosarcoma. Univariate and multivariate Cox regressions were conducted to assess the prognostic value of this feature. Weighted gene co-expression network analysis was performed to identify modules associated with patients with high-risk score. RESULTS: In total, 34 lethal genes were identified in this investigation. These genes were enriched in the necroptosis pathway. The risk model based on LASSO regression algorithm distinguishes patients with high-risk score from patients with low-risk score. Compared with low-risk patients, high-risk patients showed a shorter overall survival rate in both the training and validation sets. The time-dependent receiver operating characteristic curves of 1, 3 and 5 years displayed that the risk score has great prediction performance. The necroptosis pathway represents the main difference in biological behavior between the high-risk group and the low-risk group. Meanwhile, CDK6 and SMARCB1 may serve as important targets for detecting osteosarcoma progression. CONCLUSIONS: The present study developed a predictive model that outperformed classical clinicopathological parameters for predicting the clinical outcomes of osteosarcoma patients and identified specific lethal genes, including CDK6 and SMARCB1, as well as the necroptosis pathway. These findings may serve as potential targets for future osteosarcoma treatments.

Modelling maize yield, soil nitrogen balance and organic carbon changes under long-term fertilization in Northeast China.

Optimized fertilization is an effective strategy for improving nitrogen (N) use efficiency and maintaining high crop yield, but its long-term impacts on soil organic carbon (C) and inorganic N dynamics remain unclear. The objectives of this study were to 1) explore the economic optimum N rate and evaluate the DSSAT CERES-Maize model using the measurements from three 3-year maize (Zea mays L.) field experiments, in Gongzhuling and Yushu County, Northeast China, and 2) assess the long-term impacts of farmers' N rate (N250), optimum N rate (N180) and organic-inorganic combined N rate (MN180) on maize yields, soil N and C changes from 1985 to 2020. Results showed that similar maize yields of 8000-11,000 kg ha-1 were achieved under the average economic optimum N rate of 170 kg N ha-1 relative to N250 in both counties. Good agreements were observed between the simulated and measured maize yield, above-ground biomass, N uptake and soil nitrate (NO3--N). Long-term simulation confirmed that N180 and MN180 can achieve the same yield as N250 in both counties. The lowest annual soil inorganic N balance, NO3--N leaching, and nitrous oxide (N2O) and ammonia (NH3) emissions were achieved under MN180, followed by N180 in both sites. Higher NO3--N leaching was found in sandy clay loam soil than silt clay loam and clay loam soils. Average soil organic C (SOC, 0-0.2 m) increased from 1.3 to 2.4% in Gongzhuling and from 2.2 to 2.4% in Yushu under MN180 during the 35-year period, but it showed declining trends under N180 and N250. We concluded that the economic optimum N rate could be an option to replace current farmers' N rate for the continuous maize. Substitution of inorganic fertilizer by 20-30% manure under the optimum N rate showed advantage on maintaining high yield, reducing soil inorganic N losses as well as increasing SOC stock for sustainable agriculture.

Modelling the climate change impact of mitigation (RCP 2.6) and high emission (RCP 8.5) scenarios on maize yield and possible adaptation measures in different agroclimatic zones of Punjab, India.

BACKGROUND: A simulation study was performed for assessing climate change impact on maize under Representative Concentration Pathways (RCPs 2.6 and 8.5) for Punjab, India. The study area comprised five agroclimatic zones (AZs) including seven locations. The bias corrected temperature and rainfall data from four models (CSIRO-Mk-3-6-0, FIO-ESM, IPSL-CM5A-MR and Ensemble) were used as input in CERES-Maize model which was run with constant management practices for two Punjab maize hybrids (PMH 1 and PMH 2). The maize yield for upcoming 70 years (2025-2095) was simulated and its deviations from the baseline (2010-2021) yield were computed under optimized sowing (early-May to early-July) and current sowing (end-May to end-June) period. RESULTS: With current sowing dates, the maize yield declined under both RCP 2.6 and RCP 8.5 scenarios, respectively in all the AZs, that is, by 4-23% and 60-80% in AZ II, by 5-60% and 60-90% in AZ III, by 9-30% and 50-90% in AZ IV and by 13-40% and 30-90% in AZ V. Though yield decline was lesser under RCP 2.6 as compared to RCP 8.5, but still it indicates that adaptive strategy such as shifting of sowing dates may be helpful in stabilizing the maize yield. CONCLUSION: The results for iterative combinations of sowing period revealed that early June sowing in AZ II for both the hybrids, mid- to end-June (Ludhiana and Amritsar) and end-May to mid-June (Patiala) sowings for PMH 1 were able to nullify the negative impact of climate change. Maize cultivation in AZ IV and AZ V would not be a suitable venture for farmers of the region. © 2023 Society of Chemical Industry.

High overexpression of CERES, a plant regulator of translation, induces different phenotypical defence responses during TuMV infection.

Mutations in the eukaryotic translation initiation factors eIF4E and eIF(iso)4E confer potyvirus resistance in a range of plant hosts. This supports the notion that, in addition to their role in translation of cellular mRNAs, eIF4E isoforms are also essential for the potyvirus cycle. CERES is a plant eIF4E- and eIF(iso)4E-binding protein that, through its binding to the eIF4Es, modulates translation initiation; however, its possible role in potyvirus resistance is unknown. In this article, we analyse if the ectopic expression of AtCERES is able to interfere with turnip mosaic virus replication in plants. Our results demonstrate that, during infection, the ectopic expression of CERES in Nicotiana benthamiana promotes the development of a mosaic phenotype when it is accumulated to moderate levels, but induces veinal necrosis when it is accumulated to higher levels. This necrotic process resembles a hypersensitive response (HR)-like response that occurs with different HR hallmarks. Remarkably, Arabidopsis plants inoculated with a virus clone that promotes high expression of CERES do not show signs of infection. These final phenotypical outcomes are independent of the capacity of CERES to bind to eIF4E. All these data suggest that CERES, most likely due to its leucine-rich repeat nature, could act as a resistance protein, able to promote a range of different defence responses when it is highly overexpressed from viral constructs.

Effect of Temporal Sampling Interval on the Irradiance for Moon-Based Wide Field-of-View Radiometer.

Moon-based Earth radiation observation can provide longer-term, continuous multi-angle measurements for the Earth's outward radiative flux. In addition, the large distance between the Moon and Earth means that the radiation can be monitored by a non-scanning Moon-based Wide Field-of-View (MWFOV) radiometer considering the Earth as one pixel. In order to parameterize the radiometer, studying the effect of the temporal sampling interval on irradiance is of great importance. In this work, based on radiation transfer model, simulated irradiance time series from March 2000 to December 2020 were analyzed. Then, we used them to reveal the effects of the sampling interval on irradiance. The results show that the measurements of the MWFOV radiometer can reveal the variation of irradiance on hourly, daily and monthly time scales, and the high-frequency measurements can reflect the variation of scene types in the MWFOV-viewed area. In order to obtain more meaningful measurements, the radiation resolution of the MWFOV radiometer should be better than 0.5mW∙m-2 with an accuracy of 1% or better in the future actual design, and the sampling interval should be less than 1 h, which can ensure that 97% of the surface area can be sampled more than nine times per day for longwave radiation. The derived results in this study could facilitate Moon-based data processing and the determination of the sampling interval and radiation resolution of an MWFOV under a certain manufacturing cost and error limit.

GAUSS - genesis of asteroids and evolution of the solar system: A sample return mission to Ceres.

The goal of Project GAUSS (Genesis of Asteroids and evolUtion of the Solar System) is to return samples from the dwarf planet Ceres. Ceres is the most accessible candidate of ocean worlds and the largest reservoir of water in the inner Solar System. It shows active volcanism and hydrothermal activities in recent history. Recent evidence for the existence of a subsurface ocean on Ceres and the complex geochemistry suggest past habitability and even the potential for ongoing habitability. GAUSS will return samples from Ceres with the aim of answering the following top-level scientific questions: What is the origin of Ceres and what does this imply for the origin of water and other volatiles in the inner Solar System?What are the physical properties and internal structure of Ceres? What do they tell us about the evolutionary and aqueous alteration history of dwarf planets?What are the astrobiological implications of Ceres? Is it still habitable today?What are the mineralogical connections between Ceres and our current collections of carbonaceous meteorites?

Replicability, Repeatability, and Long-term Reproducibility of Cerebellar Morphometry.

To identify robust and reproducible methods of cerebellar morphometry that can be used in future large-scale structural MRI studies, we investigated the replicability, repeatability, and long-term reproducibility of three fully automated software tools: FreeSurfer, CEREbellum Segmentation (CERES), and automatic cerebellum anatomical parcellation using U-Net with locally constrained optimization (ACAPULCO). Replicability was defined as computational replicability, determined by comparing two analyses of the same high-resolution MRI data set performed with identical analysis software and computer hardware. Repeatability was determined by comparing the analyses of two MRI scans of the same participant taken during two independent MRI sessions on the same day for the Kirby-21 study. Long-term reproducibility was assessed by analyzing two MRI scans of the same participant in the longitudinal OASIS-2 study. We determined percent difference, the image intraclass correlation coefficient, the coefficient of variation, and the intraclass correlation coefficient between two analyses. Our results show that CERES and ACAPULCO use stochastic algorithms that result in surprisingly high differences between identical analyses for ACAPULCO and small differences for CERES. Changes between two consecutive scans from the Kirby-21 study were less than ± 5% in most cases for FreeSurfer and CERES (i.e., demonstrating high repeatability). As expected, long-term reproducibility was lower than repeatability for all software tools. In summary, CERES is an accurate, as demonstrated before, and reproducible tool for fully automated segmentation and parcellation of the cerebellum. We conclude with recommendations for the assessment of replicability, repeatability, and long-term reproducibility in future studies on cerebellar structure.

Estimation of Winter Wheat Yield in Arid and Semiarid Regions Based on Assimilated Multi-Source Sentinel Data and the CERES-Wheat Model.

The farmland area in arid and semiarid regions accounts for about 40% of the total area of farmland in the world, and it continues to increase. It is critical for global food security to predict the crop yield in arid and semiarid regions. To improve the prediction of crop yields in arid and semiarid regions, we explored data assimilation-crop modeling strategies for estimating the yield of winter wheat under different water stress conditions across different growing areas. We incorporated leaf area index (LAI) and soil moisture derived from multi-source Sentinel data with the CERES-Wheat model using ensemble Kalman filter data assimilation. According to different water stress conditions, different data assimilation strategies were applied to estimate winter wheat yields in arid and semiarid areas. Sentinel data provided LAI and soil moisture data with higher frequency (<14 d) and higher precision, with root mean square errors (RMSE) of 0.9955 m2 m-2 and 0.0305 cm3 cm-3, respectively, for data assimilation-crop modeling. The temporal continuity of the CERES-Wheat model and the spatial continuity of the remote sensing images obtained from the Sentinel data were integrated using the assimilation method. The RMSE of LAI and soil water obtained by the assimilation method were lower than those simulated by the CERES-Wheat model, which were reduced by 0.4458 m2 m-2 and 0.0244 cm3 cm-3, respectively. Assimilation of LAI independently estimated yield with high precision and efficiency in irrigated areas for winter wheat, with RMSE and absolute relative error (ARE) of 427.57 kg ha-1 and 6.07%, respectively. However, in rain-fed areas of winter wheat under water stress, assimilating both LAI and soil moisture achieved the highest accuracy in estimating yield (RMSE = 424.75 kg ha-1, ARE = 9.55%) by modifying the growth and development of the canopy simultaneously and by promoting soil water balance. Sentinel data can provide high temporal and spatial resolution data for deriving LAI and soil moisture in the study area, thereby improving the estimation accuracy of the assimilation model at a regional scale. In the arid and semiarid region of the southeastern Loess Plateau, assimilation of LAI independently can obtain high-precision yield estimation of winter wheat in irrigated area, while it requires assimilating both LAI and soil moisture to achieve high-precision yield estimation in the rain-fed area.

CERES-Maize model for simulating genotype-by-environment interaction of maize and its stability in the dry and wet savannas of Nigeria.

When properly calibrated and evaluated, dynamic crop simulation models can provide insights into the different components of genotype by environment interactions (GEIs). Modelled outputs could be used to complement data from multi-environment trials. Field experiments were conducted in the rainy and dry seasons of 2015 and 2016 across four locations in maize growing regions of Northern Nigeria using 16 maize varieties planted under near-optimal conditions of moisture and soil nitrogen. The CERES-Maize model was calibrated using data from three locations and two seasons (rainy and dry) and evaluated using data from one location and two seasons all in 2015. Observed data from the four locations and two seasons in 2016 was used to create eight different environments. Two profile pits were dug in each location and were used separately in the simulations for each environment to provide replicated data for stability analysis in a combined ANOVA. The effects of the environment, genotype and GEI were highly significant (p = 0.001) for both observed and simulated grain yields. The environment explained 67 % and 64 % of the variations in observed and simulated grain yields respectively. The variance component of GEI (13 % for observed and 15 % for simulated) were lower but still considerable when compared to that of genotypes (19 % for observed and 21 % for simulated). From the stability analysis of the observed and simulated grain yields using six different stability models, three models (ASV, Ecovalence, and Sigma) ranked Ife Hybrid as the most stable variety. The slope of the regression (bi) model ranked Sammaz 11 as the most stable variety, while the Shukla model ranked Sammaz 28 as the most stable variety. Long-term seasonal analysis with the CERES-Maize model revealed that early and intermediate maturing varieties produce high yields in both wet and dry savannas, early and extra-early varieties produce high yields only in the dry savannas, while late maturing varieties produce high yields only in the wet savannas. When properly calibrated and evaluated, the CERES-Maize model can be used to generate data for GEI and stability studies of maize genotype in the absence of observed field data.

Clear-Sky Shortwave Downward Flux at the Earth's Surface: Ground-Based Data vs. Satellite-Based Data.

The radiative flux data and other meteorological data in the BSRN archive start from 1992, but the RadFlux data, the clear-sky radiative fluxes at the BSRN sites derived through regression analyses of actually observed clear-sky fluxes, did not come into existence until the early 2000s, and at first, they were limited to the 7 NOAA SURFRAD and 4 DOE ARM sites, a subset of the BSRN sites. Recently, the RadFlux algorithm was applied more extensively to the BSRN sites for the production of clear-sky ground-based fluxes. At the time of this writing, there are 7119 site-months of clear-sky fluxes at 42 BSRN sites spanning the time from 1992 to late 2017. These data provide an unprecedented opportunity to validate the satellite-based clear-sky fluxes. In this paper, the GEWEX SRB GSW(V3.0) shortwave downward fluxes spanning 24.5 years from 1983-07 to 2007-12, the CERES SYN1deg(Ed4A) and EBAF(Ed4.0) shortwave fluxes spanning 2000-03 to mid-2017 are compared with their RadFlux counterparts on the hourly, 3-hourly, daily and monthly time scales. All the three datasets show reasonable agreement with their ground-based counterparts. Comparison of the satellite-based surface shortwave clear-sky radiative fluxes to the BSRN RadFlux analysis shows negative biases. Further analysis shows that the satellite-based atmosphere contains greater aerosol optical paths as well as more precipitable water than RadFlux analysis estimates.

Calibration Changes to Terra MODIS Collection-5 Radiances for CERES Edition 4 Cloud Retrievals.

Previous research has revealed inconsistencies between the Collection 5 (C5) calibrations of certain channels common to the Terra and Aqua MODerate-resolution Imaging Spectroradiometers (MODIS). To achieve consistency between the Terra and Aqua MODIS radiances used in the Clouds and the Earth's Radiant Energy System (CERES) Edition 4 (Ed4) cloud property retrieval system, adjustments were developed and applied to the Terra C5 calibrations for channels 1-5, 7, 20, and 26. These calibration corrections were developed independently of those used for MODIS Collection 6 (C6) data, which became available after the CERES Ed4 processing had commenced. The comparisons demonstrate that the corrections applied to the Terra C5 data for CERES Edition 4 generally resulted in Terra-Aqua radiance consistency that is as good as or better than that of the C6 datasets. The C5 adjustments resulted in more consistent Aqua and Terra cloud property retrievals than seen in the previous CERES edition. Other calibration artifacts were found in one of the corrected channels and in some of the uncorrected thermal channels after Ed4 began. Where corrections were neither developed nor applied, some artifacts are likely to have been introduced into the Ed4 cloud property record. For example, the degradation in the Aqua MODIS 0.65-µm channel in both the C5 and C6 datasets affects trends in cloud optical depth retrievals. Thus, despite the much-improved consistency achieved for the Terra and Aqua datasets in Ed4, the CERES Ed4 cloud property datasets should be used cautiously for cloud trend studies because of those remaining calibration artifacts.

Pitted terrains on (1) Ceres and implications for shallow subsurface volatile distribution.

Prior to the arrival of the Dawn spacecraft at Ceres, the dwarf planet was anticipated to be ice-rich. Searches for morphological features related to ice have been ongoing during Dawn's mission at Ceres. Here we report the identification of pitted terrains associated with fresh Cerean impact craters. The Cerean pitted terrains exhibit strong morphological similarities to pitted materials previously identified on Mars (where ice is implicated in pit development) and Vesta (where the presence of ice is debated). We employ numerical models to investigate the formation of pitted materials on Ceres and discuss the relative importance of water ice and other volatiles in pit development there. We conclude that water ice likely plays an important role in pit development on Ceres. Similar pitted terrains may be common in the asteroid belt and may be of interest to future missions motivated by both astrobiology and in situ resource utilization.

Strategies for narrowing the maize yield gap of household farms through precision fertigation under irrigated conditions using CERES-Maize model.

BACKGROUND: Nitrogen (N) application significantly increases maize yield; however, the unreasonable use of N fertilizer is common in China. The analysis of crop yield gaps can reveal the limiting factors for yield improvement, but there is a lack of practical strategies for narrowing yield gaps of household farms. The objectives of this study were to assess the yield gap of summer maize using an integrative method and to develop strategies for narrowing the maize yield gap through precise N fertilization. RESULTS: The results indicated that there was a significant difference in maize yield among fields, with a low level of variation. Additionally, significant differences in N application rate were observed among fields, with high variability. Based on long-term simulation results, the optimal N application rate was 193 kg ha-1 , with a corresponding maximum attainable yield (AYmax ) of 10 318 kg ha-1 . A considerable difference between farmers' yields and AYmax was observed. Low agronomic efficiency of applied N fertilizer (AEN ) in farmers' fields was exhibited. CONCLUSION: The integrative method lays a foundation for exploring the specific factors constraining crop yield gaps at the field scale and for developing strategies for rapid site-specific N management. Optimization strategies to narrow the maize yield gap include increasing N application rates and adjusting the N application schedule. © 2016 Society of Chemical Industry.

Assessment of radiative feedback in climate models using satellite observations of annual flux variation.

In the climate system, two types of radiative feedback are in operation. The feedback of the first kind involves the radiative damping of the vertically uniform temperature perturbation of the troposphere and Earth's surface that approximately follows the Stefan-Boltzmann law of blackbody radiation. The second kind involves the change in the vertical lapse rate of temperature, water vapor, and clouds in the troposphere and albedo of the Earth's surface. Using satellite observations of the annual variation of the outgoing flux of longwave radiation and that of reflected solar radiation at the top of the atmosphere, this study estimates the so-called "gain factor," which characterizes the strength of radiative feedback of the second kind that operates on the annually varying, global-scale perturbation of temperature at the Earth's surface. The gain factor is computed not only for all sky but also for clear sky. The gain factor of so-called "cloud radiative forcing" is then computed as the difference between the two. The gain factors thus obtained are compared with those obtained from 35 models that were used for the fourth and fifth Intergovernmental Panel on Climate Change assessment. Here, we show that the gain factors obtained from satellite observations of cloud radiative forcing are effective for identifying systematic biases of the feedback processes that control the sensitivity of simulated climate, providing useful information for validating and improving a climate model.

Within-season yield prediction with different nitrogen inputs under rain-fed condition using CERES-Wheat model in the northwest of China.

BACKGROUND: Yield prediction within season is of great use to improve agricultural risk management and decision making. The objectives of this study were to access the yield forecast performance with increasing nitrogen inputs and to determine when the acceptable predicted yield can be achieved using the CERES-Wheat model. RESULTS: the calibrated model simulated wheat yield very well under various water and nitrogen conditions. Long-term simulation demonstrated that nitrogen input enlarged the annual variability of wheat yield generally. Within-season yield prediction showed that, regardless of nitrogen inputs, yield forecasts in the later growing season improved the accuracy and reduced the uncertainty of yield prediction. In a low-yielding year (2011-2012) and a high-yielding year (1991-1992), the date of acceptable predicted yield was achieved 62 and 65 days prior to wheat maturity, respectively. In a normal-yielding year (1983-1984), inadequate precipitation after the jointing stage in most historical years led to the underestimation of wheat yield and the date of accurate yield prediction was delayed to 235-250 days after simulation (7-22 days prior to maturity) for different N inputs. CONCLUSION: Yield prediction was highly influenced by the distribution of meteorological elements during the growing season and may show great improvement if future weather can be reliably forecast early. © 2015 Society of Chemical Industry.

CERES-Maize model-based simulation of climate change impacts on maize yields and potential adaptive measures in Heilongjiang Province, China.

BACKGROUND: Climate change would cause negative impacts on future agricultural production and food security. Adaptive measures should be taken to mitigate the adverse effects. The objectives of this study were to simulate the potential effects of climate change on maize yields in Heilongjiang Province and to evaluate two selected typical household-level autonomous adaptive measures (cultivar changes and planting time adjustments) for mitigating the risks of climate change based on the CERES-Maize model. RESULTS: The results showed that flowering duration and maturity duration of maize would be shortened in the future climate and thus maize yield would reduce by 11-46% during 2011-2099 relative to 1981-2010. Increased CO2 concentration would not benefit maize production significantly. However, substituting local cultivars with later-maturing ones and delaying the planting date could increase yields as the climate changes. CONCLUSION: The results provide insight regarding the likely impacts of climate change on maize yields and the efficacy of selected adaptive measures by presenting evidence-based implications and mitigation strategies for the potential negative impacts of future climate change.

Maize yield prediction and condition monitoring at the sub-county scale in Kenya: synthesis of remote sensing information and crop modeling.

Agricultural production assessments are crucial for formulating strategies for closing yield gaps and enhancing production efficiencies. While in situ crop yield measurements can provide valuable and accurate information, such approaches are costly and lack scalability for large-scale assessments. Therefore, crop modeling and remote sensing (RS) technologies are essential for assessing crop conditions and predicting yields at larger scales. In this study, we combined RS and a crop growth model to assess phenology, evapotranspiration (ET), and yield dynamics at grid and sub-county scales in Kenya. We synthesized RS information from the Food and Agriculture Organization (FAO) Water Productivity Open-access portal (WaPOR) to retrieve sowing date information for driving the model simulations. The findings showed that grid-scale management information and progressive crop growth could be accurately derived, reducing the model output uncertainties. Performance assessment of the modeled phenology yielded satisfactory accuracies at the sub-county scale during two representative seasons. The agreement between the simulated ET and yield was improved with the combined RS-crop model approach relative to the crop model only, demonstrating the value of additional large-scale RS information. The proposed approach supports crop yield estimation in data-scarce environments and provides valuable insights for agricultural resource management enabling countermeasures, especially when shortages are perceived in advance, thus enhancing agricultural production.

Modeling maize growth and nitrogen dynamics using CERES-Maize (DSSAT) under diverse nitrogen management options in a conservation agriculture-based maize-wheat system.

Agricultural field experiments are costly and time-consuming, and often struggling to capture spatial and temporal variability. Mechanistic crop growth models offer a solution to understand intricate crop-soil-weather system, aiding farm-level management decisions throughout the growing season. The objective of this study was to calibrate and the Crop Environment Resource Synthesis CERES-Maize (DSSAT v 4.8) model to simulate crop growth, yield, and nitrogen dynamics in a long-term conservation agriculture (CA) based maize system. The model was also used to investigate the relationship between, temperature, nitrate and ammoniacal concentration in soil, and nitrogen uptake by the crop. Additionally, the study explored the impact of contrasting tillage practices and fertilizer nitrogen management options on maize yields. Using field data from 2019 and 2020, the DSSAT-CERES-Maize model was calibrated for plant growth stages, leaf area index-LAI, biomass, and yield. Data from 2021 were used to evaluate the model's performance. The treatments consisted of four nitrogen management options, viz., N0 (without nitrogen), N150 (150 kg N/ha through urea), GS (Green seeker-based urea application) and USG (urea super granules @150kg N/ha) in two contrasting tillage systems, i.e., CA-based zero tillage-ZT and conventional tillage-CT. The model accurately simulated maize cultivar's anthesis and physiological maturity, with observed value falling within 5% of the model's predictions range. LAI predictions by the model aligned well with measured values (RMSE 0.57 and nRMSE 10.33%), with a 14.6% prediction error at 60 days. The simulated grain yields generally matched with measured values (with prediction error ranging from 0 to 3%), except for plots without nitrogen application, where the model overestimated yields by 9-16%. The study also demonstrated the model's ability to accurately capture soil nitrate-N levels (RMSE 12.63 kg/ha and nRMSE 12.84%). The study concludes that the DSSAT-CERES-Maize model accurately assessed the impacts of tillage and nitrogen management practices on maize crop's growth, yield, and soil nitrogen dynamics. By providing reliable simulations during the growing season, this modelling approach can facilitate better planning and more efficient resource management. Future research should focus on expanding the model's capabilities and improving its predictions further.