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.

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.

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.

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.

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.

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.

Root proliferation adaptation strategy improved maize productivity in the US Great Plains: Insights from crop simulation model under future climate change.

Adaptation measures are essential for reducing the impact of future climate risks on agricultural production systems. The present study focuses on implementing an adaptation strategy to mitigate the impact of future climate change on rainfed maize production in the Eastern Kansas River Basin (EKSRB), an important rainfed maize-producing region in the US Great Plains, which faces potential challenges of future climate risks due to a significant east-to-west aridity gradient. We used a calibrated CERES-Maize crop model to evaluate the impacts of baseline climate conditions (1985-2014), late-term future climate scenarios (under the SSP245 emission pathway and CMIP6 models), and a novel root proliferation adaptation strategy on regional maize yield and rainfall productivity. Changes in the plant root system by increasing the root density could lead to yield benefits, especially under drought conditions. Therefore, we modified the governing equation of soil root growth in the CERES-Maize model to reflect the genetic influence of a maize cultivar to improve root density by proliferation. Under baseline conditions, maize yield values ranged from 6522 to 12,849 kgha-1, with a regional average value of 9270 kgha-1. Projections for the late-term scenario indicate a substantial decline in maize yield (36 % to 50 %) and rainfall productivity (25 % to 42 %). Introducing a hypothetical maize cultivar by employing root proliferation as an adaptation strategy resulted in a 27 % increase in regional maize yield, and a 28 % increase in rainfall productivity compared to the reference cultivar without adaptation. We observed an indication of spatial dependency of maize yield and rainfall productivity on the regional precipitation gradient, with counties towards the east having an implicit advantage over those in the west. These findings offer valuable insights for the US Great Plains maize growers and breeders, guiding strategic decisions to adapt rainfed maize production to the region's impending challenges posed by climate change.

Yield Response of Spring Maize under Future Climate and the Effects of Adaptation Measures in Northeast China.

Agriculture production has been found to be the most sensitive sector to climate change. Northeast China (NEC) is one of the world's major regions for spring maize production and it has been affected by climate change due to increases in temperature and decreases in sunshine hours and precipitation levels over the past few decades. In this study, the CERES-Maize model-v4.7 was adopted to assess the impact of future climatic change on the yield of spring maize in NEC and the effect of adaptation measures in two future periods, the 2030s (2021 to 2040) and the 2050s (2041 to 2060) relative to the baseline (1986 to 2005) under RCP4.5 and RCP8.5 scenarios. The results showed that increased temperatures and the decreases in both the precipitation level and sunshine hours in the NEC at six representative sites in the 2030s and 2050s periods based on RCP4.5 and RCP8.5 climate scenarios would shorten the maize growth durations by (1-38 days) and this would result in a reduction in maize yield by (2.5-26.4%). Adaptation measures, including altered planting date, supplemental irrigation and use of cultivars with longer growth periods could offset some negative impacts of yield decrease in maize. For high-temperature-sensitive cultivars, the adoption of early planting, cultivar change and adding irrigation practices could lead to an increase in maize yield by 23.7-43.6% and these measures were shown to be effective adaptation options towards reducing yield loss from climate change. The simulation results exhibited the effective contribution of appropriate adaptation measures in eliminating the negative impact of future climate change on maize yield.

Climate change impact uncertainty assessment and adaptations for sustainable maize production using multi-crop and climate models.

Future climate scenarios are predicting considerable threats to sustainable maize production in arid and semi-arid regions. These adverse impacts can be minimized by adopting modern agricultural tools to assess and develop successful adaptation practices. A multi-model approach (climate and crop) was used to assess the impacts and uncertainties of climate change on maize crop. An extensive field study was conducted to explore the temporal thermal variations on maize hybrids grown at farmer's fields for ten sowing dates during two consecutive growing years. Data about phenology, morphology, biomass development, and yield were recorded by adopting standard procedures and protocols. The CSM-CERES, APSIM, and CSM-IXIM-Maize models were calibrated and evaluated. Five GCMs among 29 were selected based on classification into different groups and uncertainty to predict climatic changes in the future. The results predicted that there would be a rise in temperature (1.57-3.29 °C) during the maize growing season in five General Circulation Models (GCMs) by using RCP 8.5 scenarios for the mid-century (2040-2069) as compared with the baseline (1980-2015). The CERES-Maize and APSIM-Maize model showed lower root mean square error values (2.78 and 5.41), higher d-index (0.85 and 0.87) along reliable R2 (0.89 and 0.89), respectively for days to anthesis and maturity, while the CSM-IXIM-Maize model performed well for growth parameters (leaf area index, total dry matter) and yield with reasonably good statistical indices. The CSM-IXIM-Maize model performed well for all hybrids during both years whereas climate models, NorESM1-M and IPSL-CM5A-MR, showed less uncertain results for climate change impacts. Maize models along GCMs predicted a reduction in yield (8-55%) than baseline. Maize crop may face a high yield decline that could be overcome by modifying the sowing dates and fertilizer (fertigation) and heat and drought-tolerant hybrids.

Optimizing genotype-environment-management interactions for maize farmers to adapt to climate change in different agro-ecological zones across China.

Crop growth conditions are being altered by ongoing climate change and the agronomic management practices should be adjusted accordingly and timely. In Chinese Maize Belt, climate change impacts are always compounded by agronomic management and the regional differences have yet to be well understood. How local farmers adapt to climate change is a big challenge and related adaptive strategies are urgently required. Based on detailed field experiments performed for >15 years, we applied the CERES-Maize model to disentangle the impacts of individual climate variables, quantify the contributions of three low-cost measures (cultivar, sowing date, and planting density) to yield variations, and design effective adaptation options in each zone. We found the patterns and impacts of climate change varied among the cultivated areas: yield increased by 0.39% per year in Northeast China (NEC) and 0.78% in the northwestern arid area (NWA) but decreased by 1.13% in the North China Plain (NCP). The results highlighted the considerable impacts of increased minimum temperature and decreased solar radiation on the changes of maize yield. CERES-Maize model reproduced the phenology and yield well with <9% bias and >81% yield explanation ability. The simulation results suggested that an appropriate delay in sowing date could mitigate climatic negative effects and enhance maize yields significantly. Planting cultivars of Nongda108 in NEC, Zhengdan958 in the NCP, and Shendan10 in the NWA substantially increased yield compared with planting the cultivars most widely used by farmers. The optimal planting density were 11.4, 12.3, and 12.7 plants/m2 respectively, which were generally higher than the local common levels. By optimizing genotype (G)-environment (E)-management (M) interactions, maize yield can be enhanced by at least 10%, especially in the NWA, implying that efforts to increase food production should be made in low-yielding zones. This study illustrated the patterns of climate change in different zones, and demonstrated an effective approach to develop sustainable intensification options and improve yield and stability with fewer economic-environmental costs by optimizing G × E × M interactions in the future.

Assessing the impact of climate variability on maize using simulation modeling under semi-arid environment of Punjab, Pakistan.

Climate change and variability are major threats to crop productivity. Crop models are being used worldwide for decision support system for crop management under changing climatic scenarios. Two-year field experiments were conducted at the Water Management Research Center (WMRC), University of Agriculture Faisalabad, Pakistan, to evaluate the application of CERES-Maize model for climate variability assessment under semi-arid environment. Experimental treatments included four sowing dates (27 January, 16 February, 8 March, and 28 March) with three maize hybrids (Pioneer-1543, Mosanto-DK6103, Syngenta-NK8711), adopted at farmer fields in the region. Model was calibrated with each hybrid independently using data of best sowing date (27 January) during the year 2015 and then evaluated with the data of 2016 and remaining sowing dates. Performance of model was evaluated by statistical indices. Model showed reliable information with phenological stages. Model predicted days to anthesis and maturity with lower RMSE (< 2 days) during both years. Model prediction for biological yield and grain yield were reasonably good with RMSE values of 963 and 451 kg ha-1, respectively. Model was further used to assess climate variability. Historical climate data (1980-2016) were used as input to simulate the yield for each year. Results showed that days to anthesis and maturity were negatively correlated with increase in temperature and coefficient of regression ranged from 0.63 to 0.85, while its values were 0.76 to 0.89 kg ha-1 for grain yield and biological yield, respectively. Sowing of maize hybrids (Pioneer-1543 and Mosanto-DK6103) can be recommended for the sowing on 17 January to 6 February at the farmer field for general cultivation in the region. Early sowing before 17 January should be avoided due to severe reduction in grain yield of all hybrids. A good calibrated CERES-Maize model can be used in decision-making for different management practices and assessment of climate variability in the region.

[Impacts of climate change on maize potential productivity in Northeast China and the simulation of control measures: A case study of Jilin Province, China]. / 气候变化对东北地区玉米生产潜力的影响与调控措施模拟­­ä»¥å‰æž—省为例.

In this study, we collected data of meteorology, soil property, agricultural management and corn yield from five representative sites in Jilin Province, China, and integrated these data into a crop growth model of CERES-maize to simulate the potential productivity of five corn varieties. Our objectives were to simulate, calibrate and validate genetic parameters of the corns based on the analyses of climatic effects on the productivity, and to establish best practices for enhancing crop production in response to climatic change. The results showed that the projected days of sowing-flo-wering and flowering-maturing stages and yields of corn were well consistent with the measured va-lues with normalized mean variances being 2.96%, 3.40% and 9.37%, respectively, and the stan-dard deviation ranged from -10.6% to 15.2%. The mean projected light-temperature potential productivity (LTPP) of corns ranged from 7799.60 to 12902.83 kg·hm-2·a-1, which decreased by 128.6-880.3 kg·hm-2 every 10 years. The correlation analysis suggested that climate change, i.e. temperature rising and significant decline of total radiation during the growth of corns, dominated the decrease of LTPP of corns in the region. The simulated genetic parameters indicated that the LTPP of the corns increased linearly with the increase of P5 (filling stage characteristic parameter referred to silking to physiological maturity of more than 8 ℃ heat time). Our model estimated that the LTPP might increase 154.44-261.10 kg·hm-2 for every 10 ℃·d increase of P5. The simulated sowing date delay showed that five days' sowing delay would maximize the LTPP of corns in Dunhua and Liaoyuan with 0.47% and 1.32% increase, respectively, while 15 days' delay would maximize the LTPP in Huadian and Yushu with 1.10% and 4.06% increase, respectively.

CERES-Maize Model for Determining the Optimum Planting Dates of Early Maturing Maize Varieties in Northern Nigeria.

Field trials were carried out in the Sudan Savannah of Nigeria to assess the usefulness of CERES-maize crop model as a decision support tool for optimizing maize production through manipulation of plant dates. The calibration experiments comprised of 20 maize varieties planted during the dry and rainy seasons of 2014 and 2015 at Bayero University Kano and Audu Bako College of Agriculture Dambatta. The trials for model evaluation were conducted in 16 different farmer fields across the Sudan (Bunkure and Garun-Mallam) and Northern Guinea (Tudun-Wada and Lere) Savannas using two of the calibrated varieties under four different sowing dates. The model accurately predicted grain yield, harvest index, and biomass of both varieties with low RMSE-values (below 5% of mean), high d-index (above 0.8), and high r-square (above 0.9) for the calibration trials. The time series data (tops weight, stem and leaf dry weights) were also predicted with high accuracy (% RMSEn above 70%, d-index above 0.88). Similar results were also observed for the evaluation trials, where all variables were simulated with high accuracies. Estimation efficiencies (EF)-values above 0.8 were observed for all the evaluation parameters. Seasonal and sensitivity analyses on Typic Plinthiustalfs and Plinthic Kanhaplustults in the Sudan and Northern Guinea Savannas were conducted. Results showed that planting extra early maize varieties in late July and early maize in mid-June leads to production of highest grain yields in the Sudan Savanna. In the Northern Guinea Savanna planting extra-early maize in mid-July and early maize in late July produced the highest grain yields. Delaying planting in both Agro-ecologies until mid-August leads to lower yields. Delaying planting to mid-August led to grain yield reduction of 39.2% for extra early maize and 74.4% for early maize in the Sudan Savanna. In the Northern Guinea Savanna however, delaying planting to mid-August resulted in yield reduction of 66.9 and 94.3% for extra-early and early maize, respectively.

Simulation of maize growth under different sowing times and deficit irrigation conditions / Imulation of maize growth under different sowing times and deficit irrigation conditions

Simulation models of crops are referred as an efficient complement for the experimental study. Also crop simulation models can be useful for making appropriate decisions on agricultural systems. So this study aimed to simulate the growth of maize under different sowing times and deficit irrigation conditions, using the Decision Support System for Agrotechnology Transfer (DSSAT) model in 2014 year. This study was conducted in the research field of Islamic Azad University of Karaj in 2013 year. The experiment was designed in a split-block with four replications. Treatments included four sowing times of April 30 (S1), May 20 (S2), June 10 (S3), and June 27 (S4) in the main plots and three irrigation levels of 40% available water depletion (W1), 60% available water depletion (W2), and 80% available water depletion in the sub-plots. Root Mean Square Error (RMSE) of grain yield for all four sowing times on three levels of irrigation in Karaj region varied from 581.43 to 1,990.81 kg per hectare. It was also calculated the model efficiency coefficient (d) ranged 0.87-0.98 for the trait. The RMSE of the total dry matter was determined 861.88-2,173.66 kg per hectare; that was while R2 (1:1) of total dry weight varied 0.89-0.98. The results indicate that the model's ability to predict dry matter yield of maize is good enough.

Os modelos de simulação de culturas são referidos como um complemento eficaz para o estudo experimental. Os modelos de simulação de culturas também podem ser úteis para a tomada de decisões adequadas em sistemas agrícolas. Portanto, este estudo teve como objetivo simular o crescimento do milho sob diferentes épocas de semeadura e condições de déficit de irrigação, utilizando o Sistema de Apoio à Decisão para o modelo de Transferência de Agrotecnologia (DSSAT) no ano de 2014. Este estudo foi realizado no campo experimental da Islâmica Azad Universidade de Karaj no ano de 2013. O experimento foi desenvolvido com delineamento em faixas com 4 repetições. Os tratamentos incluíram quatro épocas de semeaduras de 30 de Abril (S1), 20 de maio (S2), 10 de junho (S3), e 27 de junho (S4) nas parcelas principais e três níveis de irrigação de esgotamento de 40% de água disponível (W1), 60% depleção de água disponível (W2), e 80% esgotamento da água disponível nos sub-parcelas. O erro da raiz do quadrado médio (RMSE) do rendimento de grãos para todas as quatro épocas de semeadura nos três níveis de irrigação na região Karaj variou de 581,43 a 1,990,81 kg por hectare. Também foi calculado o coeficiente de eficiência do modelo (d) que variou de 0,87 a 0,98 para a característica. O RMSE da matéria seca total foi determinada entre 861.88 e 2,173.66 kg por hectare; enquanto R2 (1:1) da massa total variou de 0,89 a 0,98. Os resultados indicam que a capacidade do modelo para prever a produção de matéria seca de milho é suficientemente boa.

Simulação de florestamento e da produção de milho através do modelo CERES: Maize / Mayze yeld and blooming date simulation by using CERES: Maize Model

A utilização dos computadores na agricultura tem trazido inúmeros benefícios ao empresário rural. Entre tais benefícios, a utilização de programas de computador para simular o crescimento e desenvolvimento de plantas; bem como o seu uso na otimização de práticas culturais, tem se mostrado como uma opção. Objetivou-se, neste trabalho, avaliar o programa CERES-Maize na simulação do florescimento e produção de milho (Zea mays L.), em diferentes cenários de investimento. Os resultados das simulações com o programa CERES-Maize mostraram que as diferenças entre as produções simuladas, comparadas com os dados obtidos em campo, estão entre os limites de 5% a 8% de erro, aceitáveis pelo programa. A mesma tendência foi verificada entre os valores observados e simulados para os dias de florescimento, em todos os cenarios. O programa CERES-Maize mostrou-se bastante robusto e eficiente nas simulações efetuadas. O uso desse programa pode ser visto como uma ferramenta adicional ao produtor no processo decisório, durante o planejamento da implantação da cultura.

The use of computers in agriculture has brought several benefits to the farmers. Among these benefits, the use of crop models to simulate plant growth and development, as well as a tool for optimization process and decision support aid, has been an option. The aim of this study was to evaluate the efficiency of Ceres-Maize model to simulate maize blooming date and yield at different investment scenarios. Simulation results showed that the difference between simulated and observed yield data were in the 5% and 8% error range, acceptable for the program. The same tendency was observed when comparing blooming dates in all scenarios. The model was very efficient and suitable for all of the simulations performed. Without any doubt CERES-Maize may be used as a tool, by farmers, in the crop planning process.