scholarly journals Calibration and Simulation of the CERES-Sorghum and CERES-Maize Models for Crops in the Central-West Region of Paraná State

2019 ◽  
Vol 11 (18) ◽  
pp. 140
Author(s):  
Paulo Vinicius Demeneck Vieira ◽  
Paulo Sérgio Lourenço de Freitas ◽  
Roberto Rezende ◽  
Rivanildo Dallacort ◽  
João Danilo Barbieri ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Meteorological Data ◽  
Planting Dates ◽  
Early Variety ◽  
Early Maturity ◽  
Area Index ◽  
Normal Cycle ◽  
Growing Seasons ◽  
West Region

Simulation models have been widely used to generate yield data by forecasting climate variables and changes in growing seasons. The aim of this study was to calibrate genetic coefficients and simulate growth, development and yield in maize and sorghum crops based on historical meteorological data for the municipality of Juranda (2007 to 2013), in the central-west region of Paraná State, Brazil. Treatments were established based on three planting dates in two growing seasons for a group of super early maturity maize hybrids (DKB 330 Pro), and two groups of sorghum hybrids, the first a super early variety (ADV 123) and the second with a normal cycle (1G282). The variables assessed were number of days from planting to flowering, leaf area index (LAI), and 1000 seed weight and yield. Statistical coefficients were used to evaluate calibration accuracy. The results demonstrated that the models were highly efficient at simulating crop cycles, yield and leaf area index, with agreement indices and modeling efficiency values above 0.90. The results indicated that the CERES-Maize and CERES-Sorghum models generated satisfactory and comparative simulations of maize and sorghum yield for the study area on different planting dates.

Estimating leaf area index from MODIS and surface meteorological data using a dynamic Bayesian network

2012 ◽  
Vol 127 ◽  
pp. 30-43 ◽  
Author(s):  
Yuzhen Zhang ◽  
Yonghua Qu ◽  
Jindi Wang ◽  
Shunlin Liang ◽  
Yan Liu
Keyword(s):  
Bayesian Network ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Meteorological Data ◽  
Dynamic Bayesian Network ◽  
Area Index

scholarly journals The effect of leaf area index on potatoes yield in soils contaminated by some heavy metals

2011 ◽  
Vol 48 (No. 7) ◽  
pp. 298-306
Author(s):  
M. Jůzl ◽  
M. Štefl
Keyword(s):  
Heavy Metals ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Significant Influence ◽  
Growth Analysis ◽  
Growing Season ◽  
Negative Influence ◽  
Field Conditions ◽  
Early Variety ◽  
Area Index

A method of growth analysis was used to evaluate the yield results in experiments conducted during years 1999–2001 on School co-operative farm in Žabčice. In sequential terms of sampling from two potato varieties with different duration of growing season, the effect of leaf area index (L, LAI), on yield of tubers in soils contaminated by cadmium, arsine and beryllium, was evaluated. From a growers view the phytotoxic influence on development of assimilatory apparatus and yields during the growth of a very-early variety Rosara and a medium-early Korela were evaluated. These varieties were grown under field conditions in soils contaminated by graded levels of cadmium, arsenic and beryllium. The yields of tubers were positively influenced by duration of growing season and increased of leaf area index during three experimental years. On the contrary, graded levels of heavy metals had negative influence on both chosen varieties. The highest phytotoxic influence was recorded of arsine and the lowest of cadmium. Significant influence of arsenic and beryllium on size of leaf area index in the highest applied variants was found. The influence of experimental years on tuber yields was also statistically significant.

scholarly journals Cluster and Principle Component Analysis of Soybean Grown at Various Row Spacings, Planting Dates and Plant Populations

2018 ◽  
Vol 3 (1) ◽  
pp. 110-121 ◽  
Author(s):  
Charanjit Singh Kahlon ◽  
Bin Li ◽  
James Board ◽  
Mahendra Dia ◽  
Parmodh Sharma ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Dry Matter ◽  
Light Interception ◽  
Plant Population ◽  
Large Set ◽  
Planting Dates ◽  
Area Index ◽  
Principle Components Analysis

Abstract Increased light interception (LI), along with concomitant increases in crop growth rate (CGR), is the main factor explaining how cultural factors such as row spacing, plant population, and planting date affect soybean yield. Leaf area index (LAI), LI, and CGR are interrelated in a “virtuous spiral” where increased LAI leads to greater LI resulting in a higher CGR and more total dry matter per area (TDM). This increases LAI, thus accelerating the entire physiological process to a higher level. A greater understanding of this complex growth dynamic process could be achieved through use of cluster analysis and principle components analysis (PCA). Cluster analysis involves grouping of similar objects in such way that objects in same cluster are similar to each other and dissimilar to objects in other cluster. PCA is a technique used to reduce a large set of variables to a few meaningful ones. Seasonal relative leaf area index (RLAI), relative light interception (RLI), and relative total dry matter (RTDM) response curves were determined from the data by a stepwise regression analysis in which these parameters were regressed against relative days after emergence (RDAE). Greatest levels of RLAI, RLI and RTDM were observed in soybean planted early on narrow row spacings and recorded greater plant population. In contrast, lower levels of these parameters occurred on plants with wide row spacings at late planting dates. For farmers, these results are useful in terms of adopting certain cultural practices which can help in the management of stress in soybean.

scholarly journals MERIS GPP/NPP product for Estonia: II. Complex meteorological limiting factor and optimum leaf area index / MERIS’e GPP/NPP tulem Eesti jaoks: II. Kompleksne meteoroloogiline piirangutegur ja optimaalne lehepinnaindeks

2014 ◽  
Vol 61 (1) ◽  
pp. 5-26
Author(s):  
Tiit Nilson ◽  
Mattias Rennel ◽  
Mait Lang
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Meteorological Data ◽  
Vegetation Period ◽  
Meteorological Conditions ◽  
Limiting Factors ◽  
Carbon Gain ◽  
Limiting Factor ◽  
Area Index ◽  
Seasonal Course

Abstract. The merits and possible problems of the light use efficiency-concept based GPP/NPP models applied together with satellite images and meteorological data to quantitatively understand the role of different meteorological factors in forest productivity are analysed. A concept of the complex meteorological limiting factor for plant productivity is introduced. The factor includes the effects of incoming photosynthetically active radiation as well as the temperature and water limiting factors. Climatologically averaged seasonal courses of the complex meteorological limiting factor derived from the records of two contrasting meteorological stations in Estonia - inland Tartu/Tõravere and coastal Sõrve - are shown. Leaf phenology, here described via the seasonal course of leaf area index (LAI), is interpreted as a possible means to maximise the carbon gain under particular meteorological conditions. The equations for the optimum seasonal course of LAI as derived from the NPP model are presented. As the daily adjustment of plant LAI to sudden changes in meteorological conditions is not possible, several approximate strategies for LAI seasonal course to maximise the yearly NPP of vegetation are analysed. Typical optimal courses of LAI show some seasonal asymmetry resulting in lower values of LAI in the second half of the vegetation period due to higher air temperatures and respiration costs. Knowledge about optimum LAI courses has a cognitive value, but can also be used as the simulated LAI courses in several models when the measured LAI values are not available. As the considered GPP/NPP models fail to adequately describe the local trends in forest and agricultural productivity in Estonia, the ways to improve the model’s performance are shown.

scholarly journals The role of leaf area and photosynthesis in optimising the sowing date of maize

2015 ◽  
pp. 5-10
Author(s):  
Enikő Bene ◽  
Mihály Sárvári
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
High Performance ◽  
Photosynthetic Capacity ◽  
Linear Regression Analysis ◽  
Sowing Date ◽  
Area Index ◽  
The Third ◽  
Growing Seasons ◽  
Plant Sciences

  Our sowing date experiment took place in the Demonstration Garden of Institution of Plant Sciences, Agricultural Center of University of Debrecen, in 2012–2014. The thesis contains data of test year 2014. Our purpose, besides several other examinations, was to observe how sowing date influences leaf area index and activity of photosynthesis of maize hybrids, and how those factors affect fruiting. In the experiment we monitored the change of the leaf area index and the photosynthesis of hybrids with four different growing seasons. Based on the results, it can be concluded that most of the examined hybrids reached their smallest leaf area with the third sowing date and with the highest yield results. Hybrid Da Sonka had the largest leaf area (4.10 m2 m-2), and hybrid DKC 4590 produced the highest yield (13.16 t ha-1) with the third sowing date. During testing the photosynthetic capacity, the extremely high performance of the youngest plants with the third sowing date is outstanding, which can be explained by the different ripening periods. Examination of the correlation between the photosynthetic capacity and the yield, by linear regression analysis, also proves that photosynthesis has a determinative role in fruiting. The results obtained confirm that not only the environmental and agricultural factors in the growing season have effect on the yield, but also other factors like the leaf area index and the photosynthesis are determinative parameters, and all those factors together, modifying effects of each other, develop average yields.

scholarly journals EVALUATION OF CERES-MAIZE MODEL FOR HYBRID MAIZE (Zea mays L.) PRODUCTION

2019 ◽  
Vol 13 (1) ◽  
pp. 96-110
Author(s):  
Amied Ali ◽  
Bashrat Ali
Keyword(s):  
Grain Yield ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Planting Dates ◽  
Sowing Time ◽  
Maize Crop ◽  
Area Index ◽  
Hybrid Maize ◽  
The Impact ◽  
Plot Design

The trial was conducted at Agronomic Research farm, University of Sargodha during spring growing season, 2015, to calibrate and evaluate CERES-Maize model for simulating the impact of different sowing time on maize crop. The experiment was laid out in split plot design having three replications, keeping planting dates (25th Feb, 6thMar and 14thMar) in main plots and hybrids i.e. (DK-9108, DK-6525 and DK-6142) in the sub plots. The Calibration of CSM-CERES-Maize model showed the best possible closeness between simulated and observed days to flowering and physiological maturity, leaf area index (LAI), Total dry matter (TDM), and grain yield with % error of 4.0, -1.5, 0.41, 0.07, 0.14 and 0.3% , respectively, when maize hybrid H1 (DK- 6142) was sown at firstsowing date (25th Feb). DSSAT,CERES- Maize model predicted the phenological traits like anthesis and maturity phase. Number of days to anthesis and maturity simulated by model were lesser to the observed values, where as, simulated grain yield was higher as compared to observed data for all the three cultivars. Model calculated the close similarity between experimental and computer-generated values for leaf area index.

scholarly journals Temporal Dynamics of Soybean Rust Associated With Leaf Area Index in Soybean Cultivars of different Maturity Groups

Plant Disease ◽  
2015 ◽  
Vol 99 (9) ◽  
pp. 1216-1226 ◽  
Author(s):  
E. N. Moreira ◽  
F. X. R. Vale ◽  
P. A. Paul ◽  
F. A. Rodrigues ◽  
W. C. Jesus Júnior
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Planting Date ◽  
Soybean Rust ◽  
Relative Area ◽  
Planting Dates ◽  
Area Index ◽  
Progress Curve ◽  
Early Maturing ◽  
Maturity Groups

Experiments were conducted in Mato Grosso, Brazil, from 2009 to 2011 to evaluate the effects of planting date (October, November, December, and January) on soybean rust (SBR) and leaf area index (LAI) in SBR-susceptible soybean cultivars of different maturity groups (early-maturing, midseason, and late-maturing). Mean relative area under the LAI progress curve (RAULAIPC) was significantly higher (P < 0.05) for the late-maturing than early-maturing and midseason cultivars. The October planting date had significantly higher (P < 0.05) mean RAULAIPC than the December and January planting dates. Mean relative area under the SBR progress curve was significantly lower (P < 0.05) for the late-maturing than the midseason and early-maturing cultivars, and significantly higher (P < 0.05) for the December and January than the October and November planting dates. Based on the logistic population growth model, SBR severity increased over time at a significantly higher mean rate for the early-maturing than the midseason and late-maturing cultivars. It took longer for SBR to reach a certain severity level for the late-maturing cultivar planted in January than the early-maturing cultivar planted in October. This implies that fungicides would need to be applied early to the early-maturing cultivar planted in October to minimize yield loss.

scholarly journals The Impact of Seasonal Environments in a Tropical Savanna Climate on Forking, Leaf Area Index, and Biomass of Cassava Genotypes

Agronomy ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 19 ◽  
Author(s):  
Phanupong Phoncharoen ◽  
Poramate Banterng ◽  
Nimitr Vorasoot ◽  
Sanun Jogloy ◽  
Piyada Theerakulpisut ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Dry Weight ◽  
Weather Data ◽  
Storage Root ◽  
Storage Roots ◽  
Area Index ◽  
Growing Seasons ◽  
The Impact ◽  
And Storage

Information on the forking, leaf area index, and biomass of cassava for different growing seasons could help design appropriate management to improve yield. The objective was to evaluate the forking date, leaf growth, and storage root yield of different cassava genotypes grown at different planting dates. Four cassava genotypes (Kasetsart 50, Rayong 9, Rayong 11, and CMR38–125–77) were evaluated using a randomized complete block design with four replications. The cassava genotypes were planted on 20 April, 25 May, 30 June, 5 October, 10 November, and 15 December 2015, and 19 May and 3 November 2016. The soil properties prior to the planting, forking date, leaf area index (LAI), dry weights, harvest index (HI), starch content, and weather data were recorded. The forking date patterns for all of the growing seasons varied depending on the cassava genotypes. The weather caused occurring in the first forking for the Rayong 11 and CMR38–125–77 and the second forking for Rayong 11, but not for Kasetsart 50. The forking CMR38–125–77 had a higher LAI, leaf dry weight, biomass, and storage root dry weight than the non-forking Rayong 9. The higher storage root yields in Rayong 9 compared with Rayong 11 were due to an increased partitioning of the storage roots.

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