Modelo matemático para previsão da produtividade do cafeeiro

Miranda, José Messias; Reinato, Rosicler A. O.; Silva, Adriano B. da

doi:10.1590/s1415-43662014000400001

Cited by 9 publications

(4 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nitrogen can significantly affect the performance of the three main processes of photosynthesis: stomatal control of CO 2 supply, thylakoid electron transport (light reaction), and Calvin cycle (dark reaction) [20, 21]. Our results indicate that the Pn rate of NH 4 + -treated plants was higher than those of sole NO 3 − -treated plants.…”

Section: Discussionmentioning

confidence: 86%

Appropriate NH4 +: NO3 − ratio improves low light tolerance of mini Chinese cabbage seedlings

Liao

Dawuda

et al. 2017

BMC Plant Biol

View full text Add to dashboard Cite

BackgroundIn northwest of China, mini Chinese cabbage (Brassica pekinensis) is highly valued by consumers, and is widely cultivated during winter in solar-greenhouses where low light (LL) fluence (between 85 and 150 μmol m−2 s−1 in day) is a major abiotic stress factor limiting plant growth and crop productivity. The mechanisms with which various NH4 +: NO3 − ratios affected growth and photosynthesis of mini Chinese cabbage under normal (200 μmol m−2 s−1) and low (100 μmol m−2 s−1) light conditions was investigated. The four solutions with different ratios of NH4 +: NO3 − applied were 0:100, 10:90, 15:85 and 25:75 with the set up in a glasshouse in hydroponic culture. The most appropriate NH4 +: NO3 − ratio that improved the tolerance of mini Chinese cabbage seedlings to LL was found in our current study.ResultsUnder low light, the application of NH4 +: NO3 − (10:90) significantly stimulated growth compared to only NO3 − by increasing leaf area, canopy spread, biomass accumulation, and net photosynthetic rate. The increase in net photosynthetic rate was associated with an increase in: 1) maximum and effective quantum yield of PSII; 2) activities of Calvin cycle enzymes; and 3) levels of mRNA relative expression of several genes involved in Calvin cycle. In addition, glucose, fructose, sucrose, starch and total carbohydrate, which are the products of CO2 assimilation, accumulated most in the cabbage leaves that were supplied with NH4 +: NO3 − (10:90) under LL condition. Low light reduced the carbohydrate: nitrogen (C: N) ratio while the application of NH4 +: NO3 − (10:90) alleviated the negative effect of LL on C: N ratio mainly by increasing total carbohydrate contents.ConclusionsThe application of NH4 +:NO3 − (10:90) increased rbcL, rbcS, FBA, FBPase and TK expression and/or activities, enhanced photosynthesis, carbohydrate accumulation and improved the tolerance of mini Chinese cabbage seedlings to LL. The results of this study would provide theoretical basis and technical guidance for mini Chinese cabbage production. In practical production, the ratio of NH4 +:NO3 − should be adjusted with respect to light fluence for successful growing of mini Chinese cabbage.Electronic supplementary materialThe online version of this article (doi:10.1186/s12870-017-0976-8) contains supplementary material, which is available to authorized users.

show abstract

Section: Discussionmentioning

confidence: 86%

Appropriate NH4 +: NO3 − ratio improves low light tolerance of mini Chinese cabbage seedlings

Liao

Dawuda

et al. 2017

BMC Plant Biol

View full text Add to dashboard Cite

show abstract

“…Estimates described above were obtained from these equations, which are considered to be models applied for each phenological stage. Estimates obtained from these equations were equally Fahl et al (2005) to estimate the productivity in the graining stage as follows: Miranda et al (2014), aiming to develop a simple and accurate method to estimate the coffee production, also developed similar equations, inclusively with an angular coefficient equal to 0.00053 for the fruits expansion period.…”

Section: Resultsmentioning

confidence: 99%

Validation of a phenological model for coffee tree productivity in Southern State of Minas Gerais, Brazil

Miranda¹,

Carvalho²,

Fahl³

et al. 2017

Afr. J. Agric. Res.

View full text Add to dashboard Cite

There are studies on the current state of understanding productivity models, focusing on the applicability of different estimation models for coffee tree productivity; the majority involving a considerable level of complexity. Thus, when searching for a simple and direct association between phenological characteristics and coffee productivity, doing research on this hypothesis is necessary. In this study, we aimed to validate a phenological model for coffee tree productivity by using phenological indices, under given edaphoclimatic conditions of Southern State of Minas Gerais, Brazil. We used 10 sample plots obtained from the municipalities of Lavras, Varginha, Carmo de Minas, Ijaci and Santo Antônio do Amparo. Plots were chosen based on the existing history about coffee productivity, which is over 40 sacks ha -1 . Phenological data were collected in September-October, December-January and March to April of the harvesting seasons 2012/2013 and 2013/2014. The number of flowers and fruits were obtained at the fourth and fifth productive nodes of coffee plagiotropic branches sampled at the middle third of each coffee plant. Forty plants were sampled in each plot for the measurement of plant height and estimates of productivity phenological indices. Data regarding the observed production were obtained for models comparison and validation and, then, statistical tests were run. Results showed that these models are suitable for the coffee crop in the region under study. In addition, productivity phenological indices showed good correlation with the observed productivity.

show abstract

“…To ensure that these crops remain economically profitable, new cultivation techniques need to be adopted to improve the current processes. Several models are dedicated to crop yield estimation through climatic variables, flowering records, and soil factors, among others [4][5][6]35]. This study is oriented towards early prediction of coffee crop yields by using only multi-spectral image data obtained using low-cost tools.…”

Section: Discussionmentioning

confidence: 99%

“…The subsequent process is the filling of the coffee fruits, and it is fundamental to foresee the demanding nutritional requirements of the tree, before the maturation stage and picking. Furthermore, understanding crop yields allows support for decision-making in the other processes, such as picking, drying, and storage [4,5]. Crop yield is directly determined by the dynamics of the tree-soil-environment system, as defined by specific predictors in each element of the system.…”

Section: Introductionmentioning

confidence: 99%

Feasibility of Early Yield Prediction per Coffee Tree Based on Multispectral Aerial Imagery: Case of Arabica Coffee Crops in Cauca-Colombia

2023

View full text Add to dashboard Cite

Crop yield is an important factor for evaluating production processes and determining the profitability of growing coffee. Frequently, the total number of coffee beans per area unit is estimated manually by physically counting the coffee cherries, the branches, or the flowers. However, estimating yield requires an investment in time and work, so it is not usual for small producers. This paper studies a non-intrusive and attainable alternative to predicting coffee crop yield through multispectral aerial images. The proposal is designed for small low-tech producers monitored by capturing aerial photos with a MapIR camera on an unmanned aerial vehicle. This research shows how to predict yields in the early stages of the coffee tree productive cycle, such as at flowering by using aerial imagery. Physical and spectral descriptors were evaluated as predictors for yield prediction models. The results showed correlations between the selected predictors and 370 yield samples of a Colombian Arabica coffee crop. The coffee tree volume, the Normalized Difference Vegetation Index (NDVI), and the Coffee Ripeness Index (CRI) showed the highest values with 71%, 55%, and 63%, respectively. Further, these predictors were used as the inputs for regression models to analyze their precision in predicting coffee crop yield. The validation stage concluded that Linear Regression and Stochastic Descending Gradient Regression were better models with determination coefficient values of 56% and 55%, respectively, which are promising for predicting yield.

show abstract

Modelo matemático para previsão da produtividade do cafeeiro

Cited by 9 publications

References 10 publications

Appropriate NH4 +: NO3 − ratio improves low light tolerance of mini Chinese cabbage seedlings

Appropriate NH4 +: NO3 − ratio improves low light tolerance of mini Chinese cabbage seedlings

Validation of a phenological model for coffee tree productivity in Southern State of Minas Gerais, Brazil

Feasibility of Early Yield Prediction per Coffee Tree Based on Multispectral Aerial Imagery: Case of Arabica Coffee Crops in Cauca-Colombia

Contact Info

Product

Resources

About