scholarly journals Genetic parameters and line selection of Cucurbita pepo based on selection indices

2020 ◽  
Vol 36 ◽  
Author(s):  
Igor Forigo Beloti ◽  
Gabriel Mascarenhas Maciel ◽  
Fernando Cezar Juliatti ◽  
Rafael Resende Finzi ◽  
Daniel Bonifácio Oliveira Cardoso
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Genetic Parameters ◽  
Leaf Temperature ◽  
Area Index ◽  
Balanced Distribution ◽  
Multiple Characteristics ◽  
Simultaneous Selection ◽  
Selection Indexes ◽  
Selection Of

In the improvement of pumpkins, the selection based on one or a few characters of interest tends to be less efficient, leading to a superior product only compared to the few characters selected. To maximize the simultaneous selection of multiple characteristics of interest, selection indexes are used to obtain a numerical value resulting from the combination of the characters on which the simultaneous selection is to be practiced. The objective of this study was to determine genetic parameters and the most appropriate selection indexes in strains of Summer squash (C. pepo). Statistical analyzes were performed based on 65 genotypes belonging to the vegetable germplasm bank of the Federal University of Uberlândia. The variables analyzed were: leaf area index, precocity, SPAD index, productivity. plant-1, number of fruits. Plant-1, leaf temperature, NDVI index and NDRE index. The indexes were used: Smith (1936) and Hazel (1943), the sum of “Ranks” by Mulamba and Mock (1978), and Willians (1962). The selection methodologies selected ten individuals (15% of the genotypes). The values found for h² (%) ranged from 36.92% (SPAD) to 59.65% (cycle). The values obtained in the CVg / CVe quotient were below 1, varying from 0.18 for leaf temperature to 0.70 for the cycle, with the other variables close to 0.5. The CVg genetic variation coefficient (%) was also low, ranging from 1.84% for leaf temperature to 30.94% for productivity. The greatest gains obtained with direct and indirect selection were for the characters productivity (35.92%), NDRE (33.04%), number of fruits (28.93%) and leaf area index (22.72%). The Mulamba and Mock (1978) index showed the highest total selection gain value, providing a balanced distribution of selection gains, choosing the genotypes: 8, 31, 34, 38, 42, 64, 65, 66, 67 and 68.

scholarly journals Selection of sowing date and biofertilization as alternatives to improve the yield and profitability of the F68 rice variety

2020 ◽  
Vol 38 (1) ◽  
pp. 61-72
Author(s):  
Yeison Mauricio Quevedo-Amaya ◽  
José Isidro Beltrán-Medina ◽  
José Álvaro Hoyos-Cartagena ◽  
John Edinson Calderón-Carvajal ◽  
Eduardo Barragán-Quijano
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Carbon Balance ◽  
Sowing Date ◽  
Dry Matter Accumulation ◽  
Phosphate Solubilizing Bacteria ◽  
Area Index ◽  
Use Of Resources ◽  
High Efficient ◽  
Selection Of

Multiple factors influence rice yield. Developing management practices that increase crop yield and an efficient use of resources are challenging to modern agriculture. Consequently, the aim of this study was to evaluate biological nitrogen fixation and bacterial phosphorous solubilization (biofertilization) practices with the selection of the sowing date. Three sowing dates (May, July and August) were evaluated when interacting with two mineral nutrition treatments using a randomized complete block design in a split-plot arrangement. Leaf carbon balance, leaf area index, interception and radiation use efficiency, harvest index, dry matter accumulation, nutritional status, and yield were quantified. Results showed that the maximum yield was obtained in the sowing date of August. Additionally, yield increased by 18.92% with the biofertilization treatment, reaching 35.18% of profitability compared to the local production practice. High yields were related to a higher carbon balance during flowering, which was 11.56% and 54.04% higher in August than in July and May, respectively, due to a lower night temperature. In addition, a high efficient use of radiation, which in August was 17.56% and 41.23% higher than in July and May, respectively, contributed to obtain higher yields and this behavior is related to the selection of the sowing date. Likewise, a rapid development of the leaf area index and an optimum foliar nitrogen concentration (>3%) were observed. This allowed for greater efficient use of radiation and is attributed to the activity of nitrogen-fixing and phosphate solubilizing bacteria that also act as plant growth promoters.

scholarly journals Analogy-Based Crop Yield Forecasts Based on Temporal Similarity of Leaf Area Index

2021 ◽  
Vol 13 (16) ◽  
pp. 3069
Author(s):  
Yadong Liu ◽  
Junhwan Kim ◽  
David H. Fleisher ◽  
Kwang Soo Kim
Keyword(s):  
Time Series ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Crop Yield ◽  
Satellite Data ◽  
Growing Season ◽  
Environmental Data ◽  
Area Index ◽  
Current Season ◽  
Wide Range

Seasonal forecasts of crop yield are important components for agricultural policy decisions and farmer planning. A wide range of input data are often needed to forecast crop yield in a region where sophisticated approaches such as machine learning and process-based models are used. This requires considerable effort for data preparation in addition to identifying data sources. Here, we propose a simpler approach called the Analogy Based Crop-yield (ABC) forecast scheme to make timely and accurate prediction of regional crop yield using a minimum set of inputs. In the ABC method, a growing season from a prior long-term period, e.g., 10 years, is first identified as analogous to the current season by the use of a similarity index based on the time series leaf area index (LAI) patterns. Crop yield in the given growing season is then forecasted using the weighted yield average reported in the analogous seasons for the area of interest. The ABC approach was used to predict corn and soybean yields in the Midwestern U.S. at the county level for the period of 2017–2019. The MOD15A2H, which is a satellite data product for LAI, was used to compile inputs. The mean absolute percentage error (MAPE) of crop yield forecasts was <10% for corn and soybean in each growing season when the time series of LAI from the day of year 89 to 209 was used as inputs to the ABC approach. The prediction error for the ABC approach was comparable to results from a deep neural network model that relied on soil and weather data as well as satellite data in a previous study. These results indicate that the ABC approach allowed for crop yield forecast with a lead-time of at least two months before harvest. In particular, the ABC scheme would be useful for regions where crop yield forecasts are limited by availability of reliable environmental data.

Hyperspectral characteristics and leaf area index monitoring of rice (Oryza sativa L.) under carbon dioxide concentration enrichment

2021 ◽  
Vol 54 (3) ◽  
pp. 231-243
Author(s):  
Chao Liu ◽  
Zhenghua Hu ◽  
Rui Kong ◽  
Lingfei Yu ◽  
Yuanyuan Wang ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Oryza Sativa ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Oryza Sativa L ◽  
Carbon Dioxide Concentration ◽  
Area Index

scholarly journals Measuring Canopy Geometric Structure Using Optical Sensors Mounted on Terrestrial Vehicles: A Case Study in Vineyards

Agriculture ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 208
Author(s):  
Daniel Queirós da Silva ◽  
André Silva Aguiar ◽  
Filipe Neves dos Santos ◽  
Armando Jorge Sousa ◽  
Danilo Rabino ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Optical Sensors ◽  
Precision Agriculture ◽  
Real Data ◽  
Relevant Information ◽  
Normal Operation ◽  
Area Index ◽  
Sensory Data ◽  
The Right

Smart and precision agriculture concepts require that the farmer measures all relevant variables in a continuous way and processes this information in order to build better prescription maps and to predict crop yield. These maps feed machinery with variable rate technology to apply the correct amount of products in the right time and place, to improve farm profitability. One of the most relevant information to estimate the farm yield is the Leaf Area Index. Traditionally, this index can be obtained from manual measurements or from aerial imagery: the former is time consuming and the latter requires the use of drones or aerial services. This work presents an optical sensing-based hardware module that can be attached to existing autonomous or guided terrestrial vehicles. During the normal operation, the module collects periodic geo-referenced monocular images and laser data. With that data a suggested processing pipeline, based on open-source software and composed by Structure from Motion, Multi-View Stereo and point cloud registration stages, can extract Leaf Area Index and other crop-related features. Additionally, in this work, a benchmark of software tools is made. The hardware module and pipeline were validated considering real data acquired in two vineyards—Portugal and Italy. A dataset with sensory data collected by the module was made publicly available. Results demonstrated that: the system provides reliable and precise data on the surrounding environment and the pipeline is capable of computing volume and occupancy area from the acquired data.

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