A software component implementing a library of models for the simulation of pre-harvest rice grain quality

被引:17
|
作者
Cappelli, G. [1 ]
Bregaglio, S. [1 ]
Romani, M. [2 ]
Feccia, S. [2 ]
Confalonieri, R. [1 ]
机构
[1] Univ Milan, Dept Agr & Environm Sci Prod, Cassandra Lab, I-20133 Milan, Italy
[2] Ente Nazl Risi, Ctr Ric Riso, I-27030 Castello Dagogna, Italy
关键词
End-use value; Food security; Grain filling; Milling quality; Starch; WARM; STARCH ACCUMULATION; KERNEL DEVELOPMENT; HIGH-TEMPERATURE; AIR-TEMPERATURE; CHALKINESS; IMPACTS; YIELD;
D O I
10.1016/j.compag.2014.03.002
中图分类号
S [农业科学];
学科分类号
09 ;
摘要
Despite the availability of a variety of models to simulate crop growth and development, few operational approaches have been developed to assess pre-harvest quality of agricultural productions as a function of the conditions actually explored by the crop during the season. This represents a clear gap of knowledge researchers are trying to fill, in light of the evidences of a climate change-driven decline in the nutritional properties of important food crops. Rice represents the staple food for half of the world's population, and this explains the noticeable interest in rice grain quality, because of the direct implications on the economic value of productions, on their market destination, and on food security issues. This paper presents a framework-independent NET software library, i.e., UNIMI CropQuality, implementing models to simulate various aspects of rice quality: amylose, protein, lipids and starch content, viscosity profile, chalkiness, cracking and head rice yield. Alternate approaches for the simulation of the same quality property are included, to allow users to select the most suitable for specific modelling studies. A case study is also presented where the library was linked to the WARM rice model and used to simulate head rice yield and the percentage incidence of cracked and milky white kernels (severely chalky) for two rice varieties in the main European rice district. RRMSE ranged between 4.33% and 6.47% for head rice yield, 21.88% and 32.18% for cracking percentage, 35.92% and 55.01% for milky white chalkiness; modelling efficiency were always positive. The component, developed according to the state-of-the-art of component-oriented software development, is released with a Software Development Kit containing help and code documentation files, as well as sample applications showing how to use the library with different crop simulators. (C) 2014 Elsevier B.V. All rights reserved.
引用
收藏
页码:18 / 24
页数:7
相关论文
共 50 条
  • [1] Physical rice grain quality as affected by biophysical factors and pre-harvest practices
    Mapiemfu, D. L.
    Ndindeng, S. A.
    Ambang, Z.
    Tang, E. N.
    Ngome, F.
    Johnson, J. M.
    Tanaka, A.
    Saito, K.
    INTERNATIONAL JOURNAL OF PLANT PRODUCTION, 2017, 11 (04) : 561 - 576
  • [2] Effects of pre-harvest desiccants on rice yield and quality
    Bond, Jason A.
    Bollich, Patrick K.
    CROP PROTECTION, 2007, 26 (04) : 490 - 494
  • [3] Effects of pre-harvest chemical application on rice desiccation and seed quality
    He, Yong-qi
    Cheng, Jin-ping
    Liu, Liang-feng
    Li, Xiao-dan
    Yang, Bin
    Zhang, Hong-sheng
    Wang, Zhou-fei
    JOURNAL OF ZHEJIANG UNIVERSITY-SCIENCE B, 2015, 16 (10): : 813 - 823
  • [4] THE EFFECT OF PRE-HARVEST GLYPHOSATE APPLICATION ON GRAIN QUALITY AND VOLUNTEER WINTER WHEAT
    Jaskulski, Dariusz
    Jaskulska, Iwona
    ROMANIAN AGRICULTURAL RESEARCH, 2014, 31 : 283 - 289
  • [5] Pre-harvest desiccation in grain sorghum: physiological seed quality and effect on regrowth
    de Barros, Angelica Fatima
    Pimentel, Leonardo Duarte
    Lopes de Freitas, Francisco Claudio
    Cecon, Paulo Roberto
    Tomaz, Adriano Cirino
    Milagres Sousa, Elisangela Aparecida
    Ladeira, Leticia Milagres
    Biesdorf, Evandro Marcos
    REVISTA BRASILEIRA DE CIENCIAS AGRARIAS-AGRARIA, 2019, 14 (02):
  • [6] PRE-HARVEST FORECAST MODEL FOR RICE YIELD USING PRINCIPAL COMPONENT REGRESSION BASED ON BIOMETRICAL CHARACTER WITH R-SOFTWARE
    Kumar, Manoj
    Battan, K. R.
    Sheoran, O. P.
    INTERNATIONAL JOURNAL OF AGRICULTURAL AND STATISTICAL SCIENCES, 2019, 15 (01): : 323 - 326
  • [7] Phenotyping for resistance to pre-harvest sprouting in grain sorghum
    Veronica Rodriguez, Maria
    Joaquin Arata, Gonzalo
    Mabel Diaz, Sandra
    Renteria, Santiago
    Benech-Arnold, Roberto L.
    SEED SCIENCE RESEARCH, 2021, 31 (03) : 178 - 187
  • [8] STUDYING OF THE PHENOMENON OF PRE-HARVEST SPROUTING OF WHEAT GRAIN
    Shalakhmetova, G. A.
    Alikulov, Z. A.
    BULLETIN OF THE NATIONAL ACADEMY OF SCIENCES OF THE REPUBLIC OF KAZAKHSTAN, 2018, (03): : 234 - 243
  • [9] Pre-harvest factors influencing the quality of berries
    Di Vittori, Lucia
    Mazzoni, Luca
    Battino, Maurizio
    Mezzetti, Bruno
    SCIENTIA HORTICULTURAE, 2018, 233 : 310 - 322
  • [10] Pre-harvest Application of ReTain (Aminoethoxyvinylglycine, AVG) Influences Pre-harvest Drop and Fruit Quality of 'Williams' Pears
    Butar, Sinan
    Cetinbas, Melike
    JOURNAL OF AGRICULTURAL SCIENCES-TARIM BILIMLERI DERGISI, 2017, 23 (03): : 344 - 356