scholarly journals Parameter Sensitivity and Uncertainty of Radiation Interception Models for Intercropping System

2020 ◽  
Vol 27 (3) ◽  
pp. 437-456
Author(s):  
Wenzhi Zeng ◽  
Yuchao Lu ◽  
Amit Kumar Srivastava ◽  
Thomas Gaiser ◽  
Jiesheng Huang
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Suitable Model ◽  
Area Index ◽  
Radiation Interception ◽  
Sensitivity Indices ◽  
Intercepted Radiation ◽  
Main Effects ◽  
Sensitive Parameters ◽  
Model Structures

AbstractEstimating the interception of radiation is the first and crucial step for the prediction of production for intercropping systems. Determining the relative importance of radiation interception models to the specific outputs could assist in developing suitable model structures, which fit to the theory of light interception and promote model improvements. Assuming an intercropping system with a taller and a shorter crop, a variance-based global sensitivity analysis (EFAST) was applied to three radiation interception models (M1, M2 and M3). The sensitivity indices including main (Si) and total effects (STi) of the fraction of intercepted radiation by the taller (ftaller), the shorter (fshorter) and both intercrops together (fall) were quantified with different perturbations of the geometric arrangement of the crops (10-60 %). We found both ftaller and fshorter in M1 are most sensitive to the leaf area index of the taller crop (LAItaller). In M2, based on the main effects, the leaf area index of the shorter crop (LAIshorter) replaces LAItaller and becomes the most sensitive parameter for fshorter when the perturbations of widths of taller and shorter crops (Wtaller and Wshorter) become 40 % and larger. Furthermore, in M3, ftaller is most sensitive to LAItaller while fshorter is most sensitive to LAIshorter before the perturbations of geometry parameters becoming larger than 50 %. Meanwhile, LAItaller, LAIshorter, and Ktaller are the three most sensitive parameters for fall in all three models. From the results we conclude that M3 is the most plausible radiation interception model among the three models.

Canopy Development, Light Interception and Seed Production in Sunflower as Influenced by Temperature and Radiation

1984 ◽  
Vol 11 (4) ◽  
pp. 255 ◽  
Author(s):  
HM Rawson ◽  
RL Dunstone ◽  
MJ Long ◽  
JE Begg
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Seed Production ◽  
Harvest Index ◽  
Growth Patterns ◽  
Light Extinction ◽  
Seed Number ◽  
Area Index ◽  
Radiation Interception ◽  
Temperature Regimes

Well watered mini-crops of sunflower were grown either in summer or winter in glasshouses maintained under five temperature regimes and a 16 h photoperiod. A field crop was grown concurrently with the summer glasshouse study. Summer radiation (25.5 MJ day-1) increased the size and/or number of many of the variables measured compared with winter radiation (9.5 MJ day-1). However, there was interaction between light and temperature upon phenological development, plant height, leaf number and harvest index. Seed production declined at temperatures above 18/13°C in summer and above 24/19°C in winter radiation, but fatty acid composition of the seed oil changed progressively with increasing temperature and was unaffected by radiation. Leaf area per plant increased faster under summer than winter radiation and in almost all temperature regimes reached considerably higher final values which resulted in a greater percentage of the incident radiation being intercepted. Temperature, though affecting the growth patterns and final areas of individual leaves in the canopies, did not alter the relationship between leaf area index and radiation interception. The light extinction coefficient changed with leaf area index and differed between summer and winter. Biomass per plant at maturity (B, g) was best related to radiation interception up to anthesis (I, MJ m-2), such that B = -234 + 541ogl, r2 = 0.91, but seed number (S) was correlated similarly with radiation interception and with the number of degree days (D) accumulated between floral initiation and anthesis (S = 1137+ 0.0051-0.762D, R2 = 0.90). Yield (Y, g per plant) was dependent on seed number, mean temperature (T) and radiation intercepted between anthesis and maturity, and the leaf area present at anthesis. However, over 97% of the variation in yield could be accounted for by the temperature and radiation factors in the manner Y = 39.07+0.047I- 1.26T. Harvest index and yield were not correlated for the cultivar examined.

Radiation interception and leaf area index from foliage cover in diploid potato

2020 ◽  
Vol 112 (4) ◽  
pp. 2805-2811
Author(s):  
Tatiana M. Saldaña‐Villota ◽  
José Miguel Cotes‐Torres
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Diploid Potato ◽  
Area Index ◽  
Radiation Interception

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.

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