Developing perennial fruit crop models in APSIM Next Generation using grapevine as an example

Abstract

A new model for grapevines (Vitis vinifera L.) is the first perennial fruit crop model using the Agricultural Production System sIMulator (APSIM) Next Generation framework. Modules for phenology, light interception, carbohydrate allocation, yield formation and berry composition were adapted or added into APSIM Next Generation to represent the nature of fruit-bearing vines. The simulated grapevine phenological cycle starts with the dormancy phase triggered by a critical photoperiod in autumn, and then goes through the subsequent phenophases sequentially and finally returns to dormancy for a new cycle. The canopy microclimate module within APSIM Next Generation was extended to allow for row crop light interception. The carbohydrate arbitrator was enhanced to consider both sink strength and sink priority to reflect carbohydrate reserve as a concurrent competing sink. Weather conditions and source-sink ratio at critical developmental stages were used to determine potential grapevine yield components e.g., bunch number, berry number and berry fresh weight. The model was calibrated and tested extensively using four detailed datasets. The model captured the variations in the timing of measured budburst, flowering and véraison over 15 seasons across New Zealand for five different varieties. The calculated seasonal dynamics of light interception by the row and alley were consistent with field observations. The model also reproduced the dynamics of dry matter and carbohydrate reserve of different organs, and the wide variation in yield components caused by seasonal weather conditions and pruning regimes. The modelling framework developed in this work can also be used for other perennial fruit crops.

Rootstock‐conferred traits affect the water use efficiency of fruit production in Shiraz

“…As the canopy is the site of photosynthesis, it is canopy size rather than PM that is likely a more direct measure of vine's ability to support fruit production (Miller and Howell 1998, Kliewer and Dokoozlian 2005), but the Ravaz Index is likely a better measure of the total relative investment of assimilation in fruit relative to shoot. Control of this relative investment is commonly modelled by varying sink strength (Mirás‐Avalos et al 2018, Zhu et al 2021). As the scion genetics were the same for all of the vines in this study, the difference in Ravaz Index between rootstocks could potentially be inherent in the scion when growth is restricted by whatever mechanism rootstocks achieve reduced vigour due to a higher sink strength of the fruit.…”

Towards a Stochastic Model to Simulate Grapevine Architecture: A Case Study on Digitized Riesling Vines Considering Effects of Elevated CO2

“…In order to account for non-linear responses of growth and development to ambient temperatures, we decided to use the non-linear method introduced by Wang and Engel (Equation ( 6 ) [ 29 ]) and recently applied for grapevine by Zhu et al (Equations ( 3 )–( 4 ) [ 28 ]). Non-linearity in temperature response is achieved by calculating the hourly development day contribution ( ) based on a beta-distribution-like function depending on the average hourly air temperature ( ) and three additional parameters.…”

“…These phenological models are based on growing-degree-days approaches or equivalents, thus relying on temperature response functions that attribute a development contribution to each day depending on daily or hourly summary measures of local air temperatures. Calibration then includes the estimation of threshold temperatures and the necessary sum of thermal development contribution, i.e., GDD, to reach a specific stage or to transfer from one stage to another [ 25 , 28 ]. Research on phenological models includes considerations of various shapes of the response function and on the resolution of input data [ 24 , 25 , 29 , 30 ], where sub-daily temperatures and non-linear responses that have also been proven beneficial in crop modeling [ 31 ] become more prominent in grapevine phenology modeling, too.…”

“…Research on phenological models includes considerations of various shapes of the response function and on the resolution of input data [ 24 , 25 , 29 , 30 ], where sub-daily temperatures and non-linear responses that have also been proven beneficial in crop modeling [ 31 ] become more prominent in grapevine phenology modeling, too. [ 28 , 30 , 32 , 33 ]. Thus, usually more parameters need to be estimated which is sometimes counteracted by limiting the search space by fixing parameters to reasonable values [ 19 , 28 , 32 ].…”

“…[ 28 , 30 , 32 , 33 ]. Thus, usually more parameters need to be estimated which is sometimes counteracted by limiting the search space by fixing parameters to reasonable values [ 19 , 28 , 32 ]. To consider different grapevine varieties either an entire calibration for each variety is used [ 28 ] or universally applicable temperature thresholds are combined with variety-specific accumulated development contributions [ 24 ].…”