Comparison of two methods for estimating parameters of harvest index increase during seed growth

Soltani, Afshin; Galeshi, S A; Attarbashi, M.R; Taheri, Abdolhossein

doi:10.1016/j.fcr.2004.02.015

Cited by 16 publications

(5 citation statements)

References 26 publications

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“…The HI varies for different crops, with a lower value for wheat (0.37-0.43) (Huang et al, 2007;Prince et al, 2001;Soltani et al, 2004) and higher values for rice (0.42-0.47) (Prasad et al, 2006;Witt et al, 1999) and maize (0.44-0.53) (Huang et al, 2007;Prince et al, 2001). We used a value of 0.45 for the year 2000, a typical value of the three major crops: maize, rice, and wheat (Haberl et al, 2007;Sinclair, 1998).…”

Section: Varietymentioning

confidence: 99%

Estimating global cropland production from 1961 to 2010

et al. 2017

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Abstract. Global cropland net primary production (NPP) has tripled over the last 50 years, contributing 17-45 % to the increase in global atmospheric CO 2 seasonal amplitude. Although many regional-scale comparisons have been made between statistical data and modeling results, long-term national comparisons across global croplands are scarce due to the lack of detailed spatiotemporal management data. Here, we conducted a simulation study of global cropland NPP from 1961 to 2010 using a process-based model called Vegetation-Global Atmosphere-Soil (VEGAS) and compared the results with Food and Agriculture Organization of the United Nations (FAO) statistical data on both continental and country scales. According to the FAO data, the global cropland NPP was 1.3, 1.8, 2.2, 2.6, 3.0, and 3.6 PgC yr −1 in the 1960s, 1970s, 1980s, 1990s, 2000s, and 2010s, respectively. The VEGAS model captured these major trends on global and continental scales. The NPP increased most notably in the US Midwest, western Europe, and the North China Plain and increased modestly in Africa and Oceania. However, significant biases remained in some regions such as Africa and Oceania, especially in temporal evolution. This finding is not surprising as VEGAS is the first global carbon cycle model with full parameterization representing the Green Revolution. To improve model performance for different major regions, we modified the default values of management intensity associated with the agricultural Green Revolution differences across various regions to better match the FAO statistical data at the continental level and for selected countries. Across all the selected countries, the updated results reduced the RMSE from 19.0 to 10.5 TgC yr −1 (∼ 45 % decrease). The results suggest that these regional differences in model parameterization are due to differences in socioeconomic development. To better explain the past changes and predict the future trends, it is important to calibrate key parameters on regional scales and develop data sets for land management history.

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Section: Varietymentioning

confidence: 99%

Estimating global cropland production from 1961 to 2010

et al. 2017

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“…A second analysis was done to defi ne over what range during harvest-organ growth was it appropriate to consider that YGR and dHI/dt changed linearly. In previous studies (Bindi et al, 1999;Soltani et al, 2004), the ranges of linearity were determined by: 1) removing data points from the regression at the beginning and the end of harvest-organ growth periods until there was no statistical difference in the residuals between cubic and linear regression models (P > 0.05) (Bindi et al, 1999); and 2) using a three-phase segmental regression model that defi ned the range of linearity of these variables as the difference between times at which the rate of the segmental regressions changed (Soltani et al, 2004). Since data from most studies were only available from early and middle stages of the harvest-organ growth period and only a few or no points were available for the maturation period, the more complex approach to the data analysis was simplifi ed.…”

Section: Methodsmentioning

confidence: 94%

Analysis of Solanaceae Species Harvest-organ Growth by Linear Increase in Harvest Index and Harvest-organ Growth Rate

Moriondo¹,

Bindi²,

Sinclair³

2005

jashs

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Crop growth simulation models have been mainly developed to simulate final yield reliably. Thus, a main challenge in these models is the definition of a stable method for expressing the growth of harvested organs (e.g., fruit, seed, tuber, etc.). Generally, two approaches have been used: growth rate analysis of harvested organs [yield growth rate (YGR)] and analysis of harvest index (HI) increase over time (dHI/dt). This work aims to: 1) examine whether YGR and dHI/dt increase linearly over much of growing period, and 2) compare the two growth indices in terms of stability across a number of treatments, in order to identify which is the best indicator of harvest-organ growth. This analysis has already been performed for a large number of field crops, including wheat (Triticum aestivum L.), sunflower (Helianthus annuus L.), soybean [Glycine max L. (Merr.)], and pea (Pisum sativum L.), but it has never been attempted in crops where final yield is not simply seeds. In this study, YGR and dHI/dt performances for tomato (Lycopersicum esculentum Mill.), potato (Solanum tuberosum L.), and eggplant (Solanum melongena L.) were compared using 21, 18, and 4 datasets, respectively. Results indicated that both descriptors of harvest-organ growth increased linearly for most of the growth period, whilst the comparison among the two variables in terms of stability showed that, although a direct statistical test failed, dHI/dt was more suitable to describe harvest-organ growth (smaller coefficient of variability) under a large range of crop management conditions (e.g., irrigation, sowing date, planting density, and water salt concentration).

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“…Three other articles related to the heuristic role of models and reported results from experimental studies that aimed at filling knowledge gaps that were previously identified using models. The experiments and research methods were thus designed to produce new robust and generic relationships that can be used to improve models (Soltani et al, 2004, 2006; Teixeira et al, 2008).…”

Section: Status Of Ad Hoc Modeling In Studies Citing Sinclair and Selmentioning

confidence: 99%

Ad Hoc Modeling in Agronomy: What Have We Learned in the Last 15 Years?

et al. 2012

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or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. I n a famous special issue of the Agronomy Journal published in 1996, several renowned authors proposed their views on "Use and Abuse of Crop Simulation Models," aft er more than 30 yr of development of these tools in support of system approaches in agronomy. Th ese authors agreed, with varied arguments, to end with the myth of the "universal model," that is, a model expected to predict the behavior of any crop at any location under any climate and any technical management. Hence the need arose to search for compromises between at one side the mathematical and numerical properties of crop models (parameterization, uncertainty, computing time), and at the other side the scope of application, wished as broad as possible. In other words, a search for the best trade-off between the heuristic role of a model and its utility for decision-making emerged. Sinclair and Seligman proposed in their position paper (Sinclair and Seligman, 1996) which became a reference paper for the crop modeling community, several guidelines to improve "returns on the investment" in crop modeling. One of these guidelines was particularly going against the common practice at that time as they recommended "to tailor models to specifi c problems by building new models or extensively modifying old ones, instead of systematically using existing models." Such "ad hoc models" as defi ned by Sinclair and Seligman (1996), would not be overloaded by "the redundancy inherent to supposedly universal or generic crop models," and could be kept "as simple as the nature of the objective allows." Sinclair and Seligman also added that the construction of models "customized to specifi c problems" was doable thanks to the availability, already at that time, of soft ware tools designed for this purpose. Th e objective of this article is to review the fate of the idea of ad hoc modeling during the past 15 yr, and to discuss two important issues typically arising from ad hoc modeling, that is, the conceptual modeling and the computer programming tasks: (i) how to identify which components and processes of the modeled system have to be taken into account or neglected in the model, as depending on the question to be addressed using the model (model conceptualization) and (ii) how to minimize the eff orts and skills required in soft ware development for the construction of ad hoc crop models (model computerization).We fi rst recall the emergence of the concept of ad hoc modeling through revisiting the literature on crop modeling produced from the late 1980s until the special issue of the Agronomy Journal in 1996. We then review all journal articles in which Sinclair and Seligman (1996) is cited, so as to evaluate whether and how theses citations are referring to their ad hoc modeling idea. We fi nally review a broader set of recent studies tackling model conceptualization and computerization issues, with the aim of provi...

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Comparison of two methods for estimating parameters of harvest index increase during seed growth

Cited by 16 publications

References 26 publications

Estimating global cropland production from 1961 to 2010

Estimating global cropland production from 1961 to 2010

Analysis of Solanaceae Species Harvest-organ Growth by Linear Increase in Harvest Index and Harvest-organ Growth Rate

Ad Hoc Modeling in Agronomy: What Have We Learned in the Last 15 Years?

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