Evaluation of CropSyst for simulating the yield of flooded rice in northern Italy

https://doi.org/10.1016/j.eja.2004.12.002Get rights and content

Abstract

Research on rice cropping systems carried out in Europe has to face the great variability of pedo-climatic conditions, and the linked abundance of cultivated varieties, characteristic of the high latitudes-temperate areas where rice is traditionally grown.

Dynamic simulation models can provide an useful tool for system analysis needed to improve the knowledge, the agronomic management and crop monitoring.

For calibrate and validate CropSyst (never used for rice), a process-based simulation model, for Indica-type and Japonica-type varieties, data obtained from five field experiments, carried out in Northern Italy between 1989 and 2002, were used.

Plants were sampled during the life cycle from rice plots of five cv Loto, Cripto, Ariete, Drago, Thaibonnet and Sillaro, maintained at potential production, to determine some important crop variables and parameters such as aboveground biomass (AGB), leaf area index, specific leaf area, harvest index, the date of the main phonological stages.

At the end of the calibration process to the parameters (the others were set to the default value, taken from the Literature or measured) optimum mean daily temperature for growth, specific leaf area (for Japonica varieties), stem/leaf partition coefficient (empirical), leaf duration, were assigned the following values: 28 and 27 °C respectively for Japonica and Indica varieties, 27 and 29.5 m2 kg−1 respectively for Japonica early and medium-late varieties, 4.5, 3, 1.5 for Japonica early, medium-late and Indica varieties, 700, 850, 950 °C-days for the three groups of varieties.

The assessment of model performances has shown average RRMSEs of 20 and 22% at the end of calibration and for the validation process; the modelling efficiency is always positive and the coefficient of determination always very close to 1. General improvements will be achieved by the model by considering the thermal profile (strongly influenced by flooding water at mid latitudes) evolving in and over the canopy.

Introduction

Rice (Oryza sativa L.), the staple food of about one-half of the world population, providing 35–60% of the dietary calories consumed by 3 billion people, is arguably the most important crop worldwide. Although total rice production has more than doubled since 1965, problems about food security still persist (Cassman et al., 1997), so that it appears crucial to increase rice production through an increase in yield from rice-cultivated land because an expansion of irrigated areas probably will not be realizable (Mae, 1997).

With 3 million tonnes per year, the EU production of rough rice ranks 17th (0.5%) among main world producers, whereas with a consumption of 3.5 million tonnes (rough rice) the EU ranks only 19th. Rice is produced in very specific areas in France, Greece, Italy, Portugal and Spain. The main producers are Italy and Spain, which together represent 84% of the total area of about 400,000 ha, located in the Po Valley (mainly in the western part of it), and in the two delta Rivers areas (Ebro and Guadalquivir) in Spain. The rice cropping system tends to create everywhere a sort of “district regional structure”. Vercelli or Lomellina (Pavia Province), Camargue (France), Ebro (Spain), are clear examples of the concentration of activities, all related to only one crop so that whole the area seems strictly linked to rice from different point of views: agronomy (specialization, simplification); landscape (crop specific: “rice landscape”); natural resources (intensification of the resource use); economy; culture (traditional festivals, traditional recipes, movies). On the other hand this regional concentration and specialization can create also a high impact agricultural activity that, starting from the single field and the cropping system, can involve, at a large scale, the farming and the agricultural system of the region.

Modern rice-culture, facing both food and environmental security, requires sustainable and environmentally sound management analysis, both at farm and regional scale, integrating our knowledge of pedo-climatic conditions, crop production physiology, and agrotechniques for analysing agro-ecosystem. Dynamic simulation models could provide the technical support for analysing system for better planning, management, and monitoring.

Bouman et al. (1996) distinguished three major modeling groups: (i) the USA one in the International Benchmark Sites Network for Agrotechnology Transfer (IBSNAT) project (Uehara and Tsuji, 1993) which produced the CERES-family models; (ii) the Australian group which is developing the Agricultural Production system SIMulator (APSIM) system (McCown et al., 1995); (iii) the group working in The Netherlands at Wageningen which has developed the family of models described by Van Ittersum et al. (2003).

Both the Wageningen and the CERES approaches are very detailed in describing crop physiology and, although this level of detail is useful to draw attention to gaps in understanding, to give a help in interpreting data from field experiments in different environments (Monteith, 1996) and to study processes at the level of plant components (Confalonieri and Bechini, 2004), it's known that the calibration process becomes more complex as the number of parameters or, in general, the level of detail increases (Stöckle, 1992, Monteith, 1996). This is particularly true when large-scale simulations are needed because of the elevated number of parameters required by the larger spatial variability. Mahmood (1998) underlined that the detailed input data set required by the CERES level in simulating plant growth may be an impediment for its extensive use. For these reasons many of these models are described in the literature but very few have been used to successfully solve management problems (Monteith, 1996). Small appetite for data and simplicity are considered fundamental features of operative models also by Passioura (1996).

Moreover, CERES-Rice belongs to a family of models where each member is able to simulate only one crop behaviour, creating problems for cropping systems simulations.

The APSIM model, a farming system simulator, is not currently able to simulate rice although it has been parameterized for different crops (Keating et al., 2003).

In the last years, a model which does not belong to the groups of models described by Bouman et al. (1996) has been increasingly used. CropSyst (Stöckle and Nelson, 1999, Stöckle et al., 2003) is a process-based simulation model. It uses the same approach to simulate the growth and development of potentially all herbaceous crops. To reach this aim, simplifications have been introduced to describe some processes (e.g. monolayer canopy, constant specific leaf area (SLA), absence of daily assimilates partitioning). This makes CropSyst easier to be calibrated and a reduced set of crop parameters is needed. These aspects and the possibility of simulating rotations make CropSyst an useful tool for large-scale simulations (Confalonieri and Bechini, 2004). For these considerations, CropSyst can be considered a management-oriented model. Although it has already been applied to several crops and cropping systems (Stöckle et al., 1994, Pala et al., 1996, Donatelli et al., 1997, Stöckle and Debaeke, 1997, Giardini et al., 1998, Pannkuk et al., 1998, Confalonieri and Bechini, 2004), it is not possible to find in literature a calibrated rice parameters set or information about the technical adequacy of CropSyst for rice simulations.

In Europe more than 140 rice cultivars are grown. This is due to the high pedo-climatic and socio-economic heterogeneity which characterises the Continent. It is possible to distinguish two main groups of varieties: Japonica and Indica types. The first one refers generally to some traditional varieties, selected before the second World War and to more recent ones, selected between the 1970s and the 1990s, usually lower (semi-dwarf) and high yielding. The second group of varieties spread out in Europe mainly in the 1990s and it is characterised by slender grains required by North European market. The main part of the recent Japonica type varieties consists of medium and medium-late cultivars (the cycle is longer than 150 days), while few others are early varieties particularly useful when false sowing is needed because of red rice infestation.

Therefore, the objective of this work was to calibrate and validate CropSyst crop parameters for Indica type varieties, Japonica type early varieties and Japonica type medium-late varieties.

Section snippets

Experimental data

Experimental data were collected in five experiments (Table 1) carried out between 1989 and 2002 in northern Italy.

For experiments 1–4, daily meteorological data (rainfall, maximum and minimum air temperature and global solar radiation) were collected with automatic weather stations near the fields. For the first locality of the fifth experiment (Vignate), daily temperature data were measured with a floating hand made weather station, able to float in very shallow water bodies with a structure

Experimental results

Maximum daily temperature is usually lower than Tcutoff in the considered Region and meteorological data collected during the experiments confirm it. The average number of days in which minimum daily temperature was lower than Tbase during the rice cycle is 33 (maximum: 44 days in 1990 both for Gudo Visconti and Vercelli; minimum: 17 days in 1994 at Mortara).

Data of AGB accumulation at production level 1 are shown in Fig. 1, Fig. 2. In general, with the variety Loto (JE), lower AGB values were

Conclusions

With the present work CropSyst has been calibrated and validated for the simulation of rice in the heterogeneous cultivars and pedo-climatic conditions which characterize Italian rice cultivation. To reach this aim, the cultivars grown in Europe were grouped by defining three crop parameters sets, corresponding to Japonica early and medium-late and Indica varieties. The crop parameters were calibrated and validated by using data collected between 1989 and 2002 in north Italy, which represents

References (37)

Cited by (0)

View full text