Valentina Pagani

Uncertainty in crop model predictions

Crop models are used to estimate crop productivity under future climate projections, and modellers manage uncertainty by considering different scenarios and GCMs, using a range of crop simulators. Five crop models and 20 users were arranged in a randomized block design with four replicates. Parameters for maize (well studied by modellers) and rapeseed (almost ignored) were calibrated. While all models were accurate for maize (RRMSE from 16.5% to 25.9%), they were, to some extent, unsuitable for rapeseed. Although differences between biomass simulated by the models were generally significant for rapeseed, they were significant only in 30% of the cases for maize. This could suggest that in case of models well suited to a crop, user subjectivity (which explained 14% of total variance in maize outputs) can hide differences in model algorithms and, consequently, the uncertainty due to parameterization should be better investigated. Five crop models and 20 users were arranged in four randomized blocks.The significance of model factor for maize and rapeseed was evaluated.All models achieved good performance for maize and poor for rapeseed.Differences between models were significant only in 30% of the cases for maize.Parameterization uncertainty should be explicitly managed also in model ensembles.

Downstream Services for Rice Crop Monitoring in Europe: From Regional to Local Scale

The ERMES agromonitoring system for rice cultivations integrates EO data at different resolutions, crop models, and user-provided in situ data in a unified system, which drives two operational downstream services for rice monitoring. The first is aimed at providing information concerning the behavior of the current season at regional/rice district scale, while the second is dedicated to provide farmers with field-scale data useful to support more efficient and environmentally friendly crop practices. In this contribution, we describe the main characteristics of the system, in terms of overall architecture, technological solutions adopted, characteristics of the developed products, and functionalities provided to end users. Peculiarities of the system reside in its ability to cope with the needs of different stakeholders within a common platform, and in a tight integration between EO data processing and information retrieval, crop modeling, in situ data collection, and information dissemination. The ERMES system has been operationally tested in three European rice-producing countries (Italy, Spain, and Greece) during growing seasons 2015 and 2016, providing a great amount of near-real-time information concerning rice crops. Highlights of significant results are provided, with particular focus on real-world applications of ERMES products and services. Although developed with focus on European rice cultivations, solutions implemented in the ERMES system can be, and are already being, adapted to other crops and/or areas of the world, thus making it a valuable testing bed for the development of advanced, integrated agricultural monitoring systems.

New multi-model approach gives good estimations of wheat yield under semi-arid climate in Morocco

Wheat production in Morocco is crucial for economy and food security. However, wheat production is difficult because the semi-arid climate causes very variable wheat yields. To solve this issue, we need better prediction of the impact of drought on wheat yields to adapt cropping management to the semi-arid climate. Here, we adapted the models WOFOST and CropSyst to agro-climatic conditions in Morocco. Six soft and durum wheat varieties were grown during the 2011–2012 and 2012–2013 growing seasons in the experimental sites of Sidi El Aydi, Khemis Zemamra and Marchouch. Drip irrigation and rainfed treatments were arranged in a randomised-block design with three replicates. We determined the phenological stages of emergence, tillering, stem elongation, flowering and maturity. We measured aboveground biomass six times along the season. These data were used to adapt WOFOST and CropSyst to local conditions. Our results show that both models achieved good estimations, with R2 always higher than 0.91, and positive values for Nash and Sutcliffe modelling efficiencies. Results of spatially distributed simulations were then analysed for the whole country in terms of different response to drought.

Reimplementation and reuse of the Canegro model

The DSSAT-Canegro model was re-implemented in a framework-independent component.This reimplementation enhances model reuse and extension.A new model for giant reed simulation was developed extending the Canegro component.The new model succeeded in simulating giant reed in different agronomic conditions. Model reuse can be limited by software design, which often forces third parties to completely rewrite new versions of existing models before adapting them to new needs. This tendency removes resources from the improvement of models and from the extension of their domain, leading to the proliferation of software tools representing a variety of different implementations of the same algorithms. The component-oriented paradigm allows these limitations to be overcome, facilitating model reuse and extension. This study presents the application of component-oriented principles to the reimplementation of the sugarcane (Saccharum officinarum L.) model Canegro (DSSAT v4.5) in a framework-independent component following the BioMA architecture. The potential for reuse and extension of the component (UNIMI.Cassandra.CaneML.Canegro) is here demonstrated by its straightforward adaptation to the simulation of giant reed (Arundo donax L.), a promising energy crop that shares several morphological and physiological features with sugarcane. The new component, named UNIMI.Cassandra.CaneML.Arungro, extends UNIMI.Cassandra.CaneML.Canegro, and was effectively developed by inheriting about 70% of the sugarcane model code. The development, calibration and evaluation of the giant reed model were performed using field data collected in two experimental sites in Northern Italy between 2009 and 2012. Model performances were satisfactory, with average relative root mean square error and modelling efficiency for aboveground biomass simulation of 34.33% and 0.57, respectively. The Canegro component is distributed via a Software Development Kit that includes documentation of code and algorithms, and the source code of sample applications illustrating how to use it.

Estimating Leaf Area Index (LAI) in Vineyards Using the PocketLAI Smart-App

Estimating leaf area index (LAI) of Vitis vinifera using indirect methods involves some critical issues, related to its discontinuous and non-homogeneous canopy. This study evaluates the smart app PocketLAI and hemispherical photography in vineyards against destructive LAI measurements. Data were collected during six surveys in an experimental site characterized by a high level of heterogeneity among plants, allowing us to explore a wide range of LAI values. During the last survey, the possibility to combine remote sensing data and in-situ PocketLAI estimates (smart scouting) was evaluated. Results showed a good agreement between PocketLAI data and direct measurements, especially for LAI ranging from 0.13 to 1.41 (R2 = 0.94, RRMSE = 17.27%), whereas the accuracy decreased when an outlying value (vineyard LAI = 2.84) was included (R2 = 0.77, RRMSE = 43.00%), due to the saturation effect in case of very dense canopies arising from lack of green pruning. The hemispherical photography showed very high values of R2, even in presence of the outlying value (R2 = 0.94), although it showed a marked and quite constant overestimation error (RRMSE = 99.46%), suggesting the need to introduce a correction factor specific for vineyards. During the smart scouting, PocketLAI showed its reliability to monitor the spatial-temporal variability of vine vigor in cordon-trained systems, and showed a potential for a wide range of applications, also in combination with remote sensing.

Assimilating seasonality information derived from satellite data time series in crop modelling for rice yield estimation

The agricultural sector is facing important global challenges due to the pressure of food demand, increased price-competition produced by market globalization and food price volatility (G20 Agriculture Action Plan), and the necessity of more environmentally and economically sustainable farming. Earth Observation (EO) systems can significantly contribute to these topics by providing reliable real time information on crop distribution, status and seasonal dynamics. ERMES FP7 project aims to create added-value information for the rice agro-sector by integrating EO-products in crop models. Time series of moderate resolution satellite data are analyzed exploiting the PhenoRice algorithm to retrieve seasonal occurrence of agro-practices and phenological stages. Eleven years (2003-2013) of rice seasonal metrics were derived and used in WARM crop model to set up a crop forecasting systems, with the aim to provide crop yield estimates for regional authorities. Preliminary test conducted in Italy on indica rice ecotype demonstrated that the system can provide rice yield estimates explaining up to 90% of interannual variability.