Despite alfalfa's global importance, there is a dearth of crop simulation models available for predicting alfalfa growth and yield with its associated composition. The objectives of this research were to adapt the CSM-CROPGRO Perennial Forage Model for simulating alfalfa growth and yield and to describe model adaptation for this species. Data from six experimental plots grown under sprinkler irrigation in the Ebro valley (Northeast Spain) were used for model adaptation. Starting with parameters for Bracharia brizantha, the model adaptation was based on values and relationships reported from the literature for cardinal temperatures and dry matter partitioning. A Bayesian optimizer was used to optimize temperature effects on photosynthesis and daylength effects on partitioning and an inverse modeling technique was employed for nitrogen fixation rate and nodule growth. The calibration of alfalfa tissue composition was initiated from soybean composition analogy but was improved with values from alfalfa literature. There was considerable iteration in optimizing parameters for the processes outlined above where comparisons were made to measured data. After adaptation, the Root Mean Square Error and d-statistic of harvested herbage averaged across 58 harvests (yield range: 990-4617 kg ha -1 ) were 760 kg ha -1 and 0.75, respectively. In addition, good agreement was observed for Leaf Area Index (LAI) (LAI range: 0.1-6.7) with d-statistic of 0.71. Simulated belowground mass was within the range of literature values. The results of this study showed that CROPGRO-PFM-Alfalfa can be used to simulate alfalfa growth and development. Further testing with more extensive datasets is needed to improve model robustness.
Water is considered the most critical resource for sustainable development in Spain. Crop models can enhance water efficiency, which provides an economic advantage while also reducing environmental burdens. The aim of this study was to calibrate and evaluate the Decision Support System for Agro-technology Transfer (DSSAT) model for the major crops grown in the fields of the La Violada Irrigation District (VID), Spain; additionally, this research sought to evaluate the current practices and to determine the best irrigation management practices under different soil types in the VID for each crop. Crop and soil type data from 54 plots of farmers' fields were used for model calibration and evaluation during the 2015 and 2016 irrigation seasons. Two irrigation scenarios were applied in eight soil types in the VID based on the current irrigation applied by farmers and the optimum irrigation adjusted to crop requirement. The DSSAT model demonstrated good performance among maize, wheat, barley and sunflower crops. The evaluation of the current irrigation system showed that farmers were not managing their irrigation systems properly. The adjusted irrigation management application showed a potential reduction in the seasonal irrigation depth for maize-SS (short-season maize) (27%), maize-LS (long season maize) (18%) and sunflower (16%). In a broader context, optimum irrigation practices can reduce the amount of leached N and deep percolation losses by 31% (4.48 T) and 34% (1.2 hm 3 ), respectively, considering the cultivated crop area in each soil type in the entire VID. Additionally, the characteristics of the irrigation system, crop needs, soil properties, and atmospheric conditions must all be considered to properly schedule irrigation practices. In fact, poor timing or insufficient water application can result in crop drought stress and
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