Adapting the CROPGRO Model to Simulate Alfalfa Growth and Yield

Malik, Wafa; Boote, Kenneth J.; Hoogenboom, Gerrit; Cavero, J.; Dechmi, Farida

doi:10.2134/agronj2017.12.0680

Cited by 38 publications

(24 citation statements)

References 68 publications

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“…Alfalfa cultivars grown in optimal conditions have shown SLA values ranging between 250 and 460 cm 2 g −1 [41], which is in agreement with the range of variation observed in this work, excluding the values of the diploid materials (caerulea; Table 2). SLA is a morphological trait strongly associated with alfalfa drought tolerance [36,42].…”

Section: Phenotypic Diversitysupporting

confidence: 91%

Phenotypic Diversity and Productivity of Medicago sativa Subspecies from Drought-Prone Environments in Mediterranean Type Climates

Inostroza

Espinoza

Barahona

et al. 2021

Plants

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The phenotypic diversity and productivity of a diverse alfalfa (M. sativa subspp.) panel of cultivars, landraces and wild relatives with putative drought tolerance were evaluated in two Mediterranean environments (central Chile and Southern Australia). In Chile, 70 accessions were evaluated in rainfed conditions and in Australia 30 accessions under rainfed and irrigated conditions, during three growing seasons. Large phenotypic variation was observed among and within subspecies for NDVI, stem length, intercepted PAR and forage yield. Principal component analysis indicated that the first two principal components (PC) accounted for 84.2% of total variance; fall dormancy, taxa, and breeding status were closely related to the agronomical performance of alfalfa accessions. Forage yield varied largely among accessions across years and locations. A linear relationship was found between annual forage yield and annual water added to the experiments (R2 = 0.60, p < 0.001). The GxE analysis for forage yield allowed the detection of the highest yielding accessions for each of the two mega-environments identified. The accessions CTA002 and CTA003 showed greater forage yield in both Chile and Australia environments. It is concluded that new breeding lines derived from crosses between cultivated alfalfa (M. sativa subsp. sativa) and wild relatives belonging to the primary (M. sativa subsp. falcata) and tertiary (M. arborea) gene pool, achieve outstanding agronomical performance in drought-prone environments.

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Section: Phenotypic Diversitysupporting

confidence: 91%

Phenotypic Diversity and Productivity of Medicago sativa Subspecies from Drought-Prone Environments in Mediterranean Type Climates

Inostroza

Espinoza

Barahona

et al. 2021

Plants

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“…This helped pave the way for research into ML and crop prediction [20]. In 2018, Malik et al [21] highlighted the global importance of alfalfa and demonstrated that ML techniques can be useful when predicting yields, as they adapted the CROPGRO model to predict alfalfa yields. Jing et al [22] continued this research with their 2020 adaptation of CROPGRO tailored to predicting alfalfa in Canada.…”

Section: Related Workmentioning

confidence: 99%

Using Machine Learning and Feature Selection for Alfalfa Yield Prediction

Whitmire

Vance

Rasheed

et al. 2021

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Predicting alfalfa biomass and crop yield for livestock feed is important to the daily lives of virtually everyone, and many features of data from this domain combined with corresponding weather data can be used to train machine learning models for yield prediction. In this work, we used yield data of different alfalfa varieties from multiple years in Kentucky and Georgia, and we compared the impact of different feature selection methods on machine learning (ML) models trained to predict alfalfa yield. Linear regression, regression trees, support vector machines, neural networks, Bayesian regression, and nearest neighbors were all developed with cross validation. The features used included weather data, historical yield data, and the sown date. The feature selection methods that were compared included a correlation-based method, the ReliefF method, and a wrapper method. We found that the best method was the correlation-based method, and the feature set it found consisted of the Julian day of the harvest, the number of days between the sown and harvest dates, cumulative solar radiation since the previous harvest, and cumulative rainfall since the previous harvest. Using these features, the k-nearest neighbor and random forest methods achieved an average R value over 0.95, and average mean absolute error less than 200 lbs./acre. Our top R2 of 0.90 beats a previous work’s best R2 of 0.87. Our primary contribution is the demonstration that ML, with feature selection, shows promise in predicting crop yields even on simple datasets with a handful of features, and that reporting accuracies in R and R2 offers an intuitive way to compare results among various crops.

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“…Meanwhile, independent plots of wheat (n = 4), barley (n = 7), maize-LS (4), maize-SS (n = 4) and sunflower (n = 4) data were used for model evaluation corresponding to the 2015-2016 crop season. In the case of alfalfa, Malik et al (2018) has already adapted the DSSAT model for simulating alfalfa growth and yield using a dataset collected under the same conditions as those in the current study area.…”

Section: Dssat Model Description Calibration and Evaluationmentioning

confidence: 99%

“…For the irrigation management evaluation of each representative crop in the VID, the corresponding calibrated model was applied to compare the current farmers' irrigation management methods (the real irrigation dates and depths applied by the farmers were obtained from the Almudévar Water User Association for each plot and crop season) and the optimal irrigation management scenarios. In the case of alfalfa, the field experiment performed in the VID during 2015 and 2016, as described by Malik et al (2018), was used for scenario application.…”

Section: Irrigation Scenariosmentioning

confidence: 99%

“…Moreover, the DSSAT model has been applied for a diverse variety of crops, such as wheat and barley (Jahan et al, 2018), sunflower (Ashfaq et al, 2013), and maize (Hammad et al, 2017;Yakoub et al, 2017). The most recent version of the DSSAT can also be used for more herbage crops, such as alfalfa (Malik et al, 2018). However, most of the DSSAT model evolution efforts have been conducted under experimental fields and have not used real farm plots and management practices.…”

Section: Introductionmentioning

confidence: 99%

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DSSAT modelling for best irrigation management practices assessment under Mediterranean conditions

Malik

Dechmi

2019

Agricultural Water Management

Self Cite

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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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Adapting the CROPGRO Model to Simulate Alfalfa Growth and Yield

Cited by 38 publications

References 68 publications

Phenotypic Diversity and Productivity of Medicago sativa Subspecies from Drought-Prone Environments in Mediterranean Type Climates

Phenotypic Diversity and Productivity of Medicago sativa Subspecies from Drought-Prone Environments in Mediterranean Type Climates

Using Machine Learning and Feature Selection for Alfalfa Yield Prediction

DSSAT modelling for best irrigation management practices assessment under Mediterranean conditions

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