Application of Artificial Intelligence in Agrometeorology

Abstract Replacing irrigated with rainfed crops and sustainable production of major rainfed plants (such as wheat) can be an efficient strategy to restore water resources that are drying up. Identifying plant response to climate is essential to advancing this strategy and planning for precision agriculture. Wheat is the main plant of Saqez in the Lake Urmia basin of Iran, whose yield is associated with severe fluctuations. This study was conducted to investigate the climate effect on wheat yield fluctuation. For this purpose, the method of growing degree days (GDDs) and the Zadoks scale were used to divide the wheat growth period into seven stages. Forty-seven climatic variables of the first six stages were used to do factor analysis and to develop the model for forecasting pre-harvest yield. Gene expression programming (GEP), artificial neural networks (ANNs), and multivariate linear regression (MLR) methods were applied to develop the model. The results showed that 90.7% of the total variance of 47 variables can be explained by 10 factors. Eighty-two percent of yield variations were modeled by these 10 factors (r = 0.91). The mean absolute percentage error (MAPE) for the models developed by the GEP and ANN methods was 26%, and its amount for the MLR model was 35%. In this study, for the first time, the GEP method was used to model rainfed wheat yield. Comparison with MLR and ANN methods shows that GEP is suitable for modeling in this field.

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Modeling of Durum Wheat Yield Based on Sentinel-2 Imagery

Agronomy ◽

10.3390/agronomy11081486 ◽

2021 ◽

Vol 11 (8) ◽

pp. 1486

Author(s):

Chris Cavalaris ◽

Sofia Megoudi ◽

Maria Maxouri ◽

Konstantinos Anatolitis ◽

Marios Sifakis ◽

...

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

Model Development ◽

Wheat Yield ◽

Growth Period ◽

Yield Data ◽

Water Index ◽

Normalized Difference Water Index ◽

Sentinel 2

In this study, a modelling approach for the estimation/prediction of wheat yield based on Sentinel-2 data is presented. Model development was accomplished through a two-step process: firstly, the capacity of Sentinel-2 vegetation indices (VIs) to follow plant ecophysiological parameters was established through measurements in a pilot field and secondly, the results of the first step were extended/evaluated in 31 fields, during two growing periods, to increase the applicability range and robustness of the models. Modelling results were examined against yield data collected by a combine harvester equipped with a yield-monitoring system. Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were examined as plant signals and combined with Normalized Difference Water Index (NDWI) and/or Normalized Multiband Drought Index (NMDI) during the growth period or before sowing, as water and soil signals, respectively. The best performing model involved the EVI integral for the 20 April–31 May period as a plant signal and NMDI on 29 April and before sowing as water and soil signals, respectively (R2 = 0.629, RMSE = 538). However, model versions with a single date and maximum seasonal VIs values as a plant signal, performed almost equally well. Since the maximum seasonal VIs values occurred during the last ten days of April, these model versions are suitable for yield prediction.

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A New Approach to the Identifying of the Extreme Climate Effect on the Wheat Yield Reduction in the South of European Russia

Doklady Earth Sciences ◽

10.1134/s1028334x21090075 ◽

2021 ◽

Vol 500 (1) ◽

pp. 781-786

Author(s):

E. A. Cherenkova ◽

V. A. Semenov

Keyword(s):

Wheat Yield ◽

European Russia ◽

Yield Reduction ◽

The South ◽

Climate Effect ◽

New Approach ◽

Extreme Climate ◽

South Of European Russia

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Effects of delaying chemical fallowing in pasture rotations on pasture quality and wheat yield in the Victorian Wimmera

Australian Journal of Experimental Agriculture ◽

10.1071/ea9890069 ◽

1989 ◽

Vol 29 (1) ◽

pp. 69 ◽

Cited By ~ 1

Author(s):

GJ O'Leary ◽

RM Binns ◽

TR Lewis

Keyword(s):

Dry Matter ◽

Yield Loss ◽

Wheat Yield ◽

Growth Period ◽

Poor Quality ◽

Yield Losses ◽

The Third ◽

Pasture Quality ◽

Chemical Fallow ◽

Conservation Method

The effects of delaying chemical fallowing in a pasture rotation on pasture quality and subsequent wheat yield were investigated at sites near Minyip and Charlton, Victoria, in 1983 and 1984. Three chemical fallows were commenced at different times and were compared with a conventionally cultivated fallow. The earliest chemical fallow was established, together with a conventional fallow, at the end of winter. The second chemical fallow commenced towards the end of the rapid spring growth period in mid-October (early hayfreezing), and the third in mid- November (late hayfreezing) on a grass-dominant pasture. The pasture in spring ranged from 51 to 72% digestible dry matter (DDM) but the quality declined to 42-50% DDM by the end of the fallow treatments in autumn at each site in both years. Weathering of the pasture over summer reduced it to roughage. In contrast to a conventional fallow, early hayfreezing of pasture reduced the yield of subsequent wheat crops at Minyip by 14% in 1984 and 26% in 1985. Late hayfreezing caused losses of around 35% in each year at Minyip. At Charlton yield losses were much lower with only 14% loss observed from late hayfreezing in 1985. Because the feed produced by hayfreezing was of very poor quality, hayfreezing cannot be recommended as a viable fodder conservation method as it could not adequately compensate for any yield loss.

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Soil moisture retention and rainfed wheat yield variations by the addition of gypsum and green manure

Journal of Soil Science and Environmental Management ◽

10.5897/jssem2019.0780 ◽

2020 ◽

Vol 11 (1) ◽

pp. 6-16

Author(s):

Kausar Rahina ◽

Imran Akram Muhammad ◽

Iqbal Choudhary Muhammad ◽

Malik Ayesha ◽

Rashid Zahid Abdur ◽

...

Keyword(s):

Soil Moisture ◽

Green Manure ◽

Wheat Yield ◽

Moisture Retention ◽

Rainfed Wheat ◽

Soil Moisture Retention

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Evolutionary and ecological perspectives on the wheat phenotype

Proceedings of The Royal Society B Biological Sciences ◽

10.1098/rspb.2021.1259 ◽

2021 ◽

Vol 288 (1958) ◽

pp. 20211259

Author(s):

Victor O. Sadras

Keyword(s):

Precision Agriculture ◽

Current Model ◽

Crop Improvement ◽

Wheat Yield ◽

High Yield ◽

Striking Similarity ◽

Stabilizing Selection ◽

Grain Number ◽

Offspring Number ◽

Trade Offs

Technologies, from molecular genetics to precision agriculture, are outpacing theory, which is becoming a bottleneck for crop improvement. Here, we outline theoretical insights on the wheat phenotype from the perspective of three evolutionary and ecologically important relations—mother–offspring, plant–insect and plant–plant. The correlation between yield and grain number has been misinterpreted as cause-and-effect; an evolutionary perspective shows a striking similarity between crop and fishes. Both respond to environmental variation through offspring number; seed and egg size are conserved. The offspring of annual plants and semelparous fishes, lacking parental care, are subject to mother–offspring conflict and stabilizing selection. Labile reserve carbohydrates do not fit the current model of wheat yield; they can stabilize grain size, but involve trade-offs with root growth and grain number, and are at best neutral for yield. Shifting the focus from the carbon balance to an ecological role, we suggest that labile carbohydrates may disrupt aphid osmoregulation, and thus contribute to wheat agronomic adaptation. The tight association between high yield and low competitive ability justifies the view of crop yield as a population attribute whereby the behaviour of the plant becomes subordinated within that of the population, with implications for genotyping, phenotyping and plant breeding.

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Relationship between Spatial Variability Pattern of Wheat Yield and Soil Properties

Asian Soil Research Journal ◽

10.9734/asrj/2018/v1i1626 ◽

2018 ◽

pp. 1-14

Author(s):

Alidad Karami ◽

Sadegh Afzalinia

Keyword(s):

Spatial Structure ◽

Soil Properties ◽

Soil Ph ◽

Precision Agriculture ◽

Interpolation Method ◽

Irrigation System ◽

Wheat Yield ◽

Gaussian Model ◽

Variogram Model ◽

Clay Percentage

Aims: Determining effects of spatial variation of some soil properties on wheat quantity and quality variation in order that proper soil and inputs management can be applied for sustainable wheat production. Study Design: Analyzing data of a field with center pivot irrigation system and uniform management using the geostatistical method. Place and Duration of Study: Soil and Water Research Department, Fars Agricultural and Natural Resources Research and Education Center, Darab, Iran, from September 2013 to February 2014. Methodology: Wheat yield data harvested by class lexion 510 combine from 25 m2 plots (11340 locations) with the corresponding geographical location were used. Besides, soil properties and wheat yield were measured at 36 randomly selected points on the field. Interpolation of parameters was predicted with the best semi-variogram model using kriging, inverse distance weighted (IDW), and cokriging methods. Results: Results showed that wheat yield varied from 2 to 10.08 tons per hectare. Cokriging with cofactor of kernel weight interpolator had more accuracy compared to the combine default interpolator (kriging). A logical, linear correlation was found between different parameters. The best variogram model for pH, OC, and ρb was exponential, for EC, TNV, SP, soil silt and clay percentage was spherical, and for soil, percentage sand was Gaussian model. Data of soil sand, silt, and clay percentage, EC, TNV, and SP had strong spatial structure, and soil pH, OC, and ρb had moderate spatial structure. The best interpolation method for soil pH, EC, sand and silt percentage was kriging method; while, for TNV, SP, OC, ρb, and clay percentage was IDW. Conclusion: There was a close relationship between wheat yield variation and changes in the soil properties. Soil properties and wheat yield distribution maps provided valuable information which could be used for wheat yield improvement in precision agriculture.

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Mapping pre-harvest sprouting resistance loci in AAC Innova × AAC Tenacious spring wheat population

BMC Genomics ◽

10.1186/s12864-021-08209-6 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Raman Dhariwal ◽

Colin W. Hiebert ◽

Mark E. Sorrells ◽

Dean Spaner ◽

Robert J. Graf ◽

...

Keyword(s):

Candidate Genes ◽

Wheat Yield ◽

Seed Viability ◽

Pedigree Analysis ◽

Resistance Qtls ◽

Wheat Population ◽

End Use ◽

Sprouting Resistance ◽

First Time ◽

Chromosome 3B

Abstract Background Pre-harvest sprouting (PHS) is a major problem for wheat production due to its direct detrimental effects on wheat yield, end-use quality and seed viability. Annually, PHS is estimated to cause > 1.0 billion USD in losses worldwide. Therefore, identifying PHS resistance quantitative trait loci (QTLs) is crucial to aid molecular breeding efforts to minimize losses. Thus, a doubled haploid mapping population derived from a cross between white-grained PHS susceptible cv AAC Innova and red-grained resistant cv AAC Tenacious was screened for PHS resistance in four environments and utilized for QTL mapping. Results Twenty-one PHS resistance QTLs, including seven major loci (on chromosomes 1A, 2B, 3A, 3B, 3D, and 7D), each explaining ≥10% phenotypic variation for PHS resistance, were identified. In every environment, at least one major QTL was identified. PHS resistance at most of these loci was contributed by AAC Tenacious except at two loci on chromosomes 3D and 7D where it was contributed by AAC Innova. Thirteen of the total twenty-one identified loci were located to chromosome positions where at least one QTL have been previously identified in other wheat genotype(s). The remaining eight QTLs are new which have been identified for the first time in this study. Pedigree analysis traced several known donors of PHS resistance in AAC Tenacious genealogy. Comparative analyses of the genetic intervals of identified QTLs with that of already identified and cloned PHS resistance gene intervals using IWGSC RefSeq v2.0 identified MFT-A1b (in QTL interval QPhs.lrdc-3A.1) and AGO802A (in QTL interval QPhs.lrdc-3A.2) on chromosome 3A, MFT-3B-1 (in QTL interval QPhs.lrdc-3B.1) on chromosome 3B, and AGO802D, HUB1, TaVp1-D1 (in QTL interval QPhs.lrdc-3D.1) and TaMyb10-D1 (in QTL interval QPhs.lrdc-3D.2) on chromosome 3D. These candidate genes are involved in embryo- and seed coat-imposed dormancy as well as in epigenetic control of dormancy. Conclusions Our results revealed the complex PHS resistance genetics of AAC Tenacious and AAC Innova. AAC Tenacious possesses a great reservoir of important PHS resistance QTLs/genes supposed to be derived from different resources. The tracing of pedigrees of AAC Tenacious and other sources complements the validation of QTL analysis results. Finally, comparing our results with previous PHS studies in wheat, we have confirmed the position of several major PHS resistance QTLs and candidate genes.

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