A New Multi-objective Approach to Optimize Irrigation Using a Crop Simulation Model and Weather History

Gasanov, Mikhail; Merkulov, Daniil; Nikitin, Artyom; Matveev, S. A.; Stasenko, Nikita; Petrovskaia, Anna; Pukalchik, Mariia; Oseledets, Ivan

doi:10.1007/978-3-030-77970-2_7

Cited by 3 publications

(4 citation statements)

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“… Irrigated agriculture is in essence manipulation of environmental resources and conditions to produce food (animal feed, fibre and biofuel) within which the crop evapotranspiration rate (ET a ) is not a prescribed input parameter but rather an output result of, inter alia, the prescribed irrigation rate ( I r ). Common sense, along the above outlined argumentation, suggests that the second, feed forward strategy (FAO56) to determine optimal, daily and seasonal irrigation rates is perhaps not the best approach to proceed with at present and that investment of plenteous efforts in research and practice to evaluate crop evapotranspiration for recommending an optimal irrigation rate ( I r ) is not always justified. Even if regarded as not up to date, perhaps returning to use, modelled or measured, yield‐seasonal irrigation rate production functions (Howell, 1990; Kaner et al, 2019; Lopez et al, 2017; Sepaskhah & Akbari, 2005; Shalhevet et al, 1979; Steduto et al, 2012), accompanied by simple profit optimization (García‐Vila & Fereres, 2012; Rodrigues & Pereira, 2009), can be constructive in many circumstances where irrigation water costs play a role in cropping economic viability. The dissemination (allocation) of the seasonal irrigation amounts (millimetre/year) to daily amounts (millimetre/day) should be based on gained, case‐specific knowledge, formulated with, for example, the concept of water production kites (Foster & Brozović, 2018; Smilovic et al, 2016), or based on the relative optimal daily irrigation rates, established with, for example, FAO56 irrigation experiments (

K_{normalc i} {ET}_{0 i} / \sum_{i} K_{normalc i} {ET}_{0 i}, K_{normalc i} {ET}_{0 i}

, millimetre/day, is the crop potential evapotranspiration at day i during the irrigation season). Fusion of monitored or historical weather data with crop models, predicting biomass accumulation and agricultural yields, can also be constructive for allocating daily irrigation amounts (Chen et al, 2020; Gasanov et al, 2021). Since the interannual variability in the irrigation season's and monthly's potential evapotranspiration is relatively small (significantly smaller than, e.g., the rainfall variability) (Kallestad et al, 2008; Lafleur et al, 2005; Zveryaev & Allan, 2010), the interannual variability of the seasonal irrigation rate (millimetre/year) evaluated with given crop coefficients is also not expected to be large.…”

Section: Discussionmentioning

confidence: 99%

“…Thus, the economic, maximum profits, optimization should be carried out on the whole, integrated irrigation decisions scheme, accounting for all other expenses. Beyond economic profits to growers, environmental constraints should also be accounted for (Assouline et al, 2015; Gasanov et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

“…• The dissemination (allocation) of the seasonal irrigation amounts (millimetre/year) to daily amounts (millimetre/day) should be based on gained, casespecific knowledge, formulated with, for example, the concept of water production kites (Foster & Brozovi c, 2018;Smilovic et al, 2016), or based on the relative optimal daily irrigation rates, established with, for example, FAO56 irrigation experiments (K ci ET 0i = P i K ci ET 0i , K ci ET 0i , millimetre/day, is the crop potential evapotranspiration at day i during the irrigation season). Fusion of monitored or historical weather data with crop models, predicting biomass accumulation and agricultural yields, can also be constructive for allocating daily irrigation amounts (Chen et al, 2020;Gasanov et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

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Is the crop evapotranspiration rate a good surrogate for the recommended irrigation rate?

Friedman

2023

Irrigation and Drainage

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Numerous methodologies and technologies of varying levels of sophistication for irrigation rate determination have been developed over the years to address limited irrigation water availability and optimize water productivity. Crop evapotranspiration is currently evaluated mostly according to the FAO56 approach, which has been augmented in recent years with remote sensing of correlated plant indices. When no other information is available, the evapotranspiration rate does give an indication of the optimal irrigation rate in some circumstances. However, in many other circumstances discussed in this short communication, replenishing the soil with crop evapotranspiration either substantially overestimates or underestimates the optimal, physiological (agronomical/economical) irrigation rate. Consequently, this note raises doubts and opens a discussion about the extensive reliance on the evaluated crop (potential or actual) evapotranspiration rate for recommending an optimal irrigation rate (irrigation dose/irrigation amount).

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K_{normalc i} {ET}_{0 i} / \sum_{i} K_{normalc i} {ET}_{0 i}, K_{normalc i} {ET}_{0 i}

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Is the crop evapotranspiration rate a good surrogate for the recommended irrigation rate?

Friedman

2023

Irrigation and Drainage

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“…Prediction of Worldwide Energy Resource (POWER) data, which is a publicly available NASA project, was used as input data for generating weather scenarios and training machine learning models. The POWER system is a common source of meteorological data in agricultural modeling, including crop yield modeling [31,32], crop disease spread modeling [33], field irrigation optimization [34], and other tasks [35,36]. The data are created by the assimilation of satellite observations into a climate model (Goddard Earth Observing System climate model) and cover the entire land surface [37].…”

Section: Weather Datamentioning

confidence: 99%

Soil Dynamics and Crop Yield Modeling Using the MONICA Crop Simulation Model and Time Series Forecasting Methods

Mirpulatov,

Gasanov,

Matveev

2023

Agronomy

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Crop simulation models are an important tool for assessing agroecosystem performance and the impact of agrotechnologies on soil cover condition. However, the high uncertainty and labor intensiveness of long-term weather forecasting limits the applicability of such models. A possible solution may be to use time series forecasting models (SARIMAX and Prophet) and artificial neural-network-based technologies (Neural Prophet). This work compares the applicability of these methods for modeling soil condition dynamics and agroecosystem performance using the MONICA simulation model for Voronic Chernozems in the Kursk region of Russia. The goal is to determine which weather indicators are most important for the yield forecast and to choose the most appropriate methods for forecasting weather scenarios for agricultural modeling. Crop rotation of soybean and sugar beet was simulated, with agricultural techniques and fertilizer usage considered as factors. We demonstrated the high sensitivity of aboveground biomass production and soil moisture dynamics to daily temperature fluctuations and precipitation during the vegetation period. The dynamics of the leaf area index and nitrate content showed less sensitivity to the daily fluctuations of temperature and precipitation. Among the proposed forecasting methods, both SARIMAX and the Neural Prophet algorithm demonstrated the ability to forecast weather to model the dynamics of crop and soil conditions with the highest degree of approximation to actual observations. For the dynamic of the crop yield of soybean, the SARIMAX model exhibited the most favorable coefficient of determination, R2, while for sugar beet, the Neural Prophet model achieved superior R2 levels of 0.99 and 0.98, respectively.

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Guava Disease Detection Using Deep Convolutional Neural Networks: A Case Study of Guava Plants

et al. 2021

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Food production is a growing challenge with the increasing global population. To increase the yield of food production, we need to adopt new biotechnology-based fertilization techniques. Furthermore, we need to improve early prevention steps against plant disease. Guava is an essential fruit in Asian countries such as Pakistan, which is fourth in its production. Several pathological and fungal diseases attack guava plants. Furthermore, postharvest infections might result in significant output losses. A professional opinion is essential for disease analysis due to minor variances in various guava disease symptoms. Farmers’ poor usage of pesticides may result in financial losses due to incorrect diagnosis. Computer-vision-based monitoring is required with developing field guava plants. This research uses a deep convolutional neural network (DCNN)-based data enhancement using color-histogram equalization and the unsharp masking technique to identify different guava plant species. Nine angles from 360∘ were applied to increase the number of transformed plant images. These augmented data were then fed as input into state-of-the-art classification networks. The proposed method was first normalized and preprocessed. A locally collected guava disease dataset from Pakistan was used for the experimental evaluation. The proposed study uses five neural network structures, AlexNet, SqueezeNet, GoogLeNet, ResNet-50, and ResNet-101, to identify different guava plant species. The experimental results proved that ResNet-101 obtained the highest classification results, with 97.74% accuracy.

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A New Multi-objective Approach to Optimize Irrigation Using a Crop Simulation Model and Weather History

Cited by 3 publications

References 30 publications

Is the crop evapotranspiration rate a good surrogate for the recommended irrigation rate?

Is the crop evapotranspiration rate a good surrogate for the recommended irrigation rate?

Soil Dynamics and Crop Yield Modeling Using the MONICA Crop Simulation Model and Time Series Forecasting Methods

Guava Disease Detection Using Deep Convolutional Neural Networks: A Case Study of Guava Plants

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