Agrometeorological Data Research Articles

Perceived capacities of public extension personnel for climate information dissemination to farmers in Cross River State, Nigeria

The study examined the perceived capacities of public extension personnel for climate information dissemination to farmers in Cross River State, Nigeria. A multistage sampling procedure was used to select 72 extension agents. Data were collected using structured questionnaire and were analysed using a multinomial logistic regression at 5% level of significance. Some of the climate information disseminated were information on appropriate measures to take to prevent water shortage due to adverse weather conditions (75%) and early warning signs on events such as drought, flooding etc. (70.8%). Radio and/or television (68.1%), and the Ministries of Agriculture and Natural Resources (68.1%) were the main sources of climate information. Respondents perceived that they had low capacities for producing and airing radio and television messages on climate change (15.3%) and carrying out impact analysis of climate change effects on farmers (18%). Results of the multinomial logistic regression analysis showed that years of experience (x2 =6.244; p = 0.044); and highest educational qualification (x2 =11.021; p = 0.088) had positively significant effects on capacity to disseminate climate information. Extension staff should be encouraged to go for specialized studies on climate change and be trained to interpret and disseminate agro-meteorological data and information.

New estimation model for paddy soil temperature in early growth stage, and its application to estimate nitrogen mineralization of organic fertilizer

ABSTRACT Paddy soil temperatures (Ts) are required to evaluate the mineralization patterns of organic fertilizers in rice paddies. We have developed an estimation method for Ts based on the heat balance model using Agro-Meteorological Grid Square Data (AMD) with a horizontal resolution of 1 km × 1 km. The paddy water temperature (Tw) (daily mean, assuming leaf area index LAI = 0) was first calculated from the AMD using the heat balance equation. Next, the daily mean Ts was evaluated using the empirical relationship between Ts and Tw. The value of Ts can be expressed as a linear function of a time series for Tw for up to 3 days prior. At five test sites in Niigata prefecture, Japan, the mean difference and root mean square errors between the estimated and measured Ts ranged from −0.54°C to 0.77°C (mean: 0.00°C) and from 0.53°C to 1.21°C (mean: 0.86°C), respectively. Overall, the results using the estimated Ts were found to agree with the observations for each field. Using the developed Ts estimation method and the nitrogen (N) mineralization parameters of organic fertilizers, we estimated the N mineralization patterns of four fertilizers at five test sites. The results show a good agreement with the measured values. This suggests that the proposed Ts estimation method in conjunction with the AMD can be utilized to estimate the N mineralization of organic fertilizers applied in paddy fields in Niigata prefecture.

Rice-Fusion: A Multimodality Data Fusion Framework for Rice Disease Diagnosis

Rice leaf infections are a common hazard to rice production, affecting many farmers all over the world. Early detection and treatment of rice leaf infection are critical for promoting healthy rice plant growth and ensuring adequate supply for the fast-growing population. Computer-assisted rice leaf disease diagnoses are hampered due to strong image backgrounds. Popular Convolutional Neural Network (CNN) architecture extracts the features from images and diagnoses the disease to address the issues above. However, this method is best suitable for segmented images and gives low accuracy with real-time images. In this case, the Internet of Things is a paradigm shift that collects agro-meteorological information that effectively helps diagnose rice diseases. Motivated by the usefulness of CNN models and agricultural IoT, a novel multimodal data fusion framework named Rice-Fusion is proposed to diagnose rice disease. Rice disease diagnosis based on a single modality may not be accurate, and hence the fusion of heterogeneous modalities is essential for robust and reliable disease diagnosis. This gives a new dimension to the domain of rice disease diagnosis. The dataset was collected manually with 3200 rice health category samples using two modalities, namely agro-meteorological sensors and a camera. The Rice-Fusion framework initially extracts the numerical features from agro-meteorological data collected from sensors. Next, it extracts the visual features from the captured rice images. These extracted features are further fused using a concatenation layer followed by a dense layer, which provides single output for diagnosing the rice disease. The testing accuracy of Rice-Fusion is 95.31% as opposed to other unimodal framework accuracies of 82.03% and 91.25% based on CNN and Multi-Layer Perceptron (MLP) architectures, respectively. Experimental results analysis demonstrates that the proposed Rice-Fusion multimodal data fusion framework outperforms the outcome of unimodal frameworks.

A feature of organization the regional digital network of agrometeorological observations in the Tomsk region

The article describes the organization of a digital network for collection and analysis of agrometeorological data. This digital network is based on the concept of an integrated approach to the collection, systematization and analysis of agrometeorological data for forecasting and developing recommendations for agricultural producers. The digital network is three-tiered and consists of meteorological stations, agrometeorological posts and agrometeorological probes installed directly on the fields. The presented modern network for monitoring meteorological parameters is an important tool of modern agricultural production, which is especially relevant in a dynamically changing climate. It is reasonable to build such systems in the ideology of the Internet of Things (IoT).

Orchard Level Assessment of Irrigation Performance and Water Productivity of an Irrigation Community in Eastern Spain

Over the last three decades, a great investment effort has been made in the modernization of irrigation in the Valencian Community (Spain). The initial change from distribution networks to pressurized ones and the shift towards drip irrigation systems was followed by improvements in irrigation scheduling, based on agrometeorological data, soil water content sensors, and remote sensing. These improvements are considered adequate for increasing irrigation water use efficiency, but it is difficult to find systematic measurements to assess its impacts on irrigation adequacy along with irrigation productivity in fruit orchards. This work presents the results of a four year assessment of irrigation water and energy use efficiency along with water productivity of a recently established irrigation community in the province of Valencia (Spain). The study was carried out at the orchard level and focused on two fruit crops: persimmon and peach trees. Six irrigation performance indicators, relative water supply (RWS), relative irrigation supply (RIS), yield performance (Yp), global water productivity (WPoverall), output per unit irrigation water (OUI), and the percent of nitrogen fertilization obtained by irrigation water, were defined and calculated for years 2017 to 2020 in 104 persimmon and peach orchards. The results showed that most of the farmers irrigated below the crop water requirements, showing RWS and RIS values less than 1, and there was great variability among farmers, especially in WPoverall and OUI indicators.

Quantifying climate-induced drought risk to livelihood and mitigation actions in Balochistan.

Climate change-induced disasters show the highest risk for agriculture and livelihoods in rural areas of developing countries. Due to changing rainfall pattern, the arid and semiarid region of Pakistan faces frequent droughts. Farming communities affected by drought disasters are causing serious threats to livelihood, global food crises, environmental migration, and sustainable development. The existing study was designed to quantify two key components through (1) analysis of agrometeorological data (1981–2017) with exploratory data analysis and Mann–Kendall trend analysis; (2) extensive field survey (200 households). The multivariate probit model has been run to detect determinants of coping and adaptive strategies by farmers. Our results showed that the farmers supposed that temperature and rainfall were highly fluctuating in recent years equally. Farmers adopted different coping and adaptive measures that include crop diversification, input adjustment, water management, asset depletion, income diversification, and migration to sustain their livelihood during stress periods. The agrometeorological data analysis revealed that the agricultural vulnerability to drought risks increased significantly, and the survey results projected that 64.7% of the population is exposed to drought directly or indirectly. Sen’s slope quantification resulted in (0.025 °C) rise in temperature, (− 2.936 mm) decline in rainfall year−1. Modeling future scenarios resulted in an increase in temperature up to 0.7 °C, 1.2 °C, and a decrease in precipitation up to 161.48 mm, 103.5 mm in 2040 and 2060. The study evaluated a huge gap in the provision of drought risk resilience services, crop insurance, and climate-smart training practices to build capacities among farmers to cope with the impacts of extreme weather conditions. Our research might provide the groundwork to upgrade actions to drought prevention and early warning in water scarcity areas.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11069-021-04913-4.

Methodological approach for the implementation of a remote management system for large-scale irrigation

Remote management is a modular and effective approach that allows for remote control and monitoring of irrigation systems and hydro-mechanical facilities that are spatially separated, while also tracking real-time irrigation data such as flow, pressure, electric pump groups, solar panels, and agro-meteorological data. Control instrumentation and automation equipment indicate the various defects that may occur on the hydro-mechanical equipment, which can upset the normal functioning of the structures of the irrigation system. Identifying the level of operation of the remote management system is an essential step to understand better the functionality of the remote control and monitoring system and define the objectives to be met and the technical specifications necessary for its implementation. This article will present a methodological approach for the implementation of a remote management system for large-scale irrigation. The proposed methodological approachfocuses on improving the hydraulic parameters of pumping stations and irrigation hydrants and decreasing pumping station electricity consumption.

Wheat Crop Yield Estimation using Geomatics Tools in Saharanpur District

Background: For a primarily agriculture-based country like India, reliable, accurate and timely information on types of crops grown, their acreages and yield are of vital importance. In this study, attempts have been made to predict wheat crop yield in Saharanpur district of Uttar Pradesh for the year 2016-17 using various spectral indices (Normalized Difference Vegetation Index, Soil Adjusted Vegetation Index, Ratio Vegetation Index and Transformed Normalized Difference Vegetation Index) and agrometeorological variables. Methods: The spectral indices were derived using agrometeorological data (Growing Degree Days, Temperature Difference and Helio Thermal Units) and the ground truth information was collected using the Global Positioning System in the field. Observed acreage data for the wheat crop were collected from the Saharanpur district headquarter. Result: The NDVI showed the highest correlation with wheat crop yield. The spectral yield model using only the NDVI showed an R2 value of 0.81 with an RMSE of 88.02 kg/ha. However, by incorporating the NDVI with temperature, the model shows much better performance with an R2 value of 0.95 and an RMSE of 49.86 kg/ha. The rainfall showed lesser correlation with the wheat crop yield as compared to the temperature, probably due to ground water dominated region.

CROP MODELING WITH LESS DATA: THE FAO MODEL FOR SOYBEAN YIELD ESTIMATION

Crop growth simulation models such as WOFOST and DSSAT are useful, but require several inputs that sometimes are not available, especially in developing areas. In addition, measured data is usually time and labor-intensive. In search of faster and easier methods for soybean estimates, this study presents a lower input requiring methodology for yield estimation. This study combines the FAO-33 yield model with the agro-ecological zone approach for soybean yield estimations using mostly indirect data. Sowing and harvest dates and yield were collected from 74 soybean commercial farms. Agrometeorological data from the European Centre for Medium-Range Weather Forecasts (ECMWF) were used. Fifty farms (66%) were used to calibrate the model and 24 farm areas (33%) were used for evaluation purposes. Two methodologies (FAO-56 and Thornthwaite and Mather) for water balance and actual evapotranspiration (ETa) estimations were used. The comparison of yield estimations and observations showed that the use of low data input to obtain reasonable accuracy, with a mean error of −310 kg ha−1 and a mean absolute percentage error of 23.3%.

Time Series Forecasting of Univariate Agrometeorological Data: A Comparative Performance Evaluation via One-Step and Multi-Step Ahead Forecasting Strategies.

High-frequency monitoring of agrometeorological parameters is quintessential in the domain of Precision Agriculture (PA), where timeliness of collected observations and the ability to generate ahead-of-time predictions can substantially impact the crop yield. In this context, state-of-the-art internet-of-things (IoT)-based sensing platforms are often employed to generate, pre-process and assimilate real-time data from heterogeneous sensors and streaming data sources. Simultaneously, Time-Series Forecasting Algorithms (TSFAs) are responsible for generating reliable forecasts with a pre-defined forecast horizon and confidence. These TSFAs often rely on modelling the correlation between endogenous variables, the impact of exogenous variables on latent form and structural properties of data such as autocorrelation, periodicity, trend, pattern, and causality to approximate the model parameters. Traditionally, TSFAs such as the Holt–Winters (HW) and Autoregressive family of models (ARIMA) apply a linear and parametric approach towards model approximation, whilst models like Support Vector Regression (SVRs) and Neural Networks (NNs) adhere to a non-linear, non-parametric approach for modelling the historical data. Recently, Deep-Learning-based TSFAs such as Recurrent Neural Networks (RNNs), and Long-Short-Term-Memory (LSTMS) have gained popularity due to their capability to model long sequences of highly non-linear and stochastic data effectively. However, the evolution of TSFAs for predicting agrometeorological parameters pivots around one-step-ahead forecasting, which often overestimates the performance metrics defined for validating forecast capabilities of potential TSFAs. Hence, this paper attempts to evaluate and compare the performance of different machine learning (ML) and deep learning (DL) based TSFAs under one-step and multi-step-ahead forecast scenarios, thereby estimating the generalization capabilities of TSFA models over unseen data. The data used in this study are collected from an Automatic Weather Station (AWS), sampled at an interval of 15 min, and range over one month. Temperature (T) and Humidity (H) observations from the AWS are further converted into univariate, supervised time-series diurnal data profiles. Finally, walk-forward validation is used to evaluate recursive one-step-ahead forecasts until the desired prediction horizon is achieved. The results show that the Seasonal Auto-Regressive Integrated Moving Average (SARIMA) and SVR models outperform their DL-based counterparts in one-step and multi-step ahead settings with a fixed forecast horizon. This work aims to present a baseline comparison between different TSFAs to assist the process of model selection and facilitate rapid ahead-of-time forecasting for end-user applications.

Impact of Climate Warming on Cotton Growth and Yields in China and Pakistan: A Regional Perspective

Year to year change in weather poses serious threats to agriculture globally, especially in developing countries. Global climate models simulate an increase in global temperature between 2.9 to 5.5 °C till 2060, and crop production is highly vulnerable to climate warming trends. Extreme temperature causes a significant reduction in crop yields by negatively regulating the crop phenology. Therefore, to evaluate warming impact on cotton (Gossypium hirsutum L.) production and management practices, we quantified agrometeorological data of 30 years by applying multiple crop modelling tools to compute the expected rise in temperature, impact of crop phenology, yield loss, provision of agrometeorology-services, agronomic technologies, and adaptation to climate-smart agriculture. Model projections of 15 agrometeorology stations showed that the growing duration of the sowing-boll opening and sowing-harvesting stages was reduced by 2.30 to 5.66 days decade−1 and 4.23 days decade−1, respectively, in Pakistan. Temperature rise in China also advanced the planting dates, sowing emergence, 3–5 leaves, budding anthesis, full-bloom, cleft-boll, boll-opening, and boll-opening filling by 24.4, 26.2, 24.8, 23.3, 22.6, 15.8, 14.6, 5.4, 2.9, and 8.0 days. Furthermore, present findings exhibited that the warming effect of sowing-harvest time was observed 2.16 days premature, and delayed for 8.2, 2.4, and 5.3 days in the 1970s, 1980s, and 1990s in China. APSIM-cotton quantification revealed that the sowing, emergence, flowering, and maturity stages were negatively correlated with temperature −2.03, −1.93, −1.09, and −0.42 days °C−1 on average, respectively. This study also provided insight into the adaptation of smart and better cotton by improving agrotechnological services.

Theoretical foundations of probabilistic and statistical forecasting of agrometeorological risks

Еach model for forecasting agrometeorological risks based on the analysis of one-dimensional time series is effective for a certain range of initial information. In addition, the values of the initial observations can differ significantly for each specific case, respectively, the widespread use of one method for the analysis of arbitrary information can lead to significant inaccuracies. Thus, the problem of choosing a forecasting method for the initial set of agrometeorological data arises. In this regard, a universal adaptive probabilistic-statistical approach to predicting agrometeorological risks is proposed, which makes it possible to solve the problem of choosing a model. The article presents the results of the first stage of research carried out with the financial support of the Ministry of Education and Science of the Russian Federation a brief overview of the current state of research in this direction is presented, theoretical foundations for predicting agrometeorological risks for a possible onset of drought and frost have been developed, including the task of generating initial information, a description of basic forecasting models, and also a direct description of the proposed approach with a presentation of the general structure of an intelligent system, on the basis of which the corresponding algorithm can be developed and automated as directions for further work.

Satellite-based system of area estimation for main agricultural crops of Ukraine

The article studies one of the most important issues of agricultural production maintenance – development of a system of crops area estimation in Ukraine. The objective of this paper is to describe the similar system that uses high resolution satellite data and operational agrometeorological data from the network of the Hydrometeorological Centre of Ukraine as input information. The system is based on step-by-step solving of the following tasks: obtaining geoinformation data for individual agricultural crops; development of methods for multispectral satellite images classification; development of software applications to automate the process of these images classification with subsequent classification of crop areas. The research uses the following algorithms (or classifiers) to classify the agricultural land: SVM (support vector machine), RF ("random forest") and NN (neural networks). The choice of the most accurate of them formed the basis of the general method of classification. The values of spectral characteristics of red and infrared channels of a complete set of cloudless satellite images during the growing period were used as input data (features). As a result, in 2018 some test calculations were conducted to estimate the area of agricultural crops in Kyiv Region. The results of evaluation of accuracy of the satellite-based agricultural crops area estimation using the statistical data showed that the lowest accuracy is typical for winter wheat and corn. The accuracy of soybeans and spring barley classification is quite low for most of the tested fields. Sunflower and rapeseed crops showed the highest accuracy. In order to improve the accuracy of classification, it is necessary to introduce more classification features (in a temporary aspect) by processing more satellite images during the growing period, and to increase the number of test samples through systematic sampling of ground data across the regions in Ukraine. We suggest using the scheme of main agricultural crops area estimation satellite-based system by the Hydrometeorological Centre of Ukraine.

Estimating Potential Evapotranspiration in Maranhão State Using Artificial Neural Networks

Abstract The use of technology and planning in agricultural production is essential in Northeastern Brazil, which is the region of the country that most suffers from water shortage. For the best irrigation management, it is necessary to know the potential evapotranspiration rate for water control in order to increase productivity. There are several direct and indirect methods for estimating evapotranspiration, but the standard method recommended by the United Nations Agriculture Organization (FAO) is the Penman-Monteith (PETpm) method because it has higher accuracy than other methods. However, it is a difficult method to be used due to the need for a large number of meteorological elements. In this context, the objective of this study was to estimate potential evapotranspiration by the Penman-Monteith method in the micro-region of Baixo Parnaíba in Maranhão state using artificial neural networks. Agro-meteorological data were collected daily over 34 years, from 1984 to 2017, and these data were obtained from the NASA/POWER website. Subsequently, liquid radiation and potential evapotranspiration were calculated by the Penman-Monteith standard method (1998). To predict potential daily evapotranspiration, the Multi-Layer Perceptron (MLP) was chosen, which is a traditional Artificial Neural Network. The period that presented a higher evapotranspiration index was the same one that showed precipitation with a lower volume and higher temperatures. The artificial neural network model that best adapted to estimate PETpm was MLP 2-5-1. It is concluded that artificial neural networks estimate with accuracy and precision the Penman-Monteith daily potential evapotranspiration of the Lower Parnaiba in Maranhão, and potential evapotranspiration can be estimated by the Penman-Monteith method using neural networks with inputs of air temperatures.

Modelling Faba Bean (Vicia faba L.) Biomass Production for Sustainability of Agricultural Systems of Pampas

The Pampas region is characterized by a high complexity in its productive system planning and faces the challenge of satisfying future food demands, as well as reducing the environmental impact of the activity. Climate change affects crops and farmers should use species capable of adapting to the changed climate. Among these species, faba bean (Vicia faba L.) cv. ‘Alameda’ has shown good adaptation to weather variability and, as a winter legume, it can help maintain the sustainability of agricultural systems in the area. The main purpose of this research was to select the models which describe the production characteristics of the ‘Alameda’ bean by using the least number of variables. Experimental and agrometeorological data from the cultivation of the ‘Alameda’ in Azul, Buenos Aires province, Argentina were used to generate mathematical models. Several modelling methodologies have been applied to study the production characteristics of the faba bean. The prediction of the models generated was analyzed by randomly disturbing the experimental data and analyzing the magnitude of the errors produced. The models obtained will be useful for predicting the biomass production of the faba bean cv. ‘Alameda’ grown in the agroclimatic conditions of Azul, Buenos Aires province, Argentina.

Water Consumption Modeling by Coupling MODIS Images and Agrometeorological Data for Sugarcane Crops

Actual evapotranspiration (ETa) is the main component of crop water consumption. Sugarcane requires large volumes of water during its development and may induce changes in local and regional climate. Field measurements, besides their high cost, are insufficient to understand the ETa spatial variability—a gap that can be filled with remote sensing techniques, due to the spatial nature of satellite imaging and its low cost, which allows the construction of temporal, spatial series of estimates of energy and water balances components. The Simple Algorithm For Retrieving Evapotranspiration (SAFER) model was applied to sugarcane crop in the mesoregion of Bauru, Sao Paulo, Brazil. Surface agrometeorological data were used as inputs along with parameters obtained by remote sensing from January 2015 to June 2016. The highest values of daily ETa (> 5.0 mm d−1) were observed in the rainy season, during the sugarcane growing stage, indicating the phase of higher water demand, and the lowest values (< 3.0 mm d−1) were verified in the dry season, during the stages of emergence and maturation. Sugarcane crop was responsible for the consumption of 52% to 62% of the volume of precipitated water in the Bauru meso region. The results of the SAFER model using meteorological data from remote sensing (SAFER-RS) presented d1 = 0.72 in relation to results using surface weather data from the Brazilian National Institute of Meteorology-INMET (SAFER-INMET). The errors in the estimation of ETa by SAFER-RS may be due to errors in the meteorological data from the remote sensing used.

Автоматизированная система «АРМ-Агропрогноз» для агрометеорологического обеспечения АПК, адаптированная для Центрального УГМС

The paper deals with the development of a new software package that automates the main types of work of agrometeorologist-forecasters in the Roshydromet regional departments. An important difference between this technology and the one previously developed for the Ural Administration for Hydrometeorology and Environmental Monitoring is the use of open tools and libraries for its creation. The technology automates the main types of work in the regional departments, starting from processing daily and ten-day agrometeorological telegrams received via communication channels and ending with the organization of calculating agrometeorological forecasts and preparing tables for ten-day and monthly bulletins, annual reviews, various fact sheets, constructing schematic maps and graphs. Keywords: automated technology, operational meteorological and agrometeorological data, agrometeorological forecasts, creation of reports, schematic maps, graphs Fig. 9. Ref. 9.

Constructing a meteorological indicator dataset for selected European NUTS 3 regions.

The harmonization of data granularity in spatial and temporal terms is an important pre-step to any econometric and machine learning applications. Researchers, who wish to statistically test hypotheses on the relationship between agro-meteorological and European policy outcomes, often observe that agro-meteorological data is typically stored in gridded and temporally detailed form, while many relevant policy outcomes are only available on an aggregated level. This dataset intends to aid empirical investigations by providing a dataset with monthly meteorological indicators on a European Nomenclature of Territorial Units for Statistics level 3 (NUTS 3) regional level for 13 countries for the period from 1989 to 2018. The data we provide allows researchers to investigate hypothesis related to weather volatility and the probability of extreme weather events.We created this dataset from the daily data in grids of 25 km x 25 km provided by the Joint Research Centre of the European Commission. We matched the map with the raw data to a map with the administrative boundaries of European NUTS 3 regions. After appropriately weighting, we calculated the monthly, regional mean, variance and kurtosis of the following variables: maximum, minimum, average air temperature in degrees Centigrade, sum of precipitation in mm and snow depth in cm. We report the covariance between the average temperature and the precipitation as well.

Spatial-temporal characteristics of agricultural climate resources and sterile-type chilling injury in rice growing season in three provinces of Northeast China

Based on daily meteorological data and agro-meteorological data in three provinces of Northeast China during 1981-2017, combined with chilling injury indices, we analyzed the spatial-temporal distribution characteristics of solar, heat, precipitation resources and sterile-type chilling injury in rice growing season, especially in the booting and flowering stages. In 1981-2017, agriculture climatic resources in rice growing season showed a warming, drying and darkening trend. Accumulated temperature (≥10 ℃) and sunshine hours increased with a rate of 73.5 ℃·d·(10 a)-1 and 17.7 h·(10 a)-1 respectively, while precipitation decreased with a rate of 8.9 mm·(10 a)-1. At the booting stage, agricultural climatic resources showed a warming, drying and dar-kening trend. Daily average temperature increased 0.27 ℃·(10 a)-1 and sunshine hours and preci-pitation decreased 2.06 h·(10 a)-1 and 1.90 mm·(10 a)-1 respectively. At the flowering stage, agricultural climatic resources showed a trend of warming, wetting and darkening. Daily average temperature increased with a rate of 0.12 ℃·(10 a)-1, while sunshine hours decreased with a rate of 0.83 h·(10 a)-1. In contrast to that at the booting stage, precipitation in the flowering stage increased with a rate of 1.35 mm·(10 a)-1. Under the background of climate warming, the frequency and intensity of rice sterile-type chil-ling injury decreased in most regions, with significant inter-decadal fluctuations. During the study period, the frequency and intensity of sterile-type chil-ling injury were the highest in Heilongjiang Province, moderate in Jilin Province, and the lowest in Liaoning Province.

Estimation of evapotranspiration by the Food and Agricultural Organization of the United Nations (FAO) Penman–Monteith temperature (PMT) and Hargreaves–Samani (HS) models under temporal and spatial criteria – a case study in Duero basin (Spain)

Abstract. The evapotranspiration-based scheduling method is the most common method for irrigation programming in agriculture. There is no doubt that the estimation of the reference evapotranspiration (ETo) is a key factor in irrigated agriculture. However, the high cost and maintenance of agrometeorological stations and high number of sensors required to estimate it make it non-plausible, especially in rural areas. For this reason, the estimation of ETo using air temperature, in places where wind speed, solar radiation and air humidity data are not readily available, is particularly attractive. A daily data record of 49 stations distributed over Duero basin (Spain), for the period 2000–2018, was used for estimation of ETo based on seven models against Penman–Monteith (PM) FAO 56 (FAO – Food and Agricultural Organization of the United Nations) from a temporal (annual or seasonal) and spatial perspective. Two Hargreaves–Samani (HS) models, with and without calibration, and five Penman–Monteith temperature (PMT) models were used in this study. The results show that the models' performance changes considerably, depending on whether the scale is annual or seasonal. The performance of the seven models was acceptable from an annual perspective (R2&gt;0.91, NSE &gt; 0.88, MAE &lt; 0.52 and RMSE &lt; 0.69 mm d−1; NSE – Nash–Sutcliffe model efficiency; MAE – mean absolute error; RMSE – root-mean-square error). For winter, no model showed good performance. In the rest of the seasons, the models with the best performance were the following three models: PMTCUH (Penman–Monteith temperature with calibration of Hargreaves empirical coefficient – kRS, average monthly value of wind speed, and average monthly value of maximum and minimum relative humidity), HSC (Hargreaves–Samani with calibration of kRS) and PMTOUH (Penman–Monteith temperature without calibration of kRS, average monthly value of wind speed and average monthly value of maximum and minimum relative humidity). The HSC model presents a calibration of the Hargreaves empirical coefficient (kRS). In the PMTCUH model, kRS was calibrated and average monthly values were used for wind speed and maximum and minimum relative humidity. Finally, the PMTOUH model is like the PMTCUH model except that kRS was not calibrated. These results are very useful for adopting appropriate measures for efficient water management, especially in the intensive agriculture in semi-arid zones, under the limitation of agrometeorological data.