Feed-forward and feed-back control irrigation scheduling to improve the supplemental irrigation efficiency in woody perennial crops.

UNIVERSITY OF PISA

PHDPROGRAM IN AGRICULTURE,FOOD AND ENVIRONMENT

Cycle XXXIII

DOCTORALTHESIS

Feed-Forward and Feedback Control Irrigation

Scheduling to Improve the Supplemental

Irrigation Efficiency in Woody Perennial Crops

PhD student

Àngela Puig-Sirera

Supervisor, Prof. Giovanni Rallo Opponent, Prof. Diego Intrigliolo

Coordinator of the PhD Program, Prof. Andrea Cavallini

PISA FEBRUARY-2021

Acknowledgements

This thesis intends to raise awareness on the importance of the water resource and its preservation, especially in the context of Mediterranean agriculture. Through science and critical thinking, we have the opportunity to find new insights on how to save and conserve water in agriculture.

I thank to everybody that contributed to it during the three years PhD path.

First acknowledgement to my supervisor Prof. Giovanni Rallo for his willingness to pass me his broad knowledge on the topic and the importance of doing accurate and meaningful science. Working within the AgroHydrological Sensing and Modeling Laboratory and with Dr. Andrea Sbrana have given me the possibility to learn about transferring the scientific research into the farms. This activity is able to raise awareness to the diverse stakeholders on the importance of preserving water and energy.

I thank to Prof. Giuseppe Provenzano for working together during these years and giving always his feedback. In addition, I am grateful to Prof. Diego Intrigiolo for his constructive feedback along these years and continuous collaboration since my bachelor’s degree. Thanks to Prof. Pablo González-Altozano for sharing his research data and his kind support.

I appreciate the hosting and teaching that Prof. Luis Santos Pereira, Teresa Alfonso do Paço and Paula Paredes gave me during the exchange period in Lisboa.

I am much grateful for the support and trust that my family passes to me. To Anna for listening and guide me along a critical path.

I am thankful to all my friends, that are my family, and I find them always besides me.

I hope any reader of the thesis would find something that gives them more awareness on the preservation of water in agriculture.

Dear reader, for the use (not commercial) of the contents (images, diagrams and concepts) of this thesis it is mandatory to cite the source and authors.

To cite this thesis:

Scientific Productivity and Public Engagement

Peer-Reviewed Articles

Rallo, G.; Provenzano, G.; Castellini, M.; Puig-Sirera, À. 2018. Application of EMI and

FDR Sensors to Assess the Fraction of Transpirable Soil Water over an Olive Grove. Water. 2018, 10, 168.

Rallo, G., Sbrana, A., Puig-Sirera, À., Calzone, A., Marchica, A. 2019. Agrohydrological

sensing and modelling for the analysis of drought and mitigation actions: The experience of the AgrHySMo laboratory. Agrochimica, 2019, pp. 145-155.

Rallo, G., Paço, T.A., Paredes, P., Puig-Sirera, À., Massai, R., Provenzano, G., Pereira, L.S., 2020. Updated single and dual crop coefficients for tree and vine fruit crops. AGWAT-106645. Agricultural Water Management. DOI: 10.1016/j.agwat.2020.AGWAT-106645.

Puig-Sirera, À.; Rallo, G.; Paredes, P.; Paço, T.A.; Minacapilli, M.; Provenzano, G.;

Pereira, L.S. 2020. Transpiration and water use of an irrigated olive grove with sap-flow

observations and the FAO56 dual crop coefficient approach. Under revision in

Agricultural Water Management (Elsevier).

Puig-Sirera, À., Provenzano, G., Gonz, P., Intrigliolo, D.S., Rallo, G., 2021. Irrigation

water saving strategies in Citrus orchards: Analysis of the combined effects of timing and severity of soil water deficit. Agricultural Water Management. Volume 248, 1 April

2021, 106773. DOI: 10.1016/j.agwat.2021.106773.

Puig-Sirera À., Antichi D., Warren Raffa D., Rallo G. Application of remote sensing

techniques to discriminate the effect of different soil management treatments over rainfed vineyards in Chianti terroir. Remote Sensing (MDPI), 2021, 13.

Puig-Sirera, À., Marchica, A., Cotrozzi, L., Provenzano, G., Rallo, G. Macroscopic root

water uptake modelling using High-Throughput Screening (HTS) systems: Application for sage (Salvia officinalis L.) under water deficit conditions. Under revision in Biosystem

Engineering (Elsevier).

Warren Raffa, D., Antichi, D., Carlesi, S., Puig-Sirera, À., Rallo, G., Bàrberi, P. Targeted

use of ground vegetation covers increases grape yield and must quality in Mediterranean organic vineyards. Under revision in European Jounral of Agronomy (Elsevier).

Conferences

Puig-Sirera Àngela, Provenzano Giuseppe, González-Altozano Pablo, Manzano-

Juárez Juan, and Rallo Giovanni. Assessing irrigation water saving strategies in Citrus

Orchard: analysis of the combined effects of timing and magnitude of soil water deficit. EGU

General Assembly 2019. Vienna | Austria | 7–12 April 2019.

Raffa Dylan Warren, Antichi Daniele, Carlesi Stefano, Sbrana Massimo, Virili Alessandra, Puig-Sirera Àngela, Rallo Giovanni, and Bàrberi Paolo. Exploring the

effects of vineyard soil management on spontaneous vegetation, soil health, vine growth and grape quality: preliminary results from Chianti Classico. EGU General Assembly 2019.

Rallo Giovanni, Antichi Daniele, Camboni Flavio, Kartsiotis Simone, Puig-Sirera

Àngela, Raffa Dylan Warren, Sbrana Andrea, Tuker Jeff, and Provenzano Giuseppe.

Development of a soil-crop water status Wireless Sensor Network to support the agrohydrological approach in the drought audit processes: first setups in Chianti terroir.

EGU General Assembly 2019. Vienna | Austria | 7–12 April 2019.

Puig-Sirera Àngela, Rallo Giovanni, Giusti Stefano, Provenzano Giuseppe, Sbrana

Andrea, Tuker Jeff, and Massai Rossano. Expert soil moisture Wireless Sensor Network

for the feed-back control of irrigation in heterogeneous crop systems. EGU General

Assembly 2020. Vienna | Austria | 4–8 May 2020.

Fatma Hamouda, Puig-Sirera Àngela, Giusti Stefano, Provenzano Giuseppe, Sbrana Andrea, Tuker Jeff, Bonzi Lorenzo, Iacona Maurizio, Massai Rossano and Rallo Giovanni. Design and validation of a soil moisture-based WSN for an expert irrigation

management in pear trees. EGU General Assembly 2021. Vienna | Austria | 19-30 April

2021.

Public Engagement

Àngela Puig-Sirera. Sinergia tra informazioni da drone (UAV) e reti di sensori senza fili

(WSN) nel monitoraggio dei fenomeni di siccità agricola in viticoltura: l'esperienza nel territorio vitivinicolo del Chianti. Enoforum International Congress 2019. May 21-23

Vicenza, Italy.

Àngela Puig-Sirera. Loris Franco. Tecnologie dell’informazione e della comunicazione a

supporto delle pratiche agricole: Dai Vigneti del Chianti agli agrumi della Conca D’oro.

Festival dell’innovazione su acqua e irrigazione integrated in the “IX International Symposium on Irrigation of Horticultural Crops". 2019 June 17-20 Matera, Italy.

Àngela Puig-Sirera. Phenomenon of the Drought minus, PhD-, WSN for water resource

management and audit processes in urban green spaces. Final Pitch PhD+2020. February

List of principal symbols and acronyms

A area [m2_]

AAE average absolute error [units of the variable] ARE average relative error [%]

ASW available soil water, i.e., depth of water above the wilting point relative to a given soil depth [mm]

aD parameter of the deep percolation equation [mm]

b0 regression coefficient of the linear regression forced to the origin [-] bD parameter of the deep percolation equation [mm]

BD soil bulk density [g cm-3_]

c maximum leaf water potential measured in the control treatment [MPa]

cp specific heat [MJ kg-1 °C-1] cs soil heat capacity [MJ m-3 °C-1] CN curve number [-]

CR capillary rise [mm]

CR closure ratio [-]

CV coefficient of variation [-]

CWSI crop water stress index [-]

Dr depth of cumulative evapotranspiration from the root zone [mm]

DOY number of day in the year [from 1 to 365 or 366]

DP deep percolation [mm]

dIA index of agreement [-]

E evaporation [mm d-1 or mm h-1] Ea water application efficiency [-] Ec conveyance efficiency [-] Ef farm efficiency [-]

Efi field efficiency [-]

Epan pan evaporation [mm d-1 or mm h-1] Epi distribution uniformity [-]

Es conveyance efficiency at irrigation sector level [-] Es evaporation from the soil [mm d-1 or mm h-1] Eso efficiency at soil level [-]

El transpiration efficiency [-]

EC, ECt electrical conductivity of the saturation extract of the soil [dS m-1]

ECH electrical conductivity of the horizontal dipole orientation [dS m-1] ECv electrical conductivity of the vertical dipole orientation [dS m-1]

EF modelling efficiency [-]

List of principal symbols and acronyms

ETc crop evapotranspiration under standard conditions [mm d-1 or mm h-1] ETc act, ETa, ETr actual crop evapotranspiration, i.e., under non-standard conditions

[mm d-1 or mm h-1]

ETEC evapotranspiration obtained with eddy covariance measurements

[mm d-1 _{or mm h}-1_]

ETlys evapotranspiration measured in weighting lysimeters

[mm d-1 _{or mm h}-1_]

ETo (grass) reference crop evapotranspiration [mm d-1 or mm h-1] ETr alfalfa reference crop evapotranspiration [mm d-1 or mm h-1] ETref reference crop evapotranspiration [mm d-1 or mm h-1]

e°(T) saturation vapour pressure at air temperature T [kPa]

es saturation vapour pressure for a given time period [kPa] ea actual vapour pressure [kPa]

fc fraction of soil surface covered by vegetation [-]

fc eff effective fraction of soil surface covered by vegetation [-] few fraction of soil that is both exposed and wetted [-]

fw fraction of soil surface wetted by rain or irrigation [-] F sap flux [m3 _s-1_]

FTSW fraction of transpirable soil water [mm]

FTSW* critical soil water status that marks the transition to the water deficit

condition [mm]

G soil heat flux [MJ m-2 _d-1 _{or MJ m}-2 _h-1 _{or W m}-2_{]; green multispectral}

domain [nm]

GWC groundwater contribution to evapotranspiration [mm]

GWTD groundwater table depth [m]

H sensible heat flux [MJ m-2 _d-1 _{or MJ m}-2 _h-1 _{or W m}-2_]

h; H crop height [m]

I irrigation depth [mm]

Id irrigation doses [mm] J Julian [from 1 to 365 or 366]

k frequency [min]

Kc (standard) crop coefficient [-]

Kc act actual crop coefficient (under non-standard conditions) [-]

Kc ini crop coefficient during the initial growth stage [-] Kc mid crop coefficient during the mid-season growth stage [-] Kc end crop coefficient at end of the late season growth stage [-]

Kc max maximum value of crop coefficient (following rain or irrigation) [-] Kc min minimum value of crop coefficient (dry soil with no ground cover) [-] Kc non-growing crop coefficient during the non-growing crop stage [-]

Kcb basal crop coefficient [-]

List of principal symbols and acronyms

of tree foliage [-]

Kcb full basal crop coefficient during mid-season (at peak plant size or height)

for vegetation with full ground cover of LAI > 3 [-]

Kcb ini basal crop coefficient during the initial growth stage [-] Kcb mid basal crop coefficient during the mid-season growth stage [-] Kcb end basal crop coefficient at end of the late season growth stage [-] Kcb non-growing basal crop coefficient during the non-growing crop stage [-] Ke soil evaporation coefficient [-]

Ke max maximum value of Ke coefficient (following rain or irrigation) [-] Kr soil evaporation reduction coefficient [-]

Ks water stress coefficient [-]

LAI leaf area index [m2 (leaf area) m-2 (soil surface)]

LAIfield field leaf area index [m2 m−2] LAIplant plant leaf area index [m2 m−2]

LE latent heat flux [MJ m-2 _d-1 _{or MJ m}-2 _h-1 _{or W m}-2_] MAD management allowed depletion [-]

MSE mean square error [units of the variable]

MSWP midday steam water potential [MPa or bar]

NDVI normalized difference vegetation index [-]

NIR near infrared multispectral domain [nm]

NRMSE normalized root mean square error (%)

OSAVI optimized soil adjusted vegetation index [-]

P precipitation [mm]

Pa atmospheric pressure [kPa]

PBIAS percent bias [%]

PLWP predawn leaf water potential [MPa or bar]

p soil water depletion fraction for no stress [-]

R red multispectral domain [nm]

RE red edge multispectral domain [nm]

RET relative evapotranspiration [mm]

R2 _{determination coefficient of the ordinary least-squares [-]} Rg global solar radiation [MJ m-2 d-1 or MJ m-2 h-1 or W m-2] Rn net radiation [MJ m-2 d-1 or MJ m-2 h-1 or W m-2]

ra aerodynamic resistance [s m-1]

rc, min minimum canopy resistance [s m-1]

RAW readily available soil water of the root zone [mm]

REW readily evaporable water from the soil surface layer [mm]

RWU root water uptake [mm]

RH relative humidity [%]

RHmin daily minimum relative humidity [%]

List of principal symbols and acronyms

RO surface runoff [mm]

s2obs _{variance of the observed variable [units of the variable]}

SMSWP water stress integral [MPa]

SAVI soil adjusted vegetation index [-]

SRA surface root area [m2_]

SWC volumetric soil water content [m3 m-3] SWCd actual soil water content [m3 m-3]

SWCfc soil water content at field capacity [m3 m-3] SWCmin minimum soil water content [m3 m-3]

T or Tair air temperature [°C]

Tc crop transpiration [mm d-1 or mm h-1]

Tc act or Ta actual crop transpiration [mm d-1 or mm h-1]

Tdry temperature of the crop at maximum water stress [°C] Tmax daily maximum air temperature [°C]

Tmean daily mean air temperature [°C] Tmin daily minimum air temperature [°C]

Tp maximum crop transpiration [mm d-1 or mm h-1] Ts soil temperature [ºC]

TSF field field daily transpiration [mm d−1]

TSF plant plant daily transpiration depth [mm d−1]

Twet temperature of the crop without water stress [ºC]

TAW total available soil water of the root zone [mm]

TCARI transformed chlorophyll absorption in reflectance index [-]

TEW total evaporable water from the soil surface layer [mm]

TTSW total transpirable soil water [mm] t time [h or d]

u2 wind speed observed at 2 m above ground surface [m s-1] uz wind speed at z m above ground surface [m s-1]

Vapp. field quantity of water applied at each field Vent. Sector quantity of water supplied at each sector Vrec. farm quantity of water received at each farm Vroot-zone water retained in the root zone

Vsupplied quantity of water applied at each plant VT water taken up by the crop and transpired VI vegetation index [-]

VPN vapour pressure deficit [KPa]

W lysimeter pot weight [g]

Ze depth of the surface soil layer subjected to drying by evaporation [m] Zr rooting depth [m]

z soil depth [m]

List of principal symbols and acronyms

b Bowen ratio [-]

g psychrometric constant [kPa °C-1_]

D slope of saturation vapour pressure curve [kPa °C-1] DSW variation in soil water content [mm]

Dt length of time interval [d]

DT difference of temperature [ºC]

DTmax maximum difference of temperatures [ºC] Dz effective soil depth [m]

q soil water content [m3 _m-3_]

q* threshold soil water content below which transpiration is reduced due

to water stress [m3 _m-3_]

qFC soil water content at field capacity [m3 m-3]

qmin; qWP soil water content at the permanent wilting point [m3 m-3] ρ air density [Kg m−3]

l latent heat of vaporization [MJ kg-1_] s standard deviation {-}

µ mean [units of the variable]

ν sap flow density [m3 _m-2 _s-1_]

Terminology

FAO56 Food and Agriculture Organization Irrigation and Drainage Paper 56 (1998)

FAO56 PM FAO56 standardized Penman-Monteith equation

PM-ETo grass reference ETo calculated using the FAO56 standardized

Penman-Monteith equation

PM-ETr alfalfa reference ETr calculated using an extension of the FAO56

Penman-Monteith equation

Acronyms

AGL above ground level

CDO Controlled Designation of Origin DI deficit irrigation

CCI barley-clover green manure treatment CCM barley-clover dead mulch treatment CT conventional tillage

CTRL control treatment

EMI electromagnetic inductor

F pigeon bean green manure treatment FI full irrigation

List of principal symbols and acronyms

FDR frequency domain reflectometry GIS geographical information system GPS global positioning system

GSD ground sampling distance

HTS High-Throughput Screening system IoT Internet of Things

MV Montevertine farm

QGIS quantum geographical information system RDI regulated deficit irrigation

RGB red-green-blue domain RS remote sensing

S spontaneous vegetation

SDG Sustainable Development Goals SEB surface energy balance

SG San Giusto a Rentennano farm SPA soil-plant-atmosphere system SWB soil water balance

SWD soil water deficit

TDR time domain reflectometry UAV unmanned aerial vehicle WSN wireless sensor network WUE water use efficiency

Index

Acknowledgements ... 7 Scientific Productivity and Public Engagement ... 11 List of principal symbols and acronyms ... 13 Chapter I - New paradigms for water resource management in agriculture ... 47 I.1 Farm digitalization for resilient water management ... 50 I.2 Agrohydrological sensing and modeling as a tool for precision farm irrigation management ... 53 References ... 57 Chapter II - Updated single and dual crop coefficients for tree and vine fruit crops . 61 II.1 Introduction ... 62 II.2 Requirements for accuracy on deriving Kc from field studies ... 64

II.2.1 Limitations and requirements for the transferability of crop coefficients ... 64 II.2.2 Field data measurement and accuracy requirements ... 68 II.2.3 Crop coefficients derived from field measurements in the presence of advection

71

II.3 Information on selection methodologies ... 72 II.3.1 Methods adopted to select the papers ... 72 II.3.2 Methods adopted to select updated ranges of Kc/Kcb values ... 73

II.4 Review on single and dual Kc for tree and vine crops ... 73

II.4.1 Vine fruit crops, berries and hops ... 74 II.4.2 Temperate climate evergreen fruit trees ... 76 II.4.3 Temperate climate deciduous fruit trees ... 78 II.4.4 Tropical and subtropical fruit crops ... 81 II.5 Indicative standard Kc and Kcb values ... 83

II.6 Conclusions and future perspectives ... 91 References ... 93 Chapter III - Evapotranspiration partitioning and water use of an irrigated olive orchard using a dual crop coefficient model ... 127 III.1 Introduction ... 128 III.2 Material and methods ... 130 III.2.1 Site description and crop characterization ... 130 III.2.2 Field data ... 132

Sap flow and Transpiration data ... 132 Eddy covariance and Evapotranspiration data ... 134

III.2.3 SIMDualKc model and ET fluxes simulation ... 135

Model description ... 135 Goodness-of-fit-indicators used for model calibration and performance analysis ... 136

Index

III.3.1 Model calibration and validation using transpiration measurements ... 138 III.3.2 Model validation with eddy covariance measurements ... 140 III.3.3 Dynamics of the single and basal crop coefficients throughout the season ... 141 III.3.4 Soil Water Balance and Water Use ... 144 III.4 Conclusions ... 147 References ... 149 Chapter IV - Irrigation water saving strategies in Citrus orchards: analysis of the combined effects of timing and severity of soil water deficit ... 157 IV.1 Introduction ... 158 IV.2 Materials and Methods ... 160 IV.2.1 Characteristics of the experimental field ... 160 IV.2.3 Irrigation treatments ... 161 IV.2.4 Measurements of soil and tree water status ... 162 IV.2.5 Yield determination ... 162 IV.3 Results ... 163 IV.3.1 Agrometeorological characteristics and lysimeter-based crop evapotranspiration ... 163 IV.3.2 Eco-physiological crop response to soil water deficit ... 164 IV.3.3 Water stress integral and effects due to irrigation variables ... 165 IV.3.4 Effect of irrigation depth variability on crop water stress and crop yield ... 168 IV.4 Discussion ... 170 IV.5 Conclusions ... 173 Chapter V - Application of EMI and FDR Sensors to Assess the Fraction of Transpirable Soil Water over an Olive Grove ... 181 V.1 Introduction ... 182 V.2 Materials and Methods ... 184 V.2.1 Soil Physical Characterization and EM38 Calibration Procedure ... 186 V.2.3 Transpiration Fluxes and Relative Transpiration Measurements ... 188 V.2.4 Data Analysis and Pre-Processing ... 189 V.3 Results and Discussion ... 190 V.3.1 Soil Surface Texture and Spatial Analysis ... 190 V.3.2 Evaluation of Total Transpirable Soil Water (TTSW) ... 192 V.3.4 EM38 Model to Predict the Fraction of Transpirable Soil Water ... 194 V.3.5 Temporal and Spatial Variability of Soil Bulk Electrical Conductivity and Plant Water Status ... 195 V.3.6 Relations between Relative Transpiration and the Fraction of Transpirable Soil Water ... 197 V.4 Conclusions ... 198 References ... 200 Chapter VI - Application of remote sensing techniques to discriminate the effect of different soil management treatments over rainfed vineyards in Chianti terroir ... 203

Index

VI.1 Introduction ... 204 VI.2 Materials and Methods ... 206 V.2.1 Study site description ... 206 VI.2.2 Experimental design ... 207 VI.2.3 Field measurements ... 209

Soil physical characterization ... 209

Ecophysiological measurements ... 209 VI.2.4 Remote sensing measurements ... 210

UAV platform and setting ... 210

Spectral and thermal images processing ... 211 VI.2.5 Data mapping and analysis ... 211 VI.3 Results ... 212 VI.3.1 Soil physical properties ... 212 VI.3.2 Structure of the vegetation spectral variability ... 213 VI.3.3. Structure of the surface temperature variability ... 220 VI.3.4 Mapping CWSI and discrimination of treatments ... 223 VI.4 Discussion ... 225 VI.4.1 Variability within the vineyard systems ... 226 VI.4.2 Spectral vegetation and thermal indexes to discriminate among treatments . 226 VI.5 Conclusions ... 230 References ... 231 Chapter VII - Macroscopic root water uptake modelling using High-Throughput Screening (HTS) systems: Application for sage (Salvia officinalis L.) under water deficit conditions ... 239 VII.1 Introduction ... 240 VII.2 Materials and Methods ... 242 VII.2.1 Description of the HTS-system and experimental setup ... 242 VII.2.2 Crop eco-physiological and biometric measurements ... 243 VII.2.3 Determination of soil hydrological properties and irrigation scheduling protocol ... 244 VII.2.4 Procedure to assess the indicators of soil and crop water status ... 244 VII.2.5 data processing and analysis ... 245 VII.3 Results and Discussion ... 247 VII.3.1 Agro-meteorological characteristics ... 247 VII.3.2 Preprocessing of the crop and soil water status data series ... 248 VII.3.3 Soil-plant water relation and root water uptake modeling ... 251 VII.3.4 Effect of soil bulk density (BD) on root water uptake (RWU) ... 255 VII.4 Conclusions ... 257 References ... 258 Conclusions and future perspectives ... 263

List of Tables

transpiration (Tc act) and the corresponding obtained from sap-flow measurements (TSF field).

... 139 Table iii.5. The goodness-of-fit indicator is relative to the comparison between simulated evapotranspiration (ETc act) and the corresponding evapotranspiration obtained from eddy covariance (ETEC). ... 141 Table iii.6. Time-averaged single crop coefficients (Kc) for each crop growth stage. ... 144 Table iii.7. Simulated soil water balance components (all variables in mm). ... 145 Table iii.8. Ratios between soil evaporation and actual evapotranspiration (Es/ETc act, %), between actual crop transpiration and actual evapotranspiration (Tc act/ETc act, %), and between actual evapotranspiration and potential evapotranspiration (ETc act/ETc, %) ... 146 Table iv.1. Summary of water stress integral, precipitation, total irrigation amount, average irrigation doses and its variability applied during the three stages of fruit growth in 1995 and

Index

1996. The symbols µ and CV represent the mean and the coefficient of variation of irrigation

doses. ... 166 Table v.1. Vertical distribution of clay percentages in sites A and B. ... 191 Table vi.1. Description of the experimental treatments ... 207 Table vi.2. Average values (µ) and standard deviation (s) of the particles size composition (percentage of clay, silt and sand), gravel and percentage of active limestone of MV and SG ... 212 Table vi.3. Twet and Tdry temperatures (°C) of the two farms divided by treatment ... 223

List of Figures

2009, 2010 and 2011: (a) maximum (Tmax ) and minimum (Tmin) temperatures, (b) minimum

relative humidity (RHmin) and wind speed at 2 m height (u2), and (c) precipitation and reference evapotranspiration (ETo). ... 131 Figure iii.2. Actual crop transpiration dynamics measured with sap flow (TSF field, •) and simulated with SIMDualKc (Tc act, —) for a) 2009; b) 2010; c) 2011. ... 140 Figure iii.3. Actual crop evapotranspiration dynamics measured with eddy covariance (ETEC, ●) and simulated with SIMDualKc (ETc act, —) for (left) 2009 and (right) 2010. ... 141 Figure iii.4. Standard and actual basal crop coefficients (Kcb, Kcb act), evaporation coefficient (Ke) and standard and actual single crop coefficients (Kc, Kc act) for an intensive olive orchard for: (a) 2009, (b) 2010 and (c) 2011, with depicting precipitation and irrigation events. Ke (…) Kcb (―) Kcb act (- - -) Kc mean (―) Kc (―) Kc act (…) precipitation (׀), irrigation (׀). ... 143 Figure iv.1. Regulated Deficit Irrigation treatments. ... 161 Figure iv.2a, d. Daily values measured in 1995 and 1996 of (a) minimum and maximum air temperature, and average relative air humidity; (b) global solar radiation, and wind at 2.0 m above the soil surface, v; (c) reference, ET0, lysimeter evapotranspiration, ETlys, and their cumulated values; (d) precipitation, P, irrigation, I, and their cumulated values. ... 163 Figure iv.3a, d. Experimental values of Predawn (PLWP) and Midday (MSWP) Leaf Water Potential as a function of soil water content, SWC. ... 164 Figure iv.4a,b. Relationship between the water stress integral in all stages and the water supply (precipitation plus irrigation) in a) absolute and b) normalized terms. ... 166 Figure iv.5a,b. Relationship between the water stress integral, !"!#$, and the relative total amount of irrigation, I, applied during a) the whole year and b) the stage II of fruit growth. The model proposed by Rallo et al. (2017) is also reported in b). ... 167 Figure iv.6a,b. Relationship between the !"!#$, and average irrigation depth, Id, applied in each watering during a) the whole period of water deficit application and b) the phase II of fruit growth. The relationship from Rallo et al. (2017) is also displayed in b). ... 168 Figure iv.7a, b. Relationship between a) the water stress integral, SMSWP, and b) the coefficient of variation of available soil water content, CV-(SWCfc-SWCi), with the coefficient of variation of the irrigation depth, CV-Id. ... 169 Figure iv.8. Relationship between crop yield and the coefficient of variation of available soil water content, CV-(SWCfc-SWCi), observed during the three stages of crop growth. ... 170 Figure v.1. Location of the experimental farm with the sampling zone (dashed box) and the measurement points (dots). Scintillometric footprints (yellow shaded area) along two wind

List of Figures

directions are also indicated. WS: weather station; R: scintillometer receiver unit; T: scintillometer transmission unit. ... 185 Figure v.2. USDA soil texture triangle and texture of topsoil samples collected at EM38 measurement points. ... 190 Figure v.3. Map of the topsoil clay percentage. North and East coordinates are referred to UTM ED50 system. The sampling points (dots) and a transparent image of the field are also shown. ... 191 Figure v.4. Maximum, minimum, and average profiles of soil water content observed at sites A (left) and B (right). ... 192 Figure v.5. Temporal dynamics of SWC observed at different soil layers at sites A and B. Shaded zones represent the transitory phases of SWCs between the stages of free drainage (FD), free drainage and root water uptake (FD + RWU), and root water uptake (RWU). ... 193 Figure v.6. Upper (SWCfc) and lower (SWCmin) limits of TTSW obtained for the four soil layers at sites A (left) and B (right). ... 194 Figure v.7. Values of FTSW versus EM38 readings for sites A and B and their corresponding fitting equation. ... 194 Figure v.8. Temporal dynamics of ECt values and the corresponding standard deviation during the irrigation seasons of 2008 and 2009. Irrigation and precipitation events are also represented. ... 195 Figure v.9. Maps of transpirable soil water (FTSW) fraction before and after the irrigation event of 14 August 2008. The sampling points (dots) and the field image are also shown. 196 Figure v.10. Relationship between relative transpiration and the fraction of transpirable soil water. ... 197 Figure vi.1. Experimental fields and their set-up for Montevertine (top image) and San Giusto a Rentennano farm (bottom image) ... 208 Figure vi.2. Maps of active limestone and gravel content of MV and SG experimental fields ... 213 Figure vi.3. Correlation between NDVI and LAI for the two farms investigated. The error bars indicate the standard deviation of the measurements collected on each pair of transects. ... 214 Figure vi.4. NDVI maps of June and August 2018 for MV and SG ... 215 Figure vi.8. Correlation between TCARI/OSAVI and SPAD acquired on 16-June (J) and 03-August (A). The size of the bubble is proportional to the soil gravel content. ... 216 Figure vi.5. Distribution frequency of the NDVI pixels and boxplots obtained for the entire vine row treatment and for the three subplots (top; middle; bottom) localized along the slope gradient. ... 217 Figure vi.6. Distribution frequency of the NDVI pixels and boxplots obtained for the entire vine row treatment and for the three subplots (top; middle; bottom) localized along the slope gradient. ... 218

List of Figures Figure vi.7. TCARI/OSAVI maps of June and August 2018 for MV (in the top of the Figure) and SG (at the bottom of the figure). ... 219 Figure vi.9a-b. Surface temperature image referred to the entire experimental plots in a) MV and b) SG. ... 221 Figure vi.10. Distributions frequency and box-plots of the surface temperature pixels obtained for the entire treatment row and for the three subplots (top; middle; bottom) ... 222 Figure vi.11a, b. Maps of CWSI computed for a) Montevertine and b) San Giusto a Rentennano. ... 223 Figure vi.12a, b. Relationship between CWSI vs MSWP for Montevertine (a) and San Giusto a Rentennano (b). The size of the bubble is proportional to the slope gradient, whereas the number next to the bubble indicates the NDVI value ... 224 Figure vi.13a, b. Violin data encoding and pairwise comparison of CWSI values computed for the five soil management treatments for Montevertine (a) and San Giusto a Rentennano (b). ... 225 Figure vii.1 – Linear regression between load cell temperature, TLC, and air temperature, Ta. ... 246 Figure vii.2 - Regressions between soil temperature, Ts, and air temperature, Ta, during the three circadian sub-periods in which the daily hours were divided. ... 246 Figure vii.3 – Trend of agrometeorological variables measured during the experimental period (from 12 September 2019 to 5 November 2019). ... 247 Figure vii.4 – Raw, thermal drift adjusted and smoothing processed data of hourly signals collected for three consecutive days of the experimental period. ... 248 Figure vii.5a,b – Temporal dynamic of a) hourly actual evapotranspiration, ETa, and b) soil water content, q , obtained for the deficit (SAGE-DI) and the full (SAGE-FI) irrigation treatments. The boxplots corresponding to the two treatments are also shown on the right side of the figure. ... 249 Figure vii.6 - Cumulative mean values and standard deviations of actual evapotranspiration and irrigation amount during the experimental period for the SAGE-FI and SAGE-DI treatments. ... 250 Figure vii.7 – Relationship between daily actual evapotranspiration (ETa) and the corresponding root water uptake (RWU). The regression equation (black) and 1:1 (red) line are also shown. ... 251 Figure vii.8 – Comparison between the frequency distribution of daily evapotranspiration

(ETa) and root water uptake (RWU) for a) full and b) deficit irrigation treatments. The symbol

“ns” indicates that the pairing series were not significantly different. ... 252 Figure vii.9 – Screening of the agro-hydrological data expressed as actual plant actual

evapotranspiration (ETa) versus soil water status (FTSW) and atmospheric evaporative

List of Figures Figure vii.10 – Relationship between midday stem water potential, MSWP, and relative evapotranspiration, RET; the filling colours indicate the crop height, H, while the dot sizes the fraction of soil water available for transpiration, FTSW. ... 253 Figure vii.11 – Relative transpiration, RET, as a function of the fraction of soil water available for transpiration, FTSW; the root water uptake model expressed with a three parameters logistic function with the 95% of the confidence interval is also shown. ... 254 Figure vii.12 – Root weight density (RWU) as a function of soil bulk density for a) full and b) deficit irrigation treatments. The bubble colour gradients represent the ratio between the dry weight of the epigeal organs and the plant height, whereas the number close to the bubble is the cumulated ETa. ... 255

Objectives and framework of the thesis

Woody perennial crops are sparse and heterogeneous crop systems that are characteristic from the Mediterranean regions, thus with their phenological fruit growth stage, usually, under water deficit conditions. In addition, water is becoming progressively scarce, mainly due to the intensification of intensity and frequency of drought phenomena, as well as the competition among sectors using the water resource of a territory. Therefore, to study water saving protocols, new technologies and research methods to propose new solutions for the resilient water management in woody perennial crops are of paramount importance.

The thesis intends to present new insights in the topic of water management in sparse crop systems by implementing approaches such as the so-called feed-forward and feedback control. The first refers to the use of agrohydrological models to estimate crop water requirements and thus, scheduling irrigation. Meanwhile, the feedback control is based on the use of sensors to monitor diverse soil-plant water status variables to set the irrigation timing and water amount, according to reference values (i.e. thresholds) accurately defined. In addition, the feedback approach could serve as a support tool for a feed forward irrigation control by implementing the monitored data into the soil water balance model. In this thesis, these two approaches were developed with experiments carried out in the field, in controlled environments (i.e. greenhouse), as well as in-silico research activities.

The thesis was structured in seven chapters, including an introductive Chapter I addressing the new paradigms for water resource management in agriculture. The first three chapters deal with researches based on a feed-forward approach for irrigation management, whereas the last three are mainly focused on the feed-back control.

The following diagram summarizes the framework of the thesis and the main objectives of each chapter, that were addressed with the research activities carried out during the PhD course.

Agrohydrological models are mainly divided into numerical and conceptual models, being these latter more simplistic approaches enabling broader and practical use. Specifically, the procedure described in FAO 56 paper (Allen et al., 1998) is a soil water balance model that has been successfully used in agriculture. It is based on the crop coefficient (Kc) approach to estimate crop evapotranspiration (ETc). Specific Kc

are tabulated in FAO56 for each fruit and vine tree crops under standard conditions, which have been widely used for irrigation requirements. However, during the last decades new planting densities, modern training and irrigation systems have been implemented in tree and vine fruit crops, which may affect crop water requirements.

Doctoral Thesis of Puig-Sirera Àngela

methodological approaches to estimate crop coefficients, in comparison to homogeneous canopies of vegetable and field crops. Due to these factors, there is a need to update information and extend tabulated single (Kc) and basal (Kcb) standard

crop coefficients of tree and vine fruit crops.

Chapter II deals with this specific topic by reviewing the research done over the past twenty years and aimed at estimating the FAO56 crop coefficients of fruit trees and vines. It was observed that the values of Kc and Kcb strongly depend on crop

density and ground cover fraction. Moreover, new indicative standard Kcmid, Kc end and Kcb mid and Kcb end were tabulated and compared with previous studies, to conclude that

the updated standard values could be used for irrigation scheduling and water saving planning.

Agrohydrological models are mainly divided into numerical and conceptual models, being these latter more simplistic approaches enabling broader and practical use. Specifically, the procedure described in FAO 56 paper (Allen et al., 1998) is a soil water balance model that has been successfully used in agriculture. It is based on the crop coefficient (Kc) approach to estimate crop evapotranspiration (ETc). Specific Kc

are tabulated in FAO56 for each fruit and vine tree crops under standard conditions, which have been widely used for irrigation requirements. However, during the last decades new planting densities, modern training and irrigation systems have been implemented in tree and vine fruit crops, which may affect crop water requirements. In addition, sparse crop systems present complex canopies that need different methodological approaches to estimate crop coefficients, in comparison to homogeneous canopies of vegetable and field crops. Due to these factors, there is a need to update information and extend tabulated single (Kc) and basal (Kcb) standard

crop coefficients of tree and vine fruit crops.

Chapter II deals with this specific topic by reviewing the researches done over the past twenty years and aimed at estimating the FAO56 crop coefficients of fruit trees and vines. It was observed that the values of Kc and Kcb strongly depend on crop

density and ground cover fraction. Moreover, new indicative standard Kcmid, Kc end and Kcb mid and Kcb end were tabulated and compared with previous studies, to conclude that

the updated standard values could be used for irrigation scheduling and water saving planning.

An application of SIMDualKc agrohydrological model for olive orchards (Olea europaea L.) is presented in Chapter III. The model combines the FAO56 dual crop

coefficient approach to estimate crop water use and to evaluate the crop coefficients. The model was calibrated and validated with measurements of sap-flow tree transpiration and evapotranspiration data from and eddy covariance tower, acquired during three irrigation seasons. In this way, new basal crop coefficients (Kcb) for the

Doctoral Thesis of Puig-Sirera Àngela

The activities described in Chapter II and III are embedded into an international research collaboration with the research group Centro de Investigação em Agronomia, Alimentos, Ambiente e Paisagem (LEAF) of the Instituto Superior de Agronomia (Universidade de Lisboa, Portugal). The PhD exchange period was supported by this research group, whose leader is Prof. Luis Santos Pereira.

Water saving strategies are common management practices in woody perennial crops. As previous research has demonstrated, the adoption of controlled water stress at certain phases of the crop cycle can be beneficial in agronomical and environmental terms (Cammalleri et al., 2013a; Ferreira et al., 2012; Lobos et al., 2016; Rallo et al., 2017). This topic is addressed in Chapter IV, in which the crop response to soil water deficit under regulated deficit of drip irrigated mandarin trees is investigated. Seven irrigation treatments, that were applied during the three stages of fruit growth, assessed the eco-physiological crop response to different timing and severity of water stress, and in particular to identify the critical threshold of soil water status under which the crop leaf water potentials decrease. Moreover, the research permitted to identify the predictive relationship of water stress integral on the bases of the seasonal irrigation volume supplied. Finally, it was observed how the average and the coefficient of variation of irrigation doses affect the cumulated water stress of the investigated crop. The observed variability of the crop water stress was associated with the high variability of the available soil water that may influence crop yield.

The effects of the spatial variability of soil water content and conservation practices on crop water status were investigated with outdoor activities. In this context, distributed methodologies, such as proximity and remote sensing techniques were used to study the spatial variability of the soil and tree water status under deficit conditions, which were performed in field experiments.

Chapter V focuses on the use of an electromagnetic sensor (EM38, Geonics Ltd., Mississauga, ON, Canada) in a drip irrigated traditional olive orchard to perform accurate mapping of soil water content and to study the water stress function. The significant relationship demonstrated that EM38, after a site-specific calibration, is able to provide quick and reliable measurements of the fraction of transpirable soil water (FTSW). It was observed a strong spatial variability of FTSW after an irrigation event as a consequence of the spatial variability of root water uptake observed in the field.

Soil conservative practices such as cover crops have been implementing in woody perennial crops to enhance agroecosystem services. However, farmers are reluctant to their adoption and research results about the cover crop residual effects are contradictory. In addition, these management techniques add another degree of complexity for the application of feedback and/or feed forward control irrigation.

Chapter VI presents an integrated methodology to differentiate among different soil management treatments by high-resolution thermal and spectral vegetation imagery using an unmanned aerial vehicle and geostatistical software. Five soil

Objectives and framework of the thesis

management treatments were applied in two organic vineyards (Vitis vinifera L., cv. Sangiovese) from Chianti Classico terroir (Tuscany, Italy) during two experimental years. The active limestone and gravel soil components determined the spatial variability of vine biophysical and physiological characteristics. The vine canopy thermal- and vegetation-based indexes were lower on the spontaneous and pigeon bean treatments, which reveals less physiological stress on the vine rows derived from the cover crop residual effect. Therefore, the suggested methodology was able to discriminate the most suitable soil technique to map the spatial variability within the whole vineyard.

Currently, there is the need to early detect the plant ecophysiological traits more adaptable and resistant to abiotic stresses. In this sense, the High-Throughput Screening (HTS) systems can represent a robust tool and, in an agro-hydrological context, could be used for an early parameterization of the macroscopic root water uptake model.

In Chapter VII, an HTS system was developed and tested for continuous and simultaneous monitoring of the plant response to drought stress. The system is a platform that combines a weighing system with sensors to monitor soil moisture and climate variables. A first trial was conducted on sage (Salvia officinalis L.) to study the crop response to soil water deficit. The root water uptake macroscopic approach was followed to characterize the sage water stress function. Hence, the critical threshold of soil water status below which the plant becomes to experience stress was determined. In addition, the effects of soil bulk density on the root density and plant biomass were assessed, which highlights the importance of the homogeneous pot filling process.

Chapter I - New paradigms for water resource

management in agriculture

The European Green Deal (EC, 2019) and the Sustainable Development Goals of the United Nations (SDG2-Zero Hunger and SDG6-Water and sanitation, UN 2015)

have underlined water as the centre of nature, economy, industries, societal functions, as well as the wellbeing of citizens. The actions fostered by these strategies are based on research and environmentally friendly technologies that would boost water use efficiency towards healthy water bodies, secure and resilient water management and right access to its use within a circular economy.

Within this context, irrigated agriculture is the sector that demands more water, which will be the first sector affected by water scarcity, causing a reduced capacity to sustain per capita food production (Bonfante et al., 2019). Therefore, the efficient use of water in agriculture is one of the most important demanding task that new technologies are aiding to solve in agriculture (Navarro-Hellín et al., 2016).

To upgrade the efficiency of water use for agricultural production is crucial to understand how water is used. In general, the performance of irrigation systems and water use activities in agriculture are expressed with terms relative to efficiency (Pereira et al., 2012).

The irrigation system is composed of several sub-systems that use water in different spatial and temporal scales. A nested approach is a comprehensive framework to study WUE that quantifies and integrates the efficiency of water use of the various sub-systems and allows to scale up to the different scale levels. The nested approach considers the use of water for agricultural production as interlinked chains of sequential efficiency steps, which represents a simple way to quantify the overall water use efficiency (Hsiao et al., 2007).

The efficiency of the water-based process is defined as the ratio between the output (e.g. water beneficial use) and inputs (e.g. resource applied to the process), usually expressed in percentage terms (Hsiao et al., 2007; Pereira et al., 2012). Thus, the overall efficiency of a water-based process that is formed by sequential steps, is defined as the product of the elementary efficiency quantified on each step of the WUE chain. In this chain, the output component of one ratio becomes the input of the next. In this sense, the positive effects of increasing WUE at any level will propagate to the higher levels of the water chain reaching the agricultural economic system (Small and Svendsen, 1990).

In agricultural production processes, the water use efficiency can be understood as the ratio between the water depth beneficially used by the sub-systems under consideration and the total water depth supplied to that sub-system (Pereira et al., 2012).

Chapter I

In an irrigation scheme, the WUE chain starts at the territorial water source, involves the distribution networks (collective and on-farm), until reaching the soil-plant-atmosphere continuum, where transpiration and soil evaporation processes occur. These latter two processes can be considered the lowest level of the whole water chain.

Specifically, the WUE of the agricultural production can be schematized as showed in Fig. i.1, in which the following terms are used:

Ec: conveyance efficiency at irrigation scheme level composed by N sectors,

calculated as the ratio between the total quantity of water supplied at each sector (Vent. Sector), and the quantity of water distributed from the territorial source (i.e dam). The

efficiency of this step depends on the state of the collective irrigation distribution network, and its engineering and management practices.

Es: conveyance efficiency at irrigation sector level composed by N farms, calculated

as the ratio of the total quantity of water received at each farm (Vrec. farm), to the quantity

of water spilled from the first-order hydrant of the collective irrigation network. Likewise, the efficiency of this step depends on the state of the collective irrigation network.

Ef: farm efficiency associated to N fields, calculated as the ratio of the sum of the

quantity of water applied at each field (Vapp. field), to the quantity of water spilled from

the second-order hydrant of the collective irrigation network. The efficiency of this step depends on the state of the farm irrigation network used to bring the water to the irrigation systems.

Efi: field efficiency associated to N plants, calculated as the ratio of the summation

of the quantity of water applied at each plant (Vsupplied), to the quantity of water applied

by the on-farm irrigation system. The efficiency of this step depends on the distribution uniformity (Epi).

Ea: water application efficiency refers to the ratio between water retained in the root

zone (Vroot-zone) and water applied as irrigation to the plant in the field (Vsupplied).

Application efficiency is higher in microirrigation systems compared to the surface irrigation method. Moreover, at this level, the quality of the emitters (expressed in terms of Manufacturer’s Coefficient of Variation) and their relative position to the root zone are the main factors affecting Ea.

El: transpiration efficiency is a measure of the proportion of water taken up by the

crop and transpired (VT) by the leaves to the quantity stored in the root-zone (Vroot-zone).

This efficiency depends on the crops biophysical and agronomic characteristics (leaf area index, training system etc.), as well as by the crop feedback mechanisms (stomatal regulation, root water uptake ability, osmotic adjustment, capacitance, etc.).

Es: the efficiency at soil level depends on the amount of water lost by evaporation

processes to the quantity stored in the root zone. At this level, the main factor influencing Es is the soil management practices applied by the farmer.

New paradigms for water resource management in agriculture

Figure i.1. Schematic diagram of the efficiency steps of the WUE chain

The nested approach can determine where inefficiencies are located in the chain and can evaluate the potential improvements that may be reached in each of the efficiency steps and their impact on the overall efficiency. Therefore, the quantitative relations developed by the nested approach allow using optimization techniques to decide which could be the best resources allocation to maximize the water use efficiency and productivity of a certain agroecosystem. Moreover, this approach is a tool to overcome drought-related problems, water shortage, leakage and losses.

The application of new technologies, joined with those already existing, could provide new definitions of WUE indicators to study the different spatial and temporal scales involved in agricultural water use. Specifically, water use at the farm level is the most fragmented subsystem along the chain, thus a discretize approach to study water use efficiency at farm level could produce more potential improvements to the entire agricultural system.

Irrigation uniformity coefficients have been used to express the variability of supplied water volumes at field scale and hence, to assess the performance of the on-farm water distribution system. Therefore, irrigation uniformity coefficients should be accounted for accurate computation of WUE at the field scale. Moreover, crop management techniques have been used to optimize crop productions with limited water supplies, among which crop training system is the most common one to

Chapter I

These techniques are applied according to environmental constraints and can affect the soil, microclimate, plant growth, yield and quality and, consequently, photosynthetic activity and water losses (Williams and Ayars, 2005).

Even so, the most effective way to increase WUE is the fine control of irrigation (Pereira et al., 2012). Several methodologies and protocols have been studied and implemented to reduce irrigation volumes. Traditionally, methods to study crop water status or irrigation plan performance have been focused on discrete, tedious and destructive measurements, which are not able to account for high spatial and temporal resolution. Concretely, selecting new technologies, coupled with those already existing in agriculture, such as remote sensing, allow studying WUE on extensive spatial and temporal scales.

It is important to bear in mind that increased WUE at farm scale is not always transferred into public-good benefits of increased water availability, as scientific evidence has demonstrated (Perry et al., 2017). Governments have been subsidizing advanced irrigation technologies (i.e. drip and sprinkler irrigation) with the objective of increasing irrigation efficiency, which would be able to reallocate the resource to other sectors while keeping agricultural production. However, increased WUE at a farm scale has not been always translated into a decline of water consumption at basin scale (Grafton et al., 2018).

This could be illustrated by the fact that implementing high-tech irrigation technologies have led farmers to shift towards more water-demanding crops, higher planting densities and expanding the irrigated area, which turn into more on-farm water consumption and groundwater extractions.

Therefore, Grafton et al. (2018) proposed a five-step approach to be implemented in the policy agenda to preserve water availability. First, performing an accurate water accountability at farm and basin scale for better decision-making policies. Second, setting direct limits on water extraction and irrigated areas to decrease water consumption. Third, developing risk assessments to evaluate the effects of increased WUE at farm scale through the precise monitoring of water flows. Exanimating the trade-offs from subsidized irrigation technologies to evaluate the ratio benefits/costs is the fourth step. Finally, behavioral and experimental economics methodologies can help on comprehending the motives and practices of irrigators to keep agricultural production with less water extraction.

I.1 Farm digitalization for resilient water management

Technology plays a key role in any approach that seeks to improve WUE in agriculture. In this context, it is essential the digitalization of the agricultural sector, which involves incorporating new technologies to uphold the farmer decision making oriented towards efficient irrigation management. The collection of large amounts of data on agro-environmental variables with the high spatial and temporal resolution

New paradigms for water resource management in agriculture

are the main pillars for an expert decision making in farm digitalization (Fielke et al., 2019; Navarro-Hellín et al., 2016).

The network of digital technologies is known as the Internet of Things (IoT), which allows connecting the real world with wireless communication technologies, such as sensor networks. The implementation of IoT in agriculture pursues two main objectives: i) to deepen the knowledge of variables affecting the crop development using new monitoring technologies and, ii) to improve the agricultural practices by strengthening the links between technological advances and crop management (Ma et al., 2011).

The implemented technologies involve hardware and software, such as sensor nodes, satellites imagery, terrestrial and aerial drones, data storage and analysis, and advisory systems (Cambra Baseca et al., 2019). However, the focus of farm digitalization should not be just to support industrialized agriculture, but to make the whole process more efficient, sustainable, and of high quality, while paying attention to farmers’ needs (Bacco et al., 2019).

The new technologies bring many advancements compared to the classical information sources, which imply extensive sampling, time-consuming and laborious data acquisition. Instead, these new approaches cover large areas, enable sampling of edaphic factors and crop status at high spatial resolutions and the zonation of diverse areas (Allen et al., 2012).

Irrigation and water management are one of the key sectors where farm digitalization would have the most important applications with the highest benefits, as the sector is one of the most susceptible to uncertainties. Farm digitalization could help on lowering these uncertainties by facing the challenges posed by conflict uses, water scarcity and extreme weather events (Cavazza et al., 2017).

Precision agriculture is an innovative approach of farm management that advocates for farm digitalization to apply the farming techniques at the proper location, time, and intensity to prevent under and over-usage of inputs while yield is sustained. In the precision agriculture approach, fields are viewed as heterogeneous systems with spatial and temporal variability of the considered variables, which are then divided into different management zones supplying fine-tuned inputs. This fact would turn into decreased environmental impacts, enhanced farming system resilience and increased revenue for farmers (Rey-Camarés et al., 2015: Maes and Steppe, 2019; Sarri et al., 2020).

Specifically, woody perennial crops are heterogeneous and sparse crop systems that present significant spatial variability within and between fields. Thus, these systems need precise monitoring techniques accounting for their variability to adapt the farming strategies accordingly. In this way, farmers can increase the effectiveness of the inputs supplied, use them precisely, and enhance the farming system resilience (Sarri et al., 2020).

Chapter I

Remote sensing imagery from satellites, aeroplanes, UAVs or drones has been acknowledged as a prime tool to create spatial information for crop evapotranspiration (ET), which will be applied in standard procedures for assessing crop water requirements (e.g. the FAO-56 method) (Allen et al., 1998). However, remote sensing approaches need high temporal and spatial resolution data able to monitor the crop biophysical and physiological parameters, and field measurements for model calibration (Bonfante et al., 2019; Calera et al., 2017).

Recently, the increase in free and open access imagery (e.g., European Space Agency; 10-m imagery acquired by Sentinel-2), the number of commercial sensors at higher spatial resolution (e.g., of 1–5 m, WorldView2, PLEIADES, DMC, DEIMOS and Venµs) and the availability of low-cost drones with multispectral and thermal cameras have made these methodologies more widespread and operational (Bonfante et al., 2019).

The most frequently used UAVs cameras in agriculture include thermal, multispectral, hyperspectral and red-green-blue (RGB), which are used depending on the type of crop trait/status under study (Gago et al., 2015). Thermal and hyperspectral cameras are recommended to study biotic and abiotic stress; while multispectral and red-green-blue (RGB) cameras are recommended for crop growth/biomass assessment (Ezzene et al., 2019). Specifically, thermal imaging is highly suited for quick detection of drought stress and irrigation scheduling, because crop transpiration is an energy-demanding process that linearly decreases the surface temperature of vegetation as the soil water status is not limiting. Surface temperature is usually normalized, and the most common method makes use of the crop water stress index (CWSI). Besides, specific narrow-band vegetation indexes (VIs) derived from hyperspectral sensors form a relatively new method to study plant water status (Maese and Steppe, 2019).

UAVs are frequently coupled with geostatistical tools to perform accurate mapping within and between fields variables and produce quasi-real-time maps of crop water status, which helps the farmer on the decision-making process (Abdullahi et al., 2015; Gago et al., 2015).

Additionally, the new technologies enable to create user-friendly decision support systems, such as the wireless sensor network (WSN). These tools embed a network of soil-plant water status sensors, hardware and smartphone applications. The sensor measurements are sent to a platform that is interfaced with a communication board and then transferred to a database, which is accessible to the farmer through a website. This expert system would let the farmer perform feedback control of irrigation by maintaining the soil water content within a pre-delimited optimal range, and thus to identify the most appropriate irrigation farm management. The implementation of WSN could generate positive economic returns and reduce environmental impacts.