Reference Crop Research Articles

СОРТОВАЯ СПЕЦИФИЧНОСТЬ ОПТИКО-БИОЛОГИЧЕСКИХ СВОЙСТВ И ФИТОМЕТРИЧЕСКИХ ПАРАМЕТРОВ ПОСЕВОВ ОЗИМОЙ ПШЕНИЦЫ

Identification of varietal specificity of opto-biological properties of field crops is necessary for adequate interpretation of satellite images, rapid determination of phytometric parameters of crops, indication of the level of their development and justification of corrective technological solutions. The aim of the research was to evaluate the opto-biological properties and phytometric parameters of diverse biological varieties of winter wheat; identify their relationship and inter-variety variability in high-tech reference crops based on satellite and ground monitoring. Field studies were conducted in 2019-2020 on the site of competitive ecological variety testing laid on the experimental field of the Orenburg State Agrarian University in the zone of the southern steppes of the Orenburg Cis-Urals (Preduralye). During the study, we found that agrocenoses of different winter wheat varieties differ markedly in opto-biological properties expressed in normalized difference vegetation index (NDVI) values. Varieties ‘Grom’, ‘Don 95’, ‘Bystritsa’, ‘Spartak’, ‘Stanichnaya’ and ‘Sintetik’ were characterized by the lowest average NDVI values during the growing season (0.57-0.59). Higher NDVI values (0.67) were observed in the crops of ‘Zhemchuzhina Povolzhya’, ‘Kolos Orenburzhya’, ‘Orenburgskaya 105’, ‘Rifey’ and ‘Pionerskaya 32’ varieties. The variability between the varieties according to the lowest (0.51 – ‘Grom’, 0.52 – ‘Don 95’) and the highest (0.69 – ‘Kolos Orenburzhya’, 0.70 – ‘Pionerskaya 32’, 0.69 – ‘Orenburgskaya 105’) NDVI values during the maximum assimilation apparatus development reached 0.17-0.19 units (phase of full spring tillering). By the heading, when NDVI was 0.78–0.79 and 0.85 units, respectively, this difference somewhat leveled but, in general, remained significant – at the level of 0.08-0.11 units (10.9–14.9 %). The largest leaf area (30 thousand m2/ha) was typical for the crops of ‘Saratovskaya 17’, ‘Kolos Orenburzhya’, ‘Orenburgskaya 105’, ‘Rifey’ and ‘Pionerskaya 32’. There was no direct inter-variety relationship between the leaf area and NDVI of winter wheat crops (R 2= 0.38).

Study on Vegetation Water Requirements of Robinia pseudoacacia and its mixed plantation in Loess Gully Region

By calculating the vegetation water requirements(ETC ) of the typical plantations of Robinia pseudoacacia and Robinia pseudoacacia-Platycladus orientalis mixed plantation in the Loess Gully Region and analysing the coupling relationship between rainfall(P) and water requirements of each plantation, thereby scientifically guides local tree planting and afforestation, and better vegetation restoration.The Penman-Monteith formula is used to calculate the reference crop evapotranspiration in the study region, and then the double crop coefficient method is used to calculate the daily ETC of Robinia pseudoacacia and its mixed plantation in different hydrological years. The result shows that from dry year to normal year, the ETC-RP-CO is less than ETC-RP by 48.8 mm and 52.2 mm respectively, and the growth season accounts for the largest proportion. The ETC-RP-PO and ETC-RP are not satisfied in most months of the growth season. Among them, the initial period and the development period of the growth season are the largest, with the largest water requirements deficits(D) reaching 58.65mm, 51.32mm (dry year) and 106.21mm, 93.18mm (normal years).The ETC of two plantation increases from dry year to normal year.And the D also increases when vegetation water requirements satisfaction(S) increases, but the increase rate is expressed ETC > S > D.

Evaluation of the impact of climate change on reference crop evapotranspiration in Hamedan-Bahar plain

Climate change was simulated using the HADCM3 general circulation model and was downscaled using the LARS-WG downscaling model. After downscaling and uncertainty analysis, climatic parameters were generated until year 2090, assuming three emission scenarios A1, B1 and A1B. After fitting an equation for ETo, its future values were calculated for the simulation period. Results indicated an increase in the mean annual ETo under all three scenarios. The highest increase occurred under the A2 scenario and the lowest increase occurred under the B1 scenario. Comparison of monthly ETo values showed that although greenhouse gas emissions in the next 30-year period did not lead to a perceptible increase in ETo but severe long-term increase was predictable. According to predictions, the mean monthly ETo in the [cold months] in different scenarios did not change significantly and much of the increase occurring in the mean annual ETo was due to a significant increase in the monthly ETo in [hot months] of the year.

Mechanics and Prospects of Groundnut Value Chain in Mullaitivu District, Sri Lanka

Understanding the dynamics of food value chains is pivotal to enhance its efficiencies. Groundnut was opted as the reference crop. The study was administered as value chain analysis. Gross Margin (GM) and Index of Marketing Efficiency (MEI) were determined. Mullaitivu district was selected as the research site with a sample of 56. Farmers recorded the highest GM (40.34%) and their interest in further profit in means of investment and expansion was less. Three channels were identified. The highest MEI (0.94) was recorded for ‘Channel 3’. ‘Channel 2’ indicates the lowest figure for the MEI (0.23). The key value addition spot in the whole value chain is the Colombo-based wholesalers.

Comparative evaluation of actual evapotranspiration of capsicum inside and outside of naturally ventilated polyhouse

This study was conducted to study the relationship between capsicum crop evapotranspiration inside (ETCin) and crop evapotranspiration outside (ETCout) the naturally ventilated polyhouse (NVPH) using meteorological parameters. Polyhouse has a straightway impact on air temperature and relative humidity while it indirectly influences soil temperature and soil moisture inside the structure. Under this study, crop evapotranspiration was estimated by conventional method i.e., obtaining reference evapotranspiration from weather data recorded inside the polyhouse and multiplying it with crop coefficient values of capsicum crop. Reference crop evapotranspiration inside and outside the polyhouse found as 745.19 mm and 590.22 mm, respectively whereas capsicum crop evapotranspiration inside and outside the polyhouse was 868.40 mm and 694.16 mm, respectively. The results of the study revealed that the relationship between weekly ETCin and ETCout can be expressed mathematically as ETCin = 0.84 ETCout. This implies that, there was approximately 15 % lower crop evapotranspiration requirement for the capsicum crop inside the naturally ventilated polyhouse as compared to outside the polyhouse.

Improved Hargreaves Model Based on Multiple Intelligent Optimization Algorithms to Estimate Reference Crop Evapotranspiration in Humid Areas of Southwest China

Reference crop evapotranspiration (ET0) is an important indicator for precise regulation of crop water content, irrigation forecast formulation, and regional water resources management. The Hargreaves model (HG) is currently recognized as the simplest and most effective ET0 estimation model. To further improve the prediction accuracy of the HG model, this study is based on the data of 98 meteorological stations in southwest China (1961–2019), using artificial bee colony (ABC), differential evolution (DE) and particle swarm optimization (PSO) algorithms to calibrate the HG model globally. The standard ET0 value was calculated by FAO-56 Penman–Monteith (PM) model. We compare the calculation accuracy of 3 calibrated HG models and 4 empirical models commonly used (Hargreaves, Priestley–Taylor, Imark–Allen and Jensen–Hais). The main outcomes demonstrated that on a daily scale, the calibrated HG models (R2 range 0.74–0.98) are more accurate than 4 empirical models (R2 range 0.55–0.84), and ET0-PSO-HG has the best accuracy, followed by ET0-ABC-HG and ET0-DE-HG, with average R2 of 0.83, 0.82 and 0.80, average RRMSE of 0.23 mm/d, 0.25 mm/d and 0.26 mm/d, average MAE of 0.52 mm/d, 0.53 mm/d and 0.57 mm/d, and average GPI of 0.17, 0.05, and 0.04, respectively; on a monthly scale, ET0-PSO-HG also has the highest accuracy, followed by ET0-ABC-HG and ET0-DE-HG, with median R2 of 0.96, 0.95 and 0.94, median RRMSE of 0.16 mm/d, 0.17 mm/d and 0.18 mm/d respectively, median MAE of 0.46 mm/d, 0.50 mm/d, and 0.55 mm/d, median GPI of 1.12, 0.44 and 0.34, respectively. The calibrated HG models (relative error of less than 10.31%) are also better than the four empirical models (relative error greater than 16.60%). Overall, the PSO-HG model has the most accurate ET0 estimation on daily and monthly scales, and it can be suggested as the preferred model to predict ET0 in humid regions in southwest China regions.

Interpretable vs. noninterpretable machine learning models for data-driven hydro-climatological process modeling

Due to their enhanced predictive capabilities, noninterpretable machine learning (ML) models (e.g. deep learning) have recently gained a growing interest in analyzing and modeling earth & planetary science data. However, noninterpretable ML models are often treated as “black boxes” by end-users, which could limit their applicability in critical decision making processes. In this paper, we compared the predictive capabilities of three interpretable ML models with three noninterpretable ML models to answer the overarching question: Is it essential to use noninterpretable ML models for enhanced model predictions from hydro-climatological datasets? The ML model development and comparative analysis were performed using measured climate data and synthetic reference crop evapotranspiration (ETo) data, with varying levels of missing values, from five weather stations across the karstic Edwards aquifer region in semi-arid south-central Texas. Our analysis revealed that interpretable tree-based ensemble models produce comparable results to noninterpretable deep learning models on structured hydro-climatological datasets. We showed that the tree-based ensemble model is also capable of imputing varying levels of missing climate data at the weather stations, employing the newly developed sequential transfer-learning technique. We applied an explainable machine learning (eXML) framework to quantify the global order of importance of hydro-climatic (predictor) variables on ETo, while highlighting the local dependencies and interactions amongst the predictors and ETo. The eXML framework also revealed the inflection points of the climate variables at which the transition from low to high daily ETo rates occur. The ancillary explainability of ML models are expected to increase users’ confidence and support any future decision-making process in water resource management.

Estimating of Wheat Water Requirement Using Remote Sensing at El-Menia Governorate Desert Fringe-Egypt

In hyper-arid and arid zones, the crop water requirement management is considered the vital component for sustaining the crop production particularly under drought condition. As such, further investigation is needed to determine optimal water requirements to avoid wasting water in zones already facing water shortages.Further, estimating reference Evapotranspiration(ET0)and Crop Coefficient(Kc)is fundamental requirement of agricultural water management. Thus, the aim of this study is determining an actual crop coefficient(Kc)for winter wheat using remote sensing tools [Normalized Difference Vegetation Index(NDVI)and Soil-Adjusted Vegetation Index(SAVI)] - obtained from Sentinel-2A satellite images, as well as the influence of Accumulated Growing Degree Days(AGDD)for wheat on NDVI and SAVI. Consequently,data were obtained during winter wheat season(November 2019 - April 2020)on El-Menia Governorate Desert Fringe.Data analysis indicated that the total amount of AGDD required to obtain was 1408.37 C°/season for wheat to develop in its life cycle. Moreover, SAVI value recorded the highest value(0.77)in January when the NDVI obtained 0.53 at the same period. Values of NDVI increased dynamically and acquired was 0.53 which was the highest value after wheat obtained 863.79 C°/days(heat unit)in January. Furthermore, there is a linear relation between NDVI and actual Kc which reflect a strong correlation between them for all of the growing stage. Finally, the actual water requirement was 2532.68 m3/fed/season, which is a less than value (2791 m3/fed/season)calculated FAO method. Actual crop coefficients[Kc (actual)]estimated from remote sensing(RS)using NDVI, SAVI with the AGDD equation is beneficial for irrigation scheduling, evaluation of irrigation, water use efficiency and project performance and agricultural water budgets.

Estimation of evapotranspiration for paddy under alternate wetting and drying irrigation practice*

AbstractCrop evapotranspiration (ETC) of rice under an alternate wetting and drying (AWD) irrigation practice may be different from conventional (CON) irrigation methods due to the drying (water stress) phase. The focus of this study was to identify a suitable approach for estimating ETC for a rice crop under AWD practice. Field lysimeters were used for estimating daily ETC under AWD and CON practices during monsoon and non‐monsoon seasons. Lysimeter‐based evapotranspiration (ETC_Lys) was compared with that resulting from the product of reference evapotranspiration (FAO‐56 Penman–Monteith equation), crop coefficient (FAO tabulated values‐KC_Tab and FAO equation‐KC_Eqn) and soil water stress coefficient (linear equation‐KS_Lin; FAO equation‐KS_FAO; logarithmic equation‐KS_Log). The KC_Eqn and KS_FAO methods were found to be better estimates for KC and KS, respectively, for AWD practice. The mean KC in initial, mid and late season for AWD were found to be very close to CON practice. The KS was observed to vary from 1 to 0.15 for AWD. Overall, AWD irrigation practice saved 10–20% irrigation water with about 2% reduction in grain yield and 13% reduction in ETC. The reduction in ETC indicated that KS must be considered when calculating the ETC in rice under AWD irrigation practice.

Crop Coefficient for Potato Crop Evapotranspiration Estimation by Field Water Balance Method in Semi-Arid Region, Maharashtra, India

Crop evapotranspiration (ETc) estimation is essential for many studies such as irrigation system design and management, crop yield simulation, and water resource planning and management. Field studies were conducted at MPKV Rahuri, Maharashtra from 2015 - 2016 and 2016 - 2017 (2 years) in clay soils to determine crop evapotranspiration and crop coefficients (Kc) of the potato crop. The experimental area was cultivated with irrigation applied at 7-days interval. The irrigation scheduling was based on the field water balance approach. The crop evapotranspiration was determined by the field water balance; reference evapotranspiration (ETo) by the Penman-Monteith method and crop coefficients were computed using the standard FAO-56 methodology. The total reference evapotranspiration (ETo) and crop evapotranspiration (ETc) were 226 and 240 mm for the year 2015–2016 and 248 and 250 mm for the year 2016–2017, respectively. The 2-year average reference evapotranspiration was 237 mm and the crop evapotranspiration was 245 mm. The average estimated Kc values for the semi-arid region during the vegetative, tuber development, and maturity stages for potato are 0.55, 1.11, and 1.01, respectively. The calculated values are slightly lower than those suggested by FAO-56 for the vegetative and tuber development stages and higher for the maturity stage of potato. The estimated values of crop coefficients for potato are 11.25% higher than those suggested by FAO-56. However, observed variation between values from the FAO-Kc and Kc calculated by the field water balance is not significant. So, these values can be used for irrigation scheduling of potato in the semi-arid region.

Reference Evapotranspiration Prediction for Smart Irrigation

Irrigation is the most critical process for agriculture, but irrigation is the largest consumer of fresh water and causes the loss of large quantities because of the inaccuracy in crop water estimation. Our proposed system aims to improve irrigation management by estimating the amount of water needed by the crop accurately and reduces the number of meteorological parameters needed for such estimation. Detection of the reference crop evapotranspiration (ETo) is the most critical process in crop water estimation, that is considered through our proposed solution by implementing machine learning models using neural networks and linear regression to predict daily ETo using climate data like temperature, humidity, wind speed, and solar radiation. Comparing our system results with FAO-56 Penman-Monteith ET0 and cropwat8.0 software as benchmark, show that our proposed system is better than the linear regression model, in terms of determination coefficient (R^2)=.9677 and root mean square error(RMSE) =.1809, while the multiple linear regression model achieved determination coefficient (R^2)=.68 and root mean square error(RMSE) =3.01. Our system then used the predicted ETo and Crop coefficient (Kc) from FAO, to estimate crop evapotranspiration (ETc) for precision irrigation target.

Projection of Reference Crop Evapotranspiration under Future Climate Change in Poyang Lake Watershed, China

AbstractReference crop evapotranspiration (ET0) is of great importance in assessing the potential impacts of climate changes on hydrological cycles and the global energy balance. The spatiotemporal...

Optimizing Traditional Cropping Systems Under Climate Change: A Case of Maize Landraces and Bambara Groundnut.

Traditional crop species are reported to be drought-tolerant and nutrient-dense with potential to contribute to sustainable food and nutrition security within marginal production systems under climate change. We hypothesized that intercropping maize landraces (Zea mays L.) with bambara groundnut (Vigna subterranea (L.) Verdc.), together with optimum management strategies, can improve productivity and water use efficiency (WUE) under climate change. Using an ex-ante approach, we assessed climate change impacts and agronomic management options, such as plant ratios, and plant sequences, on yield and WUE of intercropped maize landrace and bambara groundnut. The Agricultural Production Systems sIMulator (APSIM) model was applied over four time periods; namely past (1961-1991), present (1995-2025), mid-century (2030-2060) and late-century (2065-2095), obtained from six GCMs. Across timescales, there were no significant differences with mean annual rainfall, but late century projections of mean annual temperature and reference crop evaporation (ET0) showed average increases of 3.5°C and 155mm, respectively. By late century and relative to the present, the projected changes in yield and WUE were -10 and -15% and 5 and 7% for intercropped bambara groundnut and maize landrace, respectively. Regardless of timescale, increasing plant population improved yield and WUE of intercropped bambara groundnut. Asynchronous planting increased yield and WUE for both maize landrace (5 and 14%) and bambara groundnut (35 and 47%, respectively). Most significant improvements were observed when either crop was planted 2-3 months apart. To reduce yield gaps in intercrop systems, low-cost management options like changing plant populations and sequential cropping can increase yield and WUE under projected climate change. To further increase sustainability, there is a need to expand the research to consider other management strategies such as use of other traditional crop species, fertilization, rainwater harvesting and soil conservation techniques.

Perennial rhizomatous grasses: Can they really increase species richness and abundance in arable land?—A meta‐analysis

AbstractPerennial rhizomatous grasses (PRG), such as miscanthus and switchgrass, are considered promising lignocellulosic feedstocks. Their cultivation is expected to experience a significant increase in the near future, as it offers a wide range of benefits. For instance, when PRG replace typical annual crops, positive biodiversity impacts are usually anticipated. However, to date, there is no solid, statistically strong evidence for this hypothesis. This study aims to evaluate its validity through a meta‐analysis based on an extensive systematic literature review of research comparing biodiversity attributes in PRG and common annual crops. Dynamics of species richness and abundance in response to PRG cultivation were quantitatively evaluated drawing on 220 paired comparisons from 25 studies. This includes data on five taxonomic groups—arthropods, birds, earthworms, mammals and plants—and three PRG—miscanthus, switchgrass and reed canary grass. The results indicate that biodiversity tends to be higher in PRG cultivations relative to the reference crops, but the initial hypothesis of significantly beneficial impacts could not be confirmed. Trends were specific to the individual taxonomic groups: significantly higher biodiversity was found for plants and small mammals. Positive but insignificant trends were observed for arthropods and birds, while earthworm response was neutral and insignificant. More substantial conclusions could not be drawn, which is mainly due to the low number of studies conducting biodiversity assessments in PRG cultivations that included a comparison with annual crops. In addition, a detailed analysis of the observed responses was impaired by poor reporting of the parameters influencing biodiversity in the studies reviewed, such as planting and crop density, as well as yields. For this reason, we conclude with a call for improved data reporting in biodiversity assessments of PRG cultivations and detail requirements for future biodiversity research.

Climatology and trends of reference evapotranspiration in Spain

AbstractThis study presents a climatology and trend analysis of reference crop evapotranspiration (ETo) over continental Spain and the Balearic Islands. Geographic features of the study region play a substantial role in the climatology of ETo. The highest values (in excess of 1,200 mm y−1) are found at lower elevations in the south, while the lowest values (less than 900 mm y−1) are found in the highest elevations in the north. A deep analysis reveals: (a) a low interannual variability; (b) summer accumulates more than 50% of annual values; and (c) the radiative component contribution is higher than 50%. A positive long‐term trend (1961–2014) has been detected for most of the study area, but showing contrasting situations when shorter periods (20–30 years) are analysed. A short initial period of negative trend was followed by a longer period of a positive trend. We argue that global dimming/brightening played a role in this process, as these two contrasting periods are clearly guided by the radiative component. A seasonal analysis of the trends reveals that spring and summer are the seasons showing the long‐term positive trends. Interestingly, a monthly analysis shows that spring trends are guided by March and April and summer trends are mostly guided by June, which is the month showing the highest relative changes. This could have important consequences for agriculture and natural ecosystems since this month represents the start of the summer (dry) period.

Estimating Evapotranspiration and Crop Coefficient of Vegetable Crops Using Pot Micro-lysimeters

Irrigation for crop production constitutes the most part of agricultural water worldwide. As most of the water withdrawn by agriculture is lost through crop evapotranspiration (ETC), efficient water management requires accurate and reliable estimation of ETC. To date, available methods for estimating ETC require expensive devices and extensive meteorological information, which become a problem in areas with no instruments and with limited data. The present study aimed to evaluate the performance of a technique to estimate ETC and crop coefficient (KC) of selected vegetable crops (lettuce, mustard, onion, bush bean, and “pechay”) using small plastic pots as micro-lysimeters under screen house conditions. Daily rainfall, reference crop evapotranspiration (ETO), and ETC were recorded. Correlation analysis between ETO and ETC showed a significant positive correlation in lettuce (R = 0.736), pechay (R = 0.687), onion (R = 0.761), and mustard (R = 0.467). The KC values of vegetable crops were obtained and the KC curve for each crop was generated by plotting the KC with respect to the stages of growth. Comparison between KC curves of crops obtained using the present technique with the generalized KC curve by the FAO56 Penman-Monteith (FAO56PM), the standard method for estimating ETC and KC, revealed a significant positive correlation in mustard (R = 0.998), onion (R = 0.982), and bush bean (R = 0.800) – suggesting that the KC curves of most crops obtained by using the present technique is comparable with the standard generalized KC curve by the FAO56PM. Taken together, these findings suggest that the present technique provided a close approximation of the water requirements of the crops tested. Since the technique is relatively simple and requires minimum input parameters for estimating ETC and KC compared to the standard method, the technique may be used as an alternative in remote areas where meteorological information is limited, as well as in places where urban agriculture is commonly practiced.

Spatiotemporal Analysis of Maize Water Requirement in the Heilongjiang Province of China during 1960–2015

Climate change will have a significant effect on crop water requirement (ETc). The spatial and temporal variations of water requirement of maize under climate change are essential elements when conducting a global water security assessment. In this paper, annual reference crop evapotranspiration (ET0) and the crop water requirement of maize were calculated by the single crop coefficient method. The crop water surplus deficit index (CWSDI) and coupling degree of ETc and effective precipitation (Pe) were calculated to analyze the relationship between ETc, ET0, and Pe. The result shows that maize average annual ET0, ETc, and precipitation were 552.97, 383.05, and 264.97 mm, respectively. Moreover, ET0, ETc, and Pe decreased by 3.28, 2.56, and 6.25 mm every decade from 1960 to 2015. The ETc decreased less than Pe did, which led to the decreasing of both CWSDI and the coupling degree of ETc and Pe. The tendency of ET0, ETc decreased first and then increased, while Pe and CWSDI increased first and then decreased, from west to east of the Heilongjiang Province. In addition, the highest ET0, ETc, and lowest CWSDI and Pe were found in the western part of Heilongjiang Province. This study indicated that even though the water deficit in the western region was alleviated and the water deficit in the eastern region grew gradually serious from 1960 to 2015, the drought situation in western Heilongjiang Province should still be taken seriously.

A paradigm of GIS and remote sensing for crop water deficit assessment in near real time to improve irrigation distribution plan

This study comprehensively used Remote Sensing (RS) and GIS for independently monitoring crop water adequacy in response to existing irrigation management system in Bari Doab, Pakistan. Canals’ rotation plan is developed before start of each cropping season to distribute water on fixed turn basis. The study combines the moderate (Landsat-8) and low resolution (MODIS) satellite imagery to assess near real time crop water deficit in canal command areas (CCAs) of the study area. Landsat imagery were used to develop crop classification and determine cropping pattern using time series of NDVI. MODIS’ NDVI product was employed to monitor the current crop health and develop annual reference NDVI cycle based on last 13 years’ imagery. Climatic data of WMO stations combined with DEM have also been used to determine reference evapotranspiration based on Penman-Monteith method. These inputs were combined to estimate average crop water deficit in CCAs for ongoing cropping season on 08-day interval, the interval parallel to the canal rotation plan. RS based reflectance crop coefficients (KCR) of current and reference crop cycle were developed to calculate potential and actual evapotranspiration, and then translated their difference into crop water deficit. Lag time of 08–10 days is limitation of the study due to satellites overpass time, but it can still prove very helpful considering the crop season spanned over 160–180 days. Results revealed that maximum crop water deficit (up to 9 mm) occurred in CCAs during crop growing (mid) stage. Crop water deficit showed inverse relation when plotted against intense rainfall months. Crop water deficit results could be helpful for irrigation managers to modify canals’ rotation plan in near real time according to crop health and growing stage. It would not only help in achieving equitable water distribution but also assuring supplies when required by the crops.

PRODUCTION AND QUALITY OF MINI WATERMELON UNDER DRIP IRRIGATION WITH BRACKISH WATER

ABSTRACT Watermelon plays an important role in Brazilian agribusiness. The objective of present study was to evaluate the effect of different irrigation managements and water salinity levels, on the main productive and postharvest characteristics of mini-watermelon (Citrullus lanatus) cv. ‘Sugar Baby’. The experiment was conducted in a greenhouse in a completely randomized design, arranged in a 2 x 4 factorial scheme, with four replicates. The treatments consisted of two types of irrigation management (conventional and pulse) and four saline levels of nutrient solution of fertigation (2.5; 4.5; 5.5; 6.5 dS m-1). The control treatment corresponded to the electrical conductivity of the nutrient solution prepared with local supply water. The irrigation depth was calculated by the product of reference evapotranspiration and crop coefficient. The number of pulses were defined and fractionated according to the atmospheric demand. The salinity of the fertigation solution affects some variables of the production, but does not reduce the quality of the mini-fruit cv. ‘Sugar Baby’. The management of pulse irrigation shows better results in low salinity, while conventional drip management is more suitable for high salinity (6.5 dS m-1).

Assessing the Robustness of Pan Evaporation Models for Estimating Reference Crop Evapotranspiration during Recalibration at Local Conditions

A classic method for assessing the reference crop evapotranspiration (ETo) is the pan evaporation (Epan) method that uses Epan measurements and pan coefficient (kp) models, which can be functions of relative humidity (RH), wind speed (u2), and temperature (T). The aim of this study is to present a methodology for evaluating the robustness of regression coefficients associated to climate parameters (RH, u2, and T) in pan method models during recalibration at local conditions. Two years of daily data from April to October (warm season) of meteorological parameters, Epan measurements from class A pan evaporimeter and ETo estimated by ASCE-standardized method for the climatic conditions of Thessaloniki (Greece, semi-arid environment), were used. The regression coefficients of six general nonlinear (NLR) regression Epan models were analyzed through recalibration using a technique called “random cross-validation nonlinear regression RCV-NLR” that produced 1000 random splits of the initial dataset into calibration and validation sets using a constant proportion (70% and 30%, respectively). The variance of the regression coefficients was analyzed based on the 95% interval of the highest posterior density distribution. NLR models that included coefficients with a 95% HPD interval that fluctuates in both positive and negative values were considered nonrobust. The machine-learning technique of random forests (RF) was also used to build a RF model that includes Epan, u2, RH, and T parameters. This model was used as a benchmark for evaluating the predictive accuracy of NLR models but, also, for assessing the relative importance of the predictor climate variables if they were all included in one NLR model. The findings of this study indicated that locally calibrated NLR functions that use only the Epan parameter presented better results, while the inclusion of additional climate parameters was redundant and led to underfitting.