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Dr. Mahesh Maskey
ucdavis

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0 Hydrology
0 Mathmatics
0 fractal
0 statistical analysis
0 agricultural

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Journal article
Published: 22 August 2019 in Water
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A surface energy balance model was conceived to estimate crop transpiration and soil evaporation in orchards and vineyards where the floor is partially wetted by micro-irrigation systems. The proposed surface energy balance model for partial wetting (SEB-PW) builds upon previous multiple-layer modelling approaches to estimate the latent, sensible, and soil heat fluxes, while partitioning the total evapotranspiration ( E T ) into dry and wet soil evaporation ( λ E s o i l ) and crop transpiration ( T ). The model estimates the energy balance and flux resistances for the evaporation from dry and wet soil areas below the canopy, evaporation from dry and wet soil areas between plant rows, crop transpiration, and total crop E T . This article describes the model development, sensitivity analysis and a preliminary model evaluation. The evaluation shows that simulated hourly E T values have a good correlation with field measurements conducted with the surface renewal method and micro-lysimeter measurements in a micro-irrigated winegrape vineyard of Northern California for a range of fractional crop canopy cover conditions. Evaluation showed that hourly L E estimates had root mean square error ( R M S E ) of 58.6 W m−2, mean absolute error ( M A E ) of 35.6 W m−2, Nash-Sutcliffe coefficient ( C N S ) of 0.85, and index of agreement ( d a ) of 0.94. Daily soil evaporation ( E s ) estimations had R M S E of 0.30 mm d−1, M A E of 0.24 mm d−1, C N S of 0.87, and d a of 0.94. E s estimation had a coefficient of determination ( r 2 ) of 0.95, when compared with the micro-lysimeter measurements, which showed that E s can reach values from 28% to 46% of the total E T after an irrigation event. The proposed SEB-PW model can be used to estimate the effect and significance of soil evaporation from wet and dry soil areas on the total E T , and to inform water balance studies for optimizing irrigation management. Further evaluation is needed to test the model in other partially wetted orchards and to test the model performance during all growing seasons and for different environmental conditions.

ACS Style

Camilo Souto; Octavio Lagos; Eduardo Holzapfel; Mahesh Lal Maskey; Lynn Wunderlich; Kristen Shapiro; Giulia Marino; Richard Snyder; Daniele Zaccaria. A Modified Surface Energy Balance to Estimate Crop Transpiration and Soil Evaporation in Micro-Irrigated Orchards. Water 2019, 11, 1747 .

AMA Style

Camilo Souto, Octavio Lagos, Eduardo Holzapfel, Mahesh Lal Maskey, Lynn Wunderlich, Kristen Shapiro, Giulia Marino, Richard Snyder, Daniele Zaccaria. A Modified Surface Energy Balance to Estimate Crop Transpiration and Soil Evaporation in Micro-Irrigated Orchards. Water. 2019; 11 (9):1747.

Chicago/Turabian Style

Camilo Souto; Octavio Lagos; Eduardo Holzapfel; Mahesh Lal Maskey; Lynn Wunderlich; Kristen Shapiro; Giulia Marino; Richard Snyder; Daniele Zaccaria. 2019. "A Modified Surface Energy Balance to Estimate Crop Transpiration and Soil Evaporation in Micro-Irrigated Orchards." Water 11, no. 9: 1747.

Journal article
Published: 08 July 2019 in Atmosphere
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Strawberry is a high value and labor-intensive specialty crop in California. The three major fruit production areas on the Central Coast complement each other in producing fruits almost throughout the year. Forecasting strawberry yield with some lead time can help growers plan for required and often limited human resources and aid in making strategic business decisions. The objectives of this paper were to investigate the correlation among various weather parameters related with strawberry yield at the field level and to evaluate yield forecasts using the predictive principal component regression (PPCR) and two machine-learning techniques: (a) a single layer neural network (NN) and (b) generic random forest (RF). The meteorological parameters were a combination of the sensor data measured in the strawberry field, meteorological data obtained from the nearest weather station, and calculated agroclimatic indices such as chill hours. The correlation analysis showed that all of the parameters were significantly correlated with strawberry yield and provided the potential to develop weekly yield forecast models. In general, the machine learning technique showed better skills in predicting strawberry yields when compared to the principal component regression. More specifically, the NN provided the most skills in forecasting strawberry yield. While observations of one growing season are capable of forecasting crop yield with reasonable skills, more efforts are needed to validate this approach in various fields in the region.

ACS Style

Mahesh L. Maskey; Tapan B Pathak; Surendra K. Dara. Weather Based Strawberry Yield Forecasts at Field Scale Using Statistical and Machine Learning Models. Atmosphere 2019, 10, 378 .

AMA Style

Mahesh L. Maskey, Tapan B Pathak, Surendra K. Dara. Weather Based Strawberry Yield Forecasts at Field Scale Using Statistical and Machine Learning Models. Atmosphere. 2019; 10 (7):378.

Chicago/Turabian Style

Mahesh L. Maskey; Tapan B Pathak; Surendra K. Dara. 2019. "Weather Based Strawberry Yield Forecasts at Field Scale Using Statistical and Machine Learning Models." Atmosphere 10, no. 7: 378.

Journal article
Published: 12 April 2019 in Agriculture
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In California, a significant percentage of the pistachio acreage is in the San Joaquin Valley on saline and saline-sodic soils. However, irrigation management practices in commercial pistachio production are based on water-use information developed nearly two decades ago from experiments conducted in non-saline orchards sprinkler-irrigated with good quality water. No information is currently available that quantify the effect of salinity or combined salinity and sodicity on water use of micro-irrigated pistachio orchards, even though such information would help growers schedule irrigations and control soil salinity through leaching. To fill this gap, a field research study was conducted in 2016 and 2017 to measure the actual evapotranspiration (ETa) from commercial pistachio orchards grown on non-saline and saline-sodic soils in the southern portion of the San Joaquin Valley of California. The study aimed at investigating the functional relations between soil salinity/sodicity and tree performance, and understanding the mechanisms regulating water-use reduction under saline and saline-sodic conditions. Pistachio ETa was measured with the residual of energy balance method using a combination of surface renewal and eddy covariance equipment. Saline and saline-sodic conditions in the soil adversely affected tree performance with different intensity. The analysis of field data showed that ETa, light interception by the tree canopy, and nut yield were highly and linearly related (r2 > 0.9). Moving from non-saline to saline and saline-sodic conditions, the canopy light interception decreased from 75% (non-saline) to around 50% (saline) and 30% (saline-sodic), and ETa decreased by 32% to 46% relative to the non-saline orchard. In saline-sodic soils, the nut yield resulted around 50% lower than that of non-saline orchard. A statistical analysis performed on the correlations between soil physical-chemical parameters and selected tree performance indicators (ETa, light interception, and nut yield) revealed that the sodium adsorption ratio (SAR) adversely affected tree performance more than the soil electrical conductivity (ECe). Results suggest that secondary effects of sodicity (i.e., degradation of soil structure, possibly leading to poor soil aeration and root hypoxia) might have had a stronger impact on pistachio performance than did salinity in the long term. The information presented in this paper can help pistachio growers and farm managers better tailor irrigation water allocation and management to site-specific orchard conditions (e.g., canopy features and soil-water salinity/sodicity), and potentially lead to water and energy savings through improved irrigation management practices.

ACS Style

Giulia Marino; Daniele Zaccaria; Richard L. Snyder; Octavio Lagos; Bruce D. Lampinen; Louise Ferguson; Stephen R. Grattan; Cayle Little; Kristen Shapiro; Mahesh Lal Maskey; Dennis L. Corwin; Elia Scudiero; Blake L. Sanden. Actual Evapotranspiration and Tree Performance of Mature Micro-Irrigated Pistachio Orchards Grown on Saline-Sodic Soils in the San Joaquin Valley of California. Agriculture 2019, 9, 76 .

AMA Style

Giulia Marino, Daniele Zaccaria, Richard L. Snyder, Octavio Lagos, Bruce D. Lampinen, Louise Ferguson, Stephen R. Grattan, Cayle Little, Kristen Shapiro, Mahesh Lal Maskey, Dennis L. Corwin, Elia Scudiero, Blake L. Sanden. Actual Evapotranspiration and Tree Performance of Mature Micro-Irrigated Pistachio Orchards Grown on Saline-Sodic Soils in the San Joaquin Valley of California. Agriculture. 2019; 9 (4):76.

Chicago/Turabian Style

Giulia Marino; Daniele Zaccaria; Richard L. Snyder; Octavio Lagos; Bruce D. Lampinen; Louise Ferguson; Stephen R. Grattan; Cayle Little; Kristen Shapiro; Mahesh Lal Maskey; Dennis L. Corwin; Elia Scudiero; Blake L. Sanden. 2019. "Actual Evapotranspiration and Tree Performance of Mature Micro-Irrigated Pistachio Orchards Grown on Saline-Sodic Soils in the San Joaquin Valley of California." Agriculture 9, no. 4: 76.

Review
Published: 26 February 2018 in Agronomy
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California is a global leader in the agricultural sector and produces more than 400 types of commodities. The state produces over a third of the country’s vegetables and two-thirds of its fruits and nuts. Despite being highly productive, current and future climate change poses many challenges to the agricultural sector. This paper provides a summary of the current state of knowledge on historical and future trends in climate and their impacts on California agriculture. We present a synthesis of climate change impacts on California agriculture in the context of: (1) historic trends and projected changes in temperature, precipitation, snowpack, heat waves, drought, and flood events; and (2) consequent impacts on crop yields, chill hours, pests and diseases, and agricultural vulnerability to climate risks. Finally, we highlight important findings and directions for future research and implementation. The detailed review presented in this paper provides sufficient evidence that the climate in California has changed significantly and is expected to continue changing in the future, and justifies the urgency and importance of enhancing the adaptive capacity of agriculture and reducing vulnerability to climate change. Since agriculture in California is very diverse and each crop responds to climate differently, climate adaptation research should be locally focused along with effective stakeholder engagement and systematic outreach efforts for effective adoption and implementation. The expected readership of this paper includes local stakeholders, researchers, state and national agencies, and international communities interested in learning about climate change and California’s agriculture.

ACS Style

Tapan B. Pathak; Mahesh L. Maskey; Jeffery A. Dahlberg; Faith Kearns; Khaled M. Bali; Daniele Zaccaria. Climate Change Trends and Impacts on California Agriculture: A Detailed Review. Agronomy 2018, 8, 25 .

AMA Style

Tapan B. Pathak, Mahesh L. Maskey, Jeffery A. Dahlberg, Faith Kearns, Khaled M. Bali, Daniele Zaccaria. Climate Change Trends and Impacts on California Agriculture: A Detailed Review. Agronomy. 2018; 8 (3):25.

Chicago/Turabian Style

Tapan B. Pathak; Mahesh L. Maskey; Jeffery A. Dahlberg; Faith Kearns; Khaled M. Bali; Daniele Zaccaria. 2018. "Climate Change Trends and Impacts on California Agriculture: A Detailed Review." Agronomy 8, no. 3: 25.

Book chapter
Published: 23 November 2017 in Fractals
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ACS Style

Carlos E. Puente; Mahesh L. Maskey; Bellie Sivakumar. Combining Fractals and Multifractals to Model Geoscience Records. Fractals 2017, 297 -332.

AMA Style

Carlos E. Puente, Mahesh L. Maskey, Bellie Sivakumar. Combining Fractals and Multifractals to Model Geoscience Records. Fractals. 2017; ():297-332.

Chicago/Turabian Style

Carlos E. Puente; Mahesh L. Maskey; Bellie Sivakumar. 2017. "Combining Fractals and Multifractals to Model Geoscience Records." Fractals , no. : 297-332.

Journal article
Published: 01 August 2017 in Journal of Hydrologic Engineering
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ACS Style

Mahesh L. Maskey; Carlos E. Puente; Bellie Sivakumar; Andrea Cortis. Deterministic Simulation of Mildly Intermittent Hydrologic Records. Journal of Hydrologic Engineering 2017, 22, 04017026 .

AMA Style

Mahesh L. Maskey, Carlos E. Puente, Bellie Sivakumar, Andrea Cortis. Deterministic Simulation of Mildly Intermittent Hydrologic Records. Journal of Hydrologic Engineering. 2017; 22 (8):04017026.

Chicago/Turabian Style

Mahesh L. Maskey; Carlos E. Puente; Bellie Sivakumar; Andrea Cortis. 2017. "Deterministic Simulation of Mildly Intermittent Hydrologic Records." Journal of Hydrologic Engineering 22, no. 8: 04017026.