Agroecology Research and Practice on a Crop-Field Scale

2016 ◽  
pp. 37-37
Keyword(s):  
Field Scale ◽  
Research And Practice ◽  
Crop Field

scholarly journals Hydrologic Analysis of an Intensively Irrigated Area in Southern Peru Using a Crop-Field Scale Framework

Water ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 318
Author(s):  
Xiaolu Wei ◽  
Pablo Garcia-Chevesich ◽  
Francisco Alejo ◽  
Vilma García ◽  
Gisella Martínez ◽  
...  
Keyword(s):  
Water Resources ◽  
Decision Makers ◽  
Scale Model ◽  
Substantial Contribution ◽  
Return Flow ◽  
Climate Change Scenarios ◽  
Field Scale ◽  
Southern Peru ◽  
Stream Discharge ◽  
Crop Field

Majes is one of the largest agricultural areas in the Arequipa region (southern Peru). Low seasonal precipitation and increasing water demands for agricultural irrigation, industry, and human consumption have made water supply projections a major concern. Agricultural development is becoming more extensive in this dry, sunny climate where crops can be grown year-round. However, because this type of project usually involves significant perturbations to the regional water cycle, understanding the effects of irrigation on local hydrology is crucial. Based on the watershed-scale Soil and Water Assessment Tool (SWAT), this investigation focuses on the impacts of intensive irrigation on hydrological responses in the Majes region. This study is unique because we allow for crop-field scale input within our regional-scale model to provide information at this smaller scale, which is important to inform local stakeholders and decision makers. Each hydrologic response unit (HRU) was generated to represent an individual crop field, so that management practices could be applied according to real-world scenarios. The management file of each HRU was modified to include different operation schedules for crop rotation, irrigation, harvest, and tillage. The model was calibrated and validated against monthly observed stream discharge during the 2009–2020 period. Additionally, evapotranspiration, irrigation water volume, and daily stream discharge downstream of the local river (Siguas) were used to verify the model performance. A total of 49 sub-basins and 4222 HRUs were created, with 3000 HRUs designated to represent individual crop fields. The simulation results revealed that infiltration from agricultural activities in Majes represents the majority of annual groundwater return flow, which makes a substantial contribution to streamflow downstream of the Siguas River. Simulations also suggested that groundwater flow processes and the interactions between surface and groundwater have a major impact on the water balance of the study area. Additionally, climate variability had a higher impact on surface runoff than on groundwater return flow, illustrating that the groundwater component in the study area is important for future water resources resiliency under expected climate change scenarios. Finally, there is a need to perform a follow-up implementation to provide a guideline for decision-makers to assess future sustainable water resources management under varying climatic conditions for this arid irrigated system.

Combining time series of Sentinel-1 and -2 with in-situ data for estimating soil moisture at crop field scale

2020 ◽  
Author(s):  
Marc Padilla ◽  
Ana Pérez ◽  
Mirta Pinilla
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Surface Soil ◽  
Correlation Coefficients ◽  
Field Size ◽  
Model Parameters ◽  
Field Scale ◽  
In Situ Data ◽  
Crop Field

<p>Soil moisture is one of the key variables for crop modeling and scheduling farm operations. Current available soil moisture products are generated at global or regional scales and its spatial resolution (~1km<sup>2</sup>) is usually too coarse for common small farms. Within the framework of the EU Horizon2020 funded TWIGA project, we intended to provide improved soil moisture estimates at crop field scale. The advantage of focusing at the scale of a single crop is that the algorithm selection can be based more on the retrieval accuracy rather than on the computing performance.</p><p>Time series of Sentinel-1 SAR backscatter (at VH and VV polarizations) and Sentinel-2 NDVI observations, on each crop field, were assimilated with a semiempirical polarimetric backscattering model for bare soil surfaces (Oh) coupled with a Water Cloud model (WCM). Some of the model parameters are the actual variables of interest to be estimated, in our case the daily surface soil moistures. They were estimated by a Bayesian inversion approach. The key advantage of using WCM, is that the effects of vegetation on backscatter can be taken into account, and therefore soil moisture estimates are available even when vegetation is present. The empirical model parameters (surface roughness, and A and B parameters of WCM) were calibrated with in-situ data from four stations in Ghana, with observations every 30 minutes from May to October 2019 at 10 cm depth. The calibration was based on a hierarchical Bayesian regression, to take into account that model parameter distributions might vary across land cover types and across in-situ stations themselves. The validation was based on the comparison between the soil moisture observations of one in-situ station and estimates from the model couple calibrated with the data from the other three in-situ stations. That procedure was repeated for each station. Correlation coefficients were above 0.64 and root mean square error bellow 0.065 m<sup>3</sup>/m<sup>3</sup> in two out of the four stations. Accuracy tended to be dependent on field size, due to the well known SAR speckle noise. The station with the lowest accuracy was locate on a 30x30m<sup>2</sup> field. Accuracy was additionally affected by likely sudden changes on the surface soil or vegetation during the analysis time windows. Correlation coefficients were higher (~0.85) on the time periods without such sudden changes.</p><p>Given the results of the current study, we would recommend that the location of eventual future in-situ stations should be preferably placed on larger fields, larger than 30x30m<sup>2</sup>. Further research would be needed to improve the model and understand better its limitations for an eventual operational implementation.</p><p> </p>

Not at Imminent Risk

Crisis ◽  
2015 ◽  
Vol 36 (6) ◽  
pp. 459-463
Author(s):  
Kate Monaghan ◽  
Martin Harris
Keyword(s):  
Early Intervention ◽  
Suicidal Ideation ◽  
Intervention Strategies ◽  
Group Method ◽  
Narrative Approach ◽  
Complex Issue ◽  
Research And Practice ◽  
Client Group ◽  
Types Of Information ◽  
Imminent Risk

Abstract. Background: Suicide is a pervasive and complex issue that can challenge counselors through the course of their careers. Research and practice focus heavily on crisis management and imminent risk rather than early intervention strategies. Early intervention strategies can assist counselors working with clients who have suicidal ideation, but are not at imminent risk, or with clients whose risk factors identify them as having a stronger trajectory for suicidal ideation. Aims: This systematic literature review examines the current literature on working with clients with suicidal ideation who are not at imminent risk, to ascertain the types of information and strategies available to counselors working with this client group. Method: An initial 622 articles were identified for analysis and from these 24 were included in the final review, which was synthesized using a narrative approach. Results: Results indicate that research into early intervention strategies is extremely limited. Conclusion: It was possible to describe emergent themes and practice guidelines to assist counselors working with clients with suicidal ideation but not at imminent risk.

Determinants of Interrater Reliability of Job Analysis: A Meta-analysis

2002 ◽  
Vol 18 (1) ◽  
pp. 52-62 ◽  
Author(s):  
Olga F. Voskuijl ◽  
Tjarda van Sliedregt
Keyword(s):  
Data Collection ◽  
Interrater Reliability ◽  
Meta Analysis ◽  
Job Analysis ◽  
Job Descriptions ◽  
Job Description ◽  
Research And Practice ◽  
Descriptive Information ◽  
Interrater Reliability Coefficients ◽  
Reliability Coefficients

Summary: This paper presents a meta-analysis of published job analysis interrater reliability data in order to predict the expected levels of interrater reliability within specific combinations of moderators, such as rater source, experience of the rater, and type of job descriptive information. The overall mean interrater reliability of 91 reliability coefficients reported in the literature was .59. The results of experienced professionals (job analysts) showed the highest reliability coefficients (.76). The method of data collection (job contact versus job description) only affected the results of experienced job analysts. For this group higher interrater reliability coefficients were obtained for analyses based on job contact (.87) than for those based on job descriptions (.71). For other rater categories (e.g., students, organization members) neither the method of data collection nor training had a significant effect on the interrater reliability. Analyses based on scales with defined levels resulted in significantly higher interrater reliability coefficients than analyses based on scales with undefined levels. Behavior and job worth dimensions were rated more reliable (.62 and .60, respectively) than attributes and tasks (.49 and .29, respectively). Furthermore, the results indicated that if nonprofessional raters are used (e.g., incumbents or students), at least two to four raters are required to obtain a reliability coefficient of .80. These findings have implications for research and practice.

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