Special Issue "Modelling for Water Management in Agriculture Systems"

A special issue of Agriculture (ISSN 2077-0472).

Deadline for manuscript submissions: closed (28 September 2018)

Special Issue Editor

Guest Editor
Dr. Saseendran S. Anapalli

ARS, Agr Syst Res Unit, USDA, Ft Collins, CO 80526, USA
Website | E-Mail
Interests: cropping system model; agricultural system model; water use efficiency

Special Issue Information

Dear Colleagues,

Scientists in the mid-to-late 20th century started feeling the need to synthesize, to a whole systems level, the quantitative knowledge obtained from numerous component experiments in cropping systems research, so that their research results could be transferable to other soils and climates. In this context, crop models were developed to effectively integrate and synthesize knowledge from different disciplines, encompassing plant, soil, water, and atmosphere, and simulate the impact of management and resources on crop production. In essence, crop models provide a vehicle for delivering detailed knowledge on a systems level directly to users.

With the competing demands for water (agriculture vs. urban needs) as well as grains (food vs. fuel), location-specific, sound, system level knowledge of crop responses to water is critical for management decisions in optimizing productivity in both rainfed and irrigated agriculture. The measured crop yield responses to irrigation water, however, may vary from year to year due to the observed wide variations in the severity and timing of the water inputs and other biotic and abiotic stresses controlled by location specific climate variability characteristics. The actual irrigation water applied to meet the needed crop water requirements will also vary with the method of irrigation and water application efficiency in the field. As such, like any other agro-management practice, the transfer of location specific irrigation technologies across locations has confronted with practical difficulties owing to different precipitation regimes, soils, and landscapes; therefore, for use in optimizing water productivity, location specific crop-water relationships that are averaged over longer term weather conditions are prerequisites. Obtaining such information based on measured experimental data at a specific location is very expensive to obtain, and hence seldom available worldwide. In this context, cropping system models based on state-of-the-science knowledge-base in soil-water-nutrient-plant-air and long-term weather data are being used to develop this information. In this perspective, comprehensive process oriented agricultural systems models are tools that provide a systems approach and a fast alternative method for extrapolating short-term experiments across climates and soils. 

Dr.  Saseendran S. Anapalli
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agriculture is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 550 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Cropping system model
  • Agricultural system model
  • Water use efficiency
  • Cropping system model
  • Irrigation
  • Limited water irrigation
  • Evapotranspiration
  • Crop water requirements
  • Irrigation efficiency

Published Papers (4 papers)

View options order results:
result details:
Displaying articles 1-4
Export citation of selected articles as:

Research

Open AccessArticle Factors Affecting Collective Actions in Farmer-Managed Irrigation Systems of Nepal
Agriculture 2018, 8(6), 77; https://doi.org/10.3390/agriculture8060077
Received: 22 April 2018 / Revised: 26 May 2018 / Accepted: 30 May 2018 / Published: 2 June 2018
PDF Full-text (496 KB) | HTML Full-text | XML Full-text
Abstract
The efficient management of agricultural water to meet its growing demand and to increase farm productivity has become a major concern in many agrarian countries. Various management principles, such as changes in governance, and crafting locally devised institutions have been prioritised in recent
[...] Read more.
The efficient management of agricultural water to meet its growing demand and to increase farm productivity has become a major concern in many agrarian countries. Various management principles, such as changes in governance, and crafting locally devised institutions have been prioritised in recent literature. The Nepalese government has considered farmers’ governance in managing irrigation systems as a successful irrigation policy. One of the measures for a successful farmer-managed irrigation systems (FMIS), each farmer must engage actively in collective choice actions and share both the costs and benefits from the system, proportionately. Various aspects in an institution affects farmers’ cooperative behavior to engage in the irrigation management process. A study of 232 FMIS in Nepal revealed that there is a need for a revised institutional design that can empower farmers by investing them with more defined property rights so that they can actively engage in maintenance activities. Full article
(This article belongs to the Special Issue Modelling for Water Management in Agriculture Systems)
Figures

Figure 1

Open AccessArticle Modelling Soil Water Content in a Tomato Field: Proximal Gamma Ray Spectroscopy and Soil–Crop System Models
Agriculture 2018, 8(4), 60; https://doi.org/10.3390/agriculture8040060
Received: 12 March 2018 / Revised: 6 April 2018 / Accepted: 17 April 2018 / Published: 19 April 2018
Cited by 2 | PDF Full-text (4451 KB) | HTML Full-text | XML Full-text
Abstract
Proximal soil sensors are taking hold in the understanding of soil hydrogeological processes involved in precision agriculture. In this context, permanently installed gamma ray spectroscopy stations represent one of the best space–time trade off methods at field scale. This study proved the feasibility
[...] Read more.
Proximal soil sensors are taking hold in the understanding of soil hydrogeological processes involved in precision agriculture. In this context, permanently installed gamma ray spectroscopy stations represent one of the best space–time trade off methods at field scale. This study proved the feasibility and reliability of soil water content monitoring through a seven-month continuous acquisition of terrestrial gamma radiation in a tomato test field. By employing a 1 L sodium iodide detector placed at a height of 2.25 m, we investigated the gamma signal coming from an area having a ~25 m radius and from a depth of approximately 30 cm. Experimental values, inferred after a calibration measurement and corrected for the presence of biomass, were corroborated with gravimetric data acquired under different soil moisture conditions, giving an average absolute discrepancy of about 2%. A quantitative comparison was carried out with data simulated by AquaCrop, CRITeRIA, and IRRINET soil–crop system models. The different goodness of fit obtained in bare soil condition and during the vegetated period highlighted that CRITeRIA showed the best agreement with the experimental data over the entire data-taking period while, in presence of the tomato crop, IRRINET provided the best results. Full article
(This article belongs to the Special Issue Modelling for Water Management in Agriculture Systems)
Figures

Figure 1

Open AccessArticle Harvesting Method Affects Water Dynamics and Yield of Sweet Orange with Huanglongbing
Agriculture 2018, 8(3), 38; https://doi.org/10.3390/agriculture8030038
Received: 8 February 2018 / Revised: 1 March 2018 / Accepted: 8 March 2018 / Published: 10 March 2018
PDF Full-text (1799 KB) | HTML Full-text | XML Full-text
Abstract
Changes in grove management practices may change crop water dynamics. The objective of this study was to estimate sap flow, stem water potential (Ψstem), and citrus yield as affected by harvesting methods in sweet orange (Citrus sinensis) trees affected
[...] Read more.
Changes in grove management practices may change crop water dynamics. The objective of this study was to estimate sap flow, stem water potential (Ψstem), and citrus yield as affected by harvesting methods in sweet orange (Citrus sinensis) trees affected by Huanglongbing. The study was initiated in March 2015 for two years on five-year-old commercial sweet orange trees at a commercial grove located at Felda, Florida (26.61° N, 81.48° W) on Felda fine sand soil (Loamy, siliceous, superactive, hyperthermic Arenic Endoaqualfs). All measurements were replicated before and after harvest in four experiments (A, B, C and D) under hand and mechanical harvesting treatments. Sap flow measurements were taken on four trees per treatment with two sensors per tree. Sap flow measured by the heat balance method at hourly intervals during March and April of 2015 and 2016 significantly declined after harvesting by 25% and 35% after hand and mechanical harvesting, respectively. Ψstem measured after harvest was significantly higher than measurements before harvest. The average value of Ψstem measured increased by 10% and 6% after hand and mechanical harvesting, respectively. Mechanical harvesting exhibited lower fruit yields that averaged between 83%, 63%, 49% and 36% of hand-harvested trees under A, B, C and D experiments, respectively. It is concluded that the hand harvesting method is less stressful and less impactful on tree water uptake and fruit yield compared with mechanical harvesting. Full article
(This article belongs to the Special Issue Modelling for Water Management in Agriculture Systems)
Figures

Figure 1

Open AccessFeature PaperArticle Evaluation of Crop to Crop Water Demand Forecasting: Tomatoes and Bell Peppers Grown in a Commercial Greenhouse
Agriculture 2017, 7(12), 104; https://doi.org/10.3390/agriculture7120104
Received: 24 October 2017 / Revised: 29 November 2017 / Accepted: 13 December 2017 / Published: 20 December 2017
PDF Full-text (2985 KB) | HTML Full-text | XML Full-text
Abstract
Forecasting crop water demand is a critical part of any greenhouse’s day-to-day operations. This study focuses on a region located in Essex County, Ontario Canada where water demand is dominated by commercial greenhouse operations (78% of capacity). Development of complex and elaborate forecasting
[...] Read more.
Forecasting crop water demand is a critical part of any greenhouse’s day-to-day operations. This study focuses on a region located in Essex County, Ontario Canada where water demand is dominated by commercial greenhouse operations (78% of capacity). Development of complex and elaborate forecasting methods such as artificial neural networks (ANN) can be costly to develop and implement, especially with the limited resources available to greenhouses. This study proposes simplified forecasting methods that would be used in conjunction with a more complex base model architecture. These simplified methods use one crop water usage as an indicator of another’s, and is titled crop-to-crop forecasting (C2C). In this study, tomatoes and peppers were evaluated, and three C2C models were developed along with an ANN base model to provide a basis for evaluation. The models were created using a dataset containing hourly watering data along with climatic and temporal data for the period between June 2015 and August 2016. The three C2C architectures used were linear regression (LR), quotient method (QM), and feed-forward neural network (FFNN), compared with the (ANN) model, which is a feed-forward neural network with extra inputs (FFNN-EI). Each model was evaluated using the root mean squared error (RMSE) and the normalized root mean squared error (NRMSE). The results show that all C2C methods have higher RMSE and NRMSE than that of the base model, with an average RMSE increase of 12% for peppers and 29% for tomatoes. Full article
(This article belongs to the Special Issue Modelling for Water Management in Agriculture Systems)
Figures

Figure 1

Back to Top