Evidence map›Paper›PMID 42728253›Full record

ArticleScientific reports2026

Optimizing irrigation scheduling in apple orchards: soil moisture thresholds balance yield, fruit quality, and water use efficiency.

Nowsheen Nazir, Meinaz Nissar, Krishna Nemali, Abdul Raouf Malik, Abdul Malik, Rifat Bhat, Amit Kumar, Shafat Ahmad Banday, Nikhat J Siddiqi, Charu Bisht and 4 more

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Nowsheen NazirDivision of Fruit Science, Sher e Kashmir University of Agricultural Sciences and Technology, Kashmir, 190025, J&k, India.
Meinaz NissarDivision of Entomology, Sher-e Kashmir University of Agricultural Sciences and Technology, Kashmir, 190025, J&k, India.
Krishna NemaliPurdue University, West Lafayette, USA.
Abdul Raouf MalikDivision of Fruit Science, Sher e Kashmir University of Agricultural Sciences and Technology, Kashmir, 190025, J&k, India.
Abdul MalikDepartment of Pharmaceutics, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia.
Rifat BhatDivision of Fruit Science, Sher e Kashmir University of Agricultural Sciences and Technology, Kashmir, 190025, J&k, India.
Amit KumarDivision of Fruit Science, Sher e Kashmir University of Agricultural Sciences and Technology, Kashmir, 190025, J&k, India.
Shafat Ahmad BandayDivision of Fruit Science, Sher e Kashmir University of Agricultural Sciences and Technology, Kashmir, 190025, J&k, India.
Nikhat J SiddiqiDepartment of Medical Surgical Nursing, College of Nursing, King Saud University, Riyadh, Saudi Arabia.
Charu BishtDepartment of Genetics and Plant Breeding, Teerthanker Mahaveer College of Agricultural Sciences Teerthanker, Mahaveer University, Moradabad, U.P, India.
Showkat RasoolCentre for Artificial Intelligence and Machine Learning, Sher e Kashmir University of Agricultural Sciences and Technology, Kashmir, 190025, J&k, India.
Immad A ShahDivision of Agricultural Statistics, Sher e Kashmir University of Agricultural Sciences and Technology, Kashmir, 190025, J&k, India.
Ab Waheed WaniDepartment of Horticulture, LPU, Jalandhar, India.
Tashooq Ahmad BhatDivision of FST, Sher e Kashmir University of Agricultural Sciences and Technology, Kashmir, 190025, J&k , India. tashu1746@gmail.com.ORCID https://orcid.org/0000-0002-7455-2401

Funding

This work was supported by the Science and Engineering Research Board, Department of Science and Technology (SERB-DST), India. The authors thank Dr. Knemali (Purdue University) and Dr. Maryna (Petiole App) for their assistance with data processing. The authors are grateful to Ongoing Research Funding Program (ORF-2026-376), King Saud University, Riyadh, Saudi Arabia.
6 · The paper itself

Abstract

Water scarcity necessitates efficient irrigation strategies in high-value crops like apple. While regulated deficit irrigation (RDI) is known to influence apple yield and quality, the optimal soil moisture threshold for automated systems in region-specific contexts remains poorly defined. This two-year study (2024-2025) evaluated the effects of four precise, soil moisture-based irrigation thresholds on the yield, fruit quality, and water productivity (WP) of 'Super Chief' apples in the northwestern Himalayas. An automated irrigation system using a Human-Machine Interface (HMI) and calibrated soil moisture sensors maintained Volumetric Water Content (VWC) at four thresholds: 100% (I100, 45% VWC), 80% (I80, 36% VWC), 60% (I60, 27% VWC), and 40% (I40, 18% VWC) of field capacity (θfc). Full irrigation (I100) maximized yield (70.80 t ha⁻¹) and fruit size (165.60 g). Severe deficit (I40) significantly enhanced fruit firmness (9.56 N) and soluble solids content (13.95 °Brix) but at the cost of a 20% yield reduction. Crucially, the I80 treatment achieved a near-optimal yield (66.24 t ha⁻¹) while using 20% less irrigation water than I100, resulting in a 31% higher water productivity (WP). Multi-criteria TOPSIS analysis objectively ranked I80 as the best compromise strategy. For 'Super Chief' apples in the Himalayan region, maintaining soil moisture at 80% θfc via sensor-based automation is the superior strategy, effectively balancing marketable yield, enhanced fruit quality, and water conservation.

Indexed as

Deficit irrigationMalus domesticaPrecision irrigationSoil moisture sensorsSustainable horticultureWater productivity

Identifiers

PMID42728253
PMCPMC13569487

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.