Life Sciences Commons^™

First Things First: Widespread Nutrient Deficiencies Limit Yields In Smallholder Oil Palm Fields, Hendra Sugianto, Juan Pablo Monzon, Iput Pradiko, Fatima A. Tenorio, Ya Li Lim, Christopher R. Donough, Sunawan, Suroso Rahutomo, Fahmuddin Agus, James Cock, Joni Amsar, Rana Farrasati, Ridho Iskandar, Juan I. Rattalino Edreira, Shofia Saleh, Heri Santoso, Antonius P. Tito, Nadib Ulfaria, Maja A. Slingerland, Patricio Grassini

Department of Agronomy and Horticulture: Faculty Publications

CONTEXT: Indonesia is the most important oil palm producing country. Nearly 40% of planted area is managed by smallholders, with yields well below the potential. Efforts to increase productivity have focused on the source of planting material, with little attention paid to plant nutrition.

OBJECTIVE: To assess the degree to which current productivity in smallholder oil palm fields is limited by nutrients in scenarios with distinct sources of planting material.

METHODS: We collected detailed data on leaf nutrient concentration from 30 fields to derive minimum sampling size needed to diagnose nutrient status. Subsequently, we collected data on …

Predicting Site‑Specific Economic Optimal Nitrogen Rate Using Machine Learning Methods And On‑Farm Precision Experimentation, Alfonso De Lara, Taro Mieno, Joe D. Luck, Laila A. Puntel

Department of Agronomy and Horticulture: Faculty Publications

Applying at the economic optimal nitrogen rate (EONR) has the potential to increase nitrogen (N) fertilization efficiency and profits while reducing negative environmental impacts. On-farm precision experimentation (OFPE) provides the opportunity to collect large amounts of data to estimate the EONR. Machine learning (ML) methods such as generalized additive models (GAM) and random forest (RF) are promising methods for estimating yields and EONR. Twenty OFPE N trials in wheat and barley were conducted and analyzed with soil, terrain and remote-sensed variables to address the following objectives: (1) to quantify the spatial variability of winter crops yield and the yield response …

An Innovative Approach To Improve Oil Production And Quality Of Mustard (Brassica Juncea L.) With Multi-Nutrient-Rich Polyhalite, Biswajit Pramanick, B.S. Mahapatra, Debarati Datta, Prithwiraj Dey, S.P. Singh, Ajay Kumar, Bappa Paramanik, Neeraj Awasthi

Department of Agronomy and Horticulture: Faculty Publications

Polyhalite popularly known as POLY4 is a multi-nutrient fertiliser containing K, S, Mg, Ca, and micronutrients. POLY4 has a low carbon footprint, is certified for organic agriculture, and has the potential to improve crop productivity and quality attributes Indian mustard which often faces challenges due to imbalanced nutrition supplied in the current fertilisation schedule. The hypothesis of the study was that the multi-nutrient fartiliser POLY4 can ensure balanced nutrition for Indian mustard. Considering this, a field experiment was conducted during the winter seasons of 2017–18 and 2018–19 to evaluate the effect of POLY4 on Indian mustard (Brassica juncea L.) …