Integración de herramientas agronómicas y tecnológicas en la detección y manejo de malas hierbas
Programa Estatal de Investigación, Desarrollo e Innovación Orientada a los Retos de la Sociedad. Agencia Estatal de Investigación. REF; PID2020-113229RB-C42 (2021 - 2024)
The development of new technologies, such as remote sensing or expert decision support systems (DSS), are fundamental for the digitalization of integrated weed management. Their implementation for several crops, different weed species, new invasive/emergent species or herbicide resistant cases, as well as for incorporating new weed control methods, are crucial in decision making regarding alternative management strategies or the proper timing. In this respect, two opportunities arise: 1) to add new information, along with the already available, to the expert DSS IPMwise for the crop maize, or adapt it to a new one, vineyard; and 2) develop new remote sensing tools for weeds in these two cropping systems. These two opportunities establish the two main objectives of the current research project to address, in both crops: a) new herbicide resistant cases, b) widen their weed spectra to new invasive/threating species in maize
(Amaranthus palmeri) and to the main weeds present in vineyard, c) weed remote sensing using Unmanned Aerial Vehicles (UAV), and d) include efficacy data of non-chemical weed control strategies. In this project will participate researchers from the UdL and Agrotecnio. This double objective is divided in several sub-objectives for maize and vineyard. For the crop maize, it is aimed to: i) incorporate dose response curves of different herbicide active ingredients into the IPMwise DDS for sensitive and resistant A. palmeri biotypes; ii) locate the presence of A. palmeri in maize fields and validate the information obtained by UAV; iii) analyse with molecular markers of the relationship between mapped A. palmeri populations and their herbicide resistance mechanisms; and iv) validate weed detection maps and site specific treatments of noxious weeds in maize fields. For the vineyard, it is aimed to: i) obtain dose response curves of different herbicide active ingredients, both synthetic and natural, in the control of Conyza bonariensis and their implementation in the IPMwise DSS; ii) generate infestation maps and site specific treatments of C. bonariensis in vineyards using UAV; iii) estimate the effect of different organic mulches, under the row vineyard, as an alternative to mechanical control methods (in-row tiller and mower); and iv) evaluate the effect of different cover-crops and roller crimper on dynamics of Cynodon dactylon (and other weeds) in organic vineyard, and detect and map weed infestations. To reach these objectives, dose-response trials in greenhouse are planned, both for synthetic and natural herbicides. Also, field efficacy trials are scheduled for diverse weed control methods: herbicides with precision sprayer in maize, and organic mulches (row) or cover crops (inter-row), in vineyard. Weed infestations maps will be generated for site specific treatments (chemical or non-chemical), which will be validated with terrestrial detection methods or crop yield maps. Finally, the relationship of A. palmeri populations mapped and their mechanisms of resistance to herbicides will be studied using molecular markers in laboratory experiments.