Introduction: One of the biggest problems in cotton farming in the Comarca Lagunera, Mexico, is the low efficiency of irrigation, since the large quantities of water used for this crop result in water shortages.
Objectives: To determine the response function of the cotton crop to different soil moisture contents, as well as the water use efficiency.
Methodology: Seven treatments were evaluated in the field: 40-40, 40-80, 60-60, 60-100, 80-40, 80-80 and 100-60 % of available moisture consumed (AMC) by the cotton plant at two phenological stages. Treatments were distributed in a randomized block design with four replications.
Results: The highest cotton yield (8.7 Mg∙ha^-1) was obtained with the treatment that developed under 63 and 62 % AMC at the first and second stages of development, respectively, by consuming 97 cm of water.
Limitation of the study: The models do not predict satisfactorily when extrapolating outside the range of moisture levels considered in the study.
Originality: No studies have been reported on extreme favorable and unfavorable conditions of soil moisture content in cotton to know its productive response.
Conclusions: The obtained model maximizes cotton production (8.74 Mg∙ha^-1) with a water deficit of 59 and 56 % AMC. The highest water productivity (0.945 kg∙m^-3) was obtained with 81 and 82 % AMC and 78 cm of water consumption.

Introduction:Chlorophyll in wheat leaves is linked to grain yield, and measuring its reflectance allows predicting crop yield.
Objective: To evaluate the potential of GreenSeeker (GS) and SPAD502+ sensors, and spectral images acquired with an infrared camera fitted on an unmanned aerial vehicle (UAV) to estimate grain yield in wheat.
Methodology: Variable nitrogen concentrations were applied, and readings were taken with SPAD502+ and direct GS on wheat during the thickened sheath and heading phenological stages. Simultaneously, aerial images were captured with a multispectral camera to determine vegetation indices.
Results: The comparison between measured and estimated yields with optical sensors shows that GS presented the best fit, with a coefficient of determination (R2) of 0.86 in thickened sheaths. The fit with the green normalized difference vegetation index (GNDVI) was better in heading (R2 = 0.89).
Limitations of the study: Sensors with higher spectral resolution and earlier crop stages should be evaluated to estimate yield earlier.
Originality: The study shows the potential of optical sensors to estimate yield, thereby overcoming the time and economic resource limitations entailed in conventional methods.
Conclusions: GNDVI and GS are reliable, fast and non-destructive options to forecast wheat grain yield; however, UAVs allow scaling up crop monitoring and reducing the time to obtain field information.