A Remote Sensing Approach for Assessing Daily Cumulative Evapotranspiration Integral in Wheat Genotype Screening for Drought Adaptation.

This study considers critical aspects of water management and crop productivity in wheat cultivation, specifically examining the daily cumulative actual evapotranspiration (ETa). Traditionally, ETa surface energy balance models have provided estimates at discrete time points, lacking a holistic integrated approach. Field trials were conducted with 22 distinct wheat varieties, grown under both irrigated and rainfed conditions over a two-year span. Leaf area index prediction was enhanced through a robust multiple regression model, incorporating data acquired from an unmanned aerial vehicle using an RGB sensor, and resulting in a predictive model with an R2 value of 0.85. For estimation of the daily cumulative ETa integral, an integrated approach involving remote sensing and energy balance models was adopted. An examination of the relationships between crop yield and evapotranspiration (ETa), while considering factors like year, irrigation methods, and wheat cultivars, unveiled a pronounced positive asymptotic pattern. This suggests the presence of a threshold beyond which additional water application does not significantly enhance crop yield. However, a genetic analysis of the 22 wheat varieties showed no correlation between ETa and yield. This implies opportunities for selecting resource-efficient wheat varieties while minimizing water use. Significantly, substantial disparities in water productivity among the tested wheat varieties indicate the possibility of intentionally choosing lines that can optimize grain production while minimizing water usage within breeding programs. The results of this research lay the foundation for the development of resource-efficient agricultural practices and the cultivation of crop varieties finely attuned to water-scarce regions.

Improving in-season wheat yield prediction using remote sensing and additional agronomic traits as predictors.

The development of accurate grain yield (GY) multivariate models using normalized difference vegetation index (NDVI) assessments obtained from aerial vehicles and additional agronomic traits is a promising option to assist, or even substitute, laborious agronomic in-field evaluations for wheat variety trials. This study proposed improved GY prediction models for wheat experimental trials. Calibration models were developed using all possible combinations of aerial NDVI, plant height, phenology, and ear density from experimental trials of three crop seasons. First, models were developed using 20, 50 and 100 plots in training sets and GY predictions were only moderately improved by increasing the size of the training set. Then, the best models predicting GY were defined in terms of the lowest Bayesian information criterion (BIC) and the inclusion of days to heading, ear density or plant height together with NDVI in most cases were better (lower BIC) than NDVI alone. This was particularly evident when NDVI saturates (with yields above 8 t ha-1) with models including NDVI and days to heading providing a 50% increase in the prediction accuracy and a 10% decrease in the root mean square error. These results showed an improvement of NDVI prediction models by the addition of other agronomic traits. Moreover, NDVI and additional agronomic traits were unreliable predictors of grain yield in wheat landraces and conventional yield quantification methods must be used in this case. Saturation and underestimation of productivity may be explained by differences in other yield components that NDVI alone cannot detect (e.g. differences in grain size and number).

Performance of the Two-Source Energy Balance (TSEB) Model as a Tool for Monitoring the Response of Durum Wheat to Drought by High-Throughput Field Phenotyping.

The current lack of efficient methods for high throughput field phenotyping is a constraint on the goal of increasing durum wheat yields. This study illustrates a comprehensive methodology for phenotyping this crop's water use through the use of the two-source energy balance (TSEB) model employing very high resolution imagery. An unmanned aerial vehicle (UAV) equipped with multispectral and thermal cameras was used to phenotype 19 durum wheat cultivars grown under three contrasting irrigation treatments matching crop evapotranspiration levels (ETc): 100%ETc treatment meeting all crop water requirements (450 mm), 50%ETc treatment meeting half of them (285 mm), and a rainfed treatment (122 mm). Yield reductions of 18.3 and 48.0% were recorded in the 50%ETc and rainfed treatments, respectively, in comparison with the 100%ETc treatment. UAV flights were carried out during jointing (April 4th), anthesis (April 30th), and grain-filling (May 22nd). Remotely-sensed data were used to estimate: (1) plant height from a digital surface model (H, R 2 = 0.95, RMSE = 0.18m), (2) leaf area index from multispectral vegetation indices (LAI, R 2 = 0.78, RMSE = 0.63), and (3) actual evapotranspiration (ETa) and transpiration (T) through the TSEB model (R 2 = 0.50, RMSE = 0.24 mm/h). Compared with ground measurements, the four traits estimated at grain-filling provided a good prediction of days from sowing to heading (DH, r = 0.58-0.86), to anthesis (DA, r = 0.59-0.85) and to maturity (r = 0.67-0.95), grain-filling duration (GFD, r = 0.54-0.74), plant height (r = 0.62-0.69), number of grains per spike (NGS, r = 0.41-0.64), and thousand kernel weight (TKW, r = 0.37-0.42). The best trait to estimate yield, DH, DA, and GFD was ETa at anthesis or during grain filling. Better forecasts for yield-related traits were recorded in the irrigated treatments than in the rainfed one. These results show a promising perspective in the use of energy balance models for the phenotyping of large numbers of durum wheat genotypes under Mediterranean conditions.

The joint effect of health shocks and eligibility for social security on labor supply.

This paper investigates whether or not suffering a health shock, and becoming eligible for social security, have a joint effect on labor supply. Despite millions of people experiencing both of these events each year, no paper has focused exclusively on the joint effect that these events may have on work outcomes. This is surprising given that experiencing a health shock may impact on how a worker responds to becoming eligible for social security. With data from the Health and Retirement Study, I model weekly hours of work as a function of health shocks, social security eligibility, and their interaction. I find that this interaction leads to a 3-4 h reduction in weekly hours of work for men, but has no effect for women. The results are robust to using different work outcomes, age groups, health shock definitions, subgroups, as well as falsification and placebo tests. The results appear to be driven by men who would have had to return to work with impaired health. Policies that promote a more flexible work situation for older men may alleviate these problems in the future.

The effect of cancer on the labor supply of employed men over the age of 65.

This paper investigates the relationship between cancer diagnosis and the labor supply of employed men over the age of 65. While almost 60% of male cancers are diagnosed in men over the age of 65, no previous research has examined the effect that cancer has on this age group, which is surprising given the relevance of this group to public policy. With data from the Health and Retirement Study, I show that cancer has a significant negative effect on the labor supply of these workers. Using a combination of linear regression models and propensity score matching, I find that respondents who are diagnosed with cancer work 3 fewer hours per week than their non-cancer counterparts. They are also 10 percentage points more likely to stop working. This reduction seems to be driven by a deterioration in physical and mental health.

The effects of cancer on older workers in the English labour market.

The continued rise in overall cancer survival rates has ignited a field of research which examines the effect that cancer has on survivors' employment. Previous estimates of the effect of cancer on labour market outcomes, using U.S. data, show significant reductions in employment in the first 6 months after diagnosis. However, this impact has been found to dissipate after 12 and 18 months. I use data from the English Longitudinal Study of Ageing and find that, in England, where there is a more generous welfare support system, not only does cancer have a negative impact in the first 6-month period following diagnosis, but also in the second 6-month period. I estimate that, in the second 6-month period after diagnosis, respondents with cancer are 12.2% points less likely to work and work 4.2 fewer hours a week when compared to matched, healthy controls. This suggests that the negative effects from cancer can persist for longer than the six months identified previously. These results have implications for government policy and employers, because it increases the length of time that survivors may be on government supported sick pay, and the expected time that workers will be absent from work due to illness.

Census parcels cropping system classification from multitemporal remote imagery: a proposed universal methodology.

A procedure named CROPCLASS was developed to semi-automate census parcel crop assessment in any agricultural area using multitemporal remote images. For each area, CROPCLASS consists of a) a definition of census parcels through vector files in all of the images; b) the extraction of spectral bands (SB) and key vegetation index (VI) average values for each parcel and image; c) the conformation of a matrix data (MD) of the extracted information; d) the classification of MD decision trees (DT) and Structured Query Language (SQL) crop predictive model definition also based on preliminary land-use ground-truth work in a reduced number of parcels; and e) the implementation of predictive models to classify unidentified parcels land uses. The software named CROPCLASS-2.0 was developed to semi-automatically perform the described procedure in an economically feasible manner. The CROPCLASS methodology was validated using seven GeoEye-1 satellite images that were taken over the LaVentilla area (Southern Spain) from April to October 2010 at 3- to 4-week intervals. The studied region was visited every 3 weeks, identifying 12 crops and others land uses in 311 parcels. The DT training models for each cropping system were assessed at a 95% to 100% overall accuracy (OA) for each crop within its corresponding cropping systems. The DT training models that were used to directly identify the individual crops were assessed with 80.7% OA, with a user accuracy of approximately 80% or higher for most crops. Generally, the DT model accuracy was similar using the seven images that were taken at approximately one-month intervals or a set of three images that were taken during early spring, summer and autumn, or set of two images that were taken at about 2 to 3 months interval. The classification of the unidentified parcels for the individual crops was achieved with an OA of 79.5%.

Semi-automatic normalization of multitemporal remote images based on vegetative pseudo-invariant features.

A procedure to achieve the semi-automatic relative image normalization of multitemporal remote images of an agricultural scene called ARIN was developed using the following procedures: 1) defining the same parcel of selected vegetative pseudo-invariant features (VPIFs) in each multitemporal image; 2) extracting data concerning the VPIF spectral bands from each image; 3) calculating the correction factors (CFs) for each image band to fit each image band to the average value of the image series; and 4) obtaining the normalized images by linear transformation of each original image band through the corresponding CF. ARIN software was developed to semi-automatically perform the ARIN procedure. We have validated ARIN using seven GeoEye-1 satellite images taken over the same location in Southern Spain from early April to October 2010 at an interval of approximately 3 to 4 weeks. The following three VPIFs were chosen: citrus orchards (CIT), olive orchards (OLI) and poplar groves (POP). In the ARIN-normalized images, the range, standard deviation (s. d.) and root mean square error (RMSE) of the spectral bands and vegetation indices were considerably reduced compared to the original images, regardless of the VPIF or the combination of VPIFs selected for normalization, which demonstrates the method's efficacy. The correlation coefficients between the CFs among VPIFs for any spectral band (and all bands overall) were calculated to be at least 0.85 and were significant at P = 0.95, indicating that the normalization procedure was comparably performed regardless of the VPIF chosen. ARIN method was designed only for agricultural and forestry landscapes where VPIFs can be identified.

Modelling the cost-effectiveness of adjuvant lapatinib for early-stage breast cancer.

BACKGROUND: It has been shown in the NeoALTTO trial that a neoadjuvant regimen containing paclitaxel, lapatinib and trastuzumab is superior to regimens which include only one of the HER2 antagonists with paclitaxel. In light of these results, we modelled the potential cost-effectiveness of adjuvant lapatinib for patients with HER2-positive early-stage breast cancer. MATERIAL AND METHODS: We constructed a Markov state-transition model with three different health states: disease free, relapse, and death. We assumed an 18-week course of lapatinib was added to the TCH arm of the BCIRG 006 trial. Since no efficacy data are available for combining adjuvant lapatinib with trastuzumab, we ran the model assuming five different hypothetical hazard ratios for disease free survival when lapatinib is added to TCH (TCH was used as the control group). The hazard ratios were 0.9, 0.8, 0.7, 0.6, and 0.5. Outcomes are given in quality-adjusted life-years (QALYs). Both costs and QALYs were discounted at the 4% rate. We calculated the cost per QALY from the perspective of the Irish health care system. Probabilistic sensitivity analysis and one-way sensitivity were performed and confidence intervals were bootstrapped. RESULTS: The incremental cost-effectiveness ratios (ICERs) for the five hazard ratios are €53 089/QALY, €27 893/QALY, €18 463/QALY, €13 527/QALY and €10 490/QALY, respectively. Using the €45 000/QALY threshold, an adjuvant lapatinib regimen is cost-effective at the 0.8 hazard ratio. Adjuvant lapatinib becomes cost-effective at the 0.879 hazard ratio where the ICER is €44 825/QALY. CONCLUSION: In the Irish setting, an adjuvant lapatinib regimen would be considered cost-effective for patients with HER2-positive early-stage breast cancer for four of the five hypothesised hazard ratios. Data from both adjuvant and neoadjuvant trials suggest that the hazard ratio required to achieve cost-effectiveness for adjuvant lapatinib is both possible and plausible.