Gap-free 16-year (2005-2020) sub-diurnal surface meteorological observations across Florida.

The sub-tropical, flat, peninsular region of Florida is subject to a unique climate with extreme weather events that impact agriculture, public health, and management of natural resources. Meteorological data at high temporal resolutions especially in tropical latitudes are essential to understand diurnal and semi-diurnal variations of climate, which are considered as the fundamental modes of climate variations of our Earth system. However, many meteorological datasets contain gaps that limit their use for validation of models and further detailed observational analysis. The objective of this paper is to apply a set of data gap filling strategies to develop a gap-free dataset with 15-minute observations for the sub-tropical region of Florida. Using data from the Florida Automated Weather Network (FAWN), methods of linear interpolation, trend continuation, reference to external sources, and nearest station substitution were applied to fill the data gaps depending on the extent of the gap. The outcome of this study provides continuous, publicly accessible surface meteorological observations for 30 FAWN stations at 15-minute intervals for years 2005-2020.

A model of crop diversification under labor shocks.

As demands on agriculture increase, food producers will need to employ management strategies that not only increase yields but reduce environmental impacts. Modeling is a powerful tool for informing decision-making about current and future practices. We present a model to evaluate the effects of crop diversification on the robustness of simulated farms under labor shocks. We use an example inspired by the Florida production system of high-value, labor-intensive fruits. We find that crop diversification to high-value crops is a robust strategy when labor shocks are mild, and that crop diversification becomes less valuable as more simulated farms practice it. Based on our results, we suggest that crop diversification is a useful management strategy under specific conditions, but that policies designed to encourage crop diversification must consider broad effects as well as farm-level benefits.

Optimizing irrigation and nitrogen requirements for maize through empirical modeling in semi-arid environment.

Uncertainty in future availability of irrigation water and regulation of nutrient amount, management strategies for irrigation and nitrogen (N) are essential to maximize the crop productivity. To study the response of irrigation and N on water productivity and economic return of maize (Zea mays L.) grain yield, an experiment was conducted at Water Management Research Center, University of Agriculture Faisalabad, Pakistan in 2015 and 2016. Treatments included of full and three reduced levels of irrigation, with four rates of N fertilization. An empirical model was developed using observed grain yield for irrigation and N levels. Results from model and economic analysis showed that the N rates of 235, 229, 233, and 210 kg ha-1 were the most economical optimum N rates to achieve the economic yield of 9321, 8937, 5748, and 3493 kg ha-1 at 100%, 80%, 60%, and 40% irrigation levels, respectively. Economic optimum N rates were further explored to find out the optimum level of irrigation as a function of the total water applied using a quadratic equation. The results showed that 520 mm is the optimum level of irrigation for the entire growing season in 2015 and 2016. Results also revealed that yield is not significantly affected by reducing the irrigation from full irrigation to 80% of full irrigation. It is concluded from the study that the relationship between irrigation and N can be used for efficient management of irrigation and N and to reduce the losses of N to avoid the economic loss and environmental hazards. The empirical equation can help farmers to optimize irrigation and N to obtain maximum economic return in semi-arid regions with sandy loam soils.

Assessing the impact of climate variability on maize using simulation modeling under semi-arid environment of Punjab, Pakistan.

Climate change and variability are major threats to crop productivity. Crop models are being used worldwide for decision support system for crop management under changing climatic scenarios. Two-year field experiments were conducted at the Water Management Research Center (WMRC), University of Agriculture Faisalabad, Pakistan, to evaluate the application of CERES-Maize model for climate variability assessment under semi-arid environment. Experimental treatments included four sowing dates (27 January, 16 February, 8 March, and 28 March) with three maize hybrids (Pioneer-1543, Mosanto-DK6103, Syngenta-NK8711), adopted at farmer fields in the region. Model was calibrated with each hybrid independently using data of best sowing date (27 January) during the year 2015 and then evaluated with the data of 2016 and remaining sowing dates. Performance of model was evaluated by statistical indices. Model showed reliable information with phenological stages. Model predicted days to anthesis and maturity with lower RMSE (< 2 days) during both years. Model prediction for biological yield and grain yield were reasonably good with RMSE values of 963 and 451 kg ha-1, respectively. Model was further used to assess climate variability. Historical climate data (1980-2016) were used as input to simulate the yield for each year. Results showed that days to anthesis and maturity were negatively correlated with increase in temperature and coefficient of regression ranged from 0.63 to 0.85, while its values were 0.76 to 0.89 kg ha-1 for grain yield and biological yield, respectively. Sowing of maize hybrids (Pioneer-1543 and Mosanto-DK6103) can be recommended for the sowing on 17 January to 6 February at the farmer field for general cultivation in the region. Early sowing before 17 January should be avoided due to severe reduction in grain yield of all hybrids. A good calibrated CERES-Maize model can be used in decision-making for different management practices and assessment of climate variability in the region.