Improving nitrogen (N) management is critical to maximizing the productivity and sustainability of our agroecosystems. Improving N management requires an understanding of crop N status and yield potential early enough in the growing season when changes to N management can influence yields. However, given the lack of tools currently available to accurately assess crop N status, farmers continuously face the challenge of determining whether their crops require additional N fertilizer. The recent emergence of remote sensing technology has provided a promising alternative that can provide farmers the information they need in an accurate and timely manner. Several studies have demonstrated the potential of remote sensing technology to accurately assess the health and vigor of vegetation at the landscape scale, however few have explored how this technology can be utilized to inform sustainable crop management at the farm scale. This knowledge gap is what inspired this research and led us to investigate how remote sensing technology can be utilized to improve in-season N management in California rice systems.In California, where more than 200,000 ha of flooded rice (Oryza sativa) is cultivated annually, the recommended N management strategy is for farmers to apply the average seasonal N fertilizer requirement prior to flooding and planting as aqua-ammonia injected into the soil. On-farm studies have reported that N fertilizer applied in this manner is efficiently utilized by the crop as it remains protected from denitrification and ammonia volatilization losses until the crop needs it. At panicle initiation (PI), it is recommended to assess crop N status to determine if additional N fertilizer inputs are required as top-dress. The current tools available to assess rice N status include the SPAD chlorophyll meter and Leaf Color Chart, but these tools are not often used as they are time consuming and subjective. Thus, most top-dress N applications take place without evaluating crop N status; possibly resulting in inefficiencies due to over application. Our goal was to improve N management in California rice systems by developing a sensor-based decision support tool that could guide California rice farmers in their mid-season top-dress N management. This was pursued through N response trials that were established over a 4-yr. period across fourteen on-farm locations throughout the Sacramento Valley rice growing region of California. At PI, Normalized Difference Vegetation Index (NDVI) was measured using both a proximal crop sensor and a multispectral aerial sensor, and Normalized Difference Red-Edge Index (NDRE) was measured only using an aerial sensor. After NDVI and NDRE measurements, biomass was sampled destructively and then top-dress N fertilizer was applied. At maturity, rice plants were harvested to quantify grain yield. In the first chapter, our objective was to determine which N status parameter is best assessed by NDVI at PI and how accurately NDVI at PI can predict grain yield. The N status parameters quantified in this study were aboveground biomass, plant N concentration, and total N uptake. Quadratic linear regression models were developed to describe the relationship between each N status parameter and NDVI, and a simple linear regression model was developed to describe the relationship between grain yield and NDVI. Our results showed that PI N status was best assessed by NDVI when quantified as total N uptake and that NDVI at PI was positively correlated with grain yield. However, our results also showed that NDVI saturated once crop N uptake exceeded a certain threshold, suggesting alternative indices that do not saturate may provide a basis for a better assessment. In the second chapter, our objective was to compare the sensitivity of aerially sensed NDVI and NDRE to proximally sensed NDVI for assessing rice crop status when quantified as PI N uptake and grain yield. In order to make direct comparisons across the three indices, the raw values from each index were normalized by calculating the Sufficiency-Index (SI). Quadratic-plateau linear regression models were developed to describe the relationship between each SI and PI N uptake and linear mixed effects models were developed to describe the relationship between each SI and grain yield. Our results showed that aerial NDRE SI and proximal NDVI SI were similarly sensitive at assessing PI N uptake and grain yield, whereas aerial NDVI SI was poorly sensitive. The difference in sensitivity among the three indices was attributed to the relative amount of saturation of each index. Our finding that both the aerial NDRE SI and proximal NDVI SI measured PI rice crop status effectively provides a unique advantage for end-users as it allows them the flexibility to choose the sensor most suitable for their goals. In the final chapter, our objective was to develop a NDVI Response-Index capable of predicting the grain yield response to top-dress N fertilizer applied at PI. The NDVI Response-Index was developed by comparing the NDVI of each field treatment to the NDVI of a N non-limiting plot. At PI, top-dress N fertilizer was applied to every plot, and at maturity grain yield was quantified. A linear mixed effects model was developed to describe the relationship between NDVI Response-Index and grain yield with and without top-dress N. An economic analysis was performed to determine the magnitude of grain yield response required for top-dress N applications to be economically feasible. Based on our results, we found that top-dress N applications become profitable once NDVI Response-Index exceeds 1.07 by PI. The NDVI Response-Index presented here provides a useful tool for farmers to make precise mid-season top-dress N decisions which can result in positive outcomes for both crop productivity and environmental sustainability.

In California’s water-seeded rice systems, both high yields and nitrogen (N) use efficiency are achieved when the pre-plant N fertilizer (usually aqueous-ammonia; aqua-N) is injected into a dry seedbed before flooding and planting. However, there are situations where applying N fertilizer in this manner is not possible. The objective of this study was to evaluate N management practices by testing different N sources and application times. Over two growing seasons (three site-years) we evaluated different N sources including aqua-N, granular urea, ammonium sulfate, and three enhanced efficiency nitrogen fertilizers (EENFs). Application times for the EENFs, urea, and ammonium sulfate were one day after flooding and 2 weeks after flooding. Urea and ammonium sulfate were also applied in a 4-way split (ratio of 20:30:30:20 applied every two weeks). In 2021, additional treatments were added with aqua-N, ammonium sulfate, and urea applied before flooding; and urea applications at 3, 4, and 5 weeks after flooding. Grain yield and agronomic nitrogen use efficiency (ANUE) were measured and used to make comparisons. All fertilizer N treatments increased grain yields and ANUE relative to the zero-N control. The EENF treatments performed similarly to or worse than urea applied alone. Splitting urea applications was the best option for applying N fertilizer after the field was flooded. Only one split N treatment was evaluated in this study; this warrants further research on fine-tuning the best N splits for these systems.

To address the complex challenges facing agricultural sustainability, there is need for adaptive land management strategies that promote sustainable agricultural practices while adapting to changing environmental conditions. This research presents three possible approaches to managing agricultural sustainability at the landscape scale, utilizing the Sacramento Valley of California as a research template. This region was a native wetland habitat that was reclaimed for agricultural production and is now dominated by rice agriculture. The unique climate, topography, and soils of this region make it important habitat for endangered species as well. The three chapters of this dissertation discuss sustainability challenges and potential solutions for this research: (1) Methylmercury dynamics in the region’s water, plants and soil during a summer and winter season; (2) Crop rotation as a solution to managing limited water resources and weed pressure; (3) The drivers and consequences of converting rice fields into perennial tree crops. Each of these three sections is introduced in the following paragraphs.Methylmercury (MeHg) is an environmental toxin often produced in and exported from flooded soils. Little is known about MeHg in agricultural wetlands of the Sacramento Valley, where irrigation drain water flows into sensitive wetlands downstream. Soil, grain, and surface water (dissolved and particulate) MeHg and total mercury (THg) were monitored in six commercial rice fields across this region throughout a winter fallow season and subsequent growing season. Both dissolved and particulate mercury fractions were higher in fallow season rice field-water. Total suspended solids and particulate mercury concentrations were positively correlated (r = 0.99 and 0.98 for THg and MeHg, respectively), suggesting that soil MeHg was suspended in the water column and potentially exported. Filtered THg and MeHg concentrations were positively correlated with absorbance at 254 nm (r = 0.47 and 0.58, respectively) in fallow season field water. In the growing season, fields with higher irrigation water MeHg concentrations (due to recycled water use) also had elevated in-field MeHg (r = 0.86, p < 0.05) and grain MeHg concentrations (r = 0.96, p < 0.01). A mass balance analysis shows that soil mercury pools were orders of magnitude larger than surface water or grain mercury pools; however, fallow season drainage and grain harvest were the primary pathways for MeHg export. Based on these findings, efforts to reduce mercury exports in rice field drainage water should focus on reducing discharge turbidity and the build-up of labile carbon pools during the fallow season. Crop rotation is one strategy for adapting agroecosystems to a framework that balances ecological diversity, sustainability, and food production. The Sacramento Valley, one of the most productive rice growing regions in the US, faces sustainability challenges including increasing herbicide resistant weed pressure and water use restrictions. Increasing crop diversity may help address these challenges, but this region has unique soil attributes including high clay content, salinity, alkalinity, and cemented subsurface layers, and the degree to which these soil properties influence crop rotation decisions remain unclear. The objectives of this study were to quantify the extent of crop rotation in this region, compare soil properties for rotated and continuous rice fields, and assess the potential for expanding rotations based on the geographic coverage of influential soil variables. Using satellite derived land cover data for 2007-2021, our analysis shows that only ~5,000 ha are in rotation with rice, while 220,000 ha are in continuous rice production. This land cover information is fused with SSURGO soil maps in a spatial random forest model. The modeling approach indicates that fields with soil pH between 6.5 and 8, EC between 0.5 and 2 (ds m-1), and saturated hydraulic conductivity less than 2 (μm s-1) are more likely to be rotated. However, we estimate that only 11% of the continuous rice area has all three of these soil properties combined, suggesting soil limitations are an important constraint. To better understand barriers to agroecological diversification, this research highlights a method for evaluating land use decisions in relation to spatial variability of soil properties. Broader land use changes in California’s Central Valley over the past 20 years have been dominated by shifts from annual to periannual cropping systems. However, the Mediterranean climate of in this region is prone to drought conditions, which are increasing in frequency due to climate change. The northern portion of the Central Valley is dominated by commercial rice production. Periannual tree crops such as almond and walnut are increasing in this area, however large portions of the valley have unique wetland-basin soil attributes that make growing non-flooded crops difficult. We use a remote sensing land cover information and spatial soil information to quantify the land use changes in the region, and to better understand the effect of soil type. Our analysis shows that almond and walnut have increased in the area over the past 15 years, but that their area is limited (10% of total area). Crop prices, and revenue per hectare are 3-4 fold higher for almond and walnut compared to rice, which has incentivized some growers to plant these crops. However, our random forest model reveals that continuous rice fields and periannual fields have distinct soil types. Clay is the most important variable in the model, and fields with high clay (>40%) are unlikely to be planted in almond or walnut. Many of the fields with high clay are in the interior basins of the rice growing region, where there are no perennial tree crops. This research provides a framework for evaluating land use decision making to promote sustainable land management.

Flooded rice systems are critical for global food security but contribute significantly to anthropogenic greenhouse gas (GHG) emissions due to high methane (CH4) production in anerobic soils. Herbicide use in conventional rice systems has also created selection pressure for herbicide resistant aquatic weed species that threaten yields. Dry seeding (DS) rice, which in California includes early season drainage events, has been shown to reduce CH4 emissions and shift weed species emergence for improved control compared to continuously flooded water-seeded systems (WS). The effect of these drainage events on nitrogen (N) fertilizer losses and nitrous oxide (N2O) emissions, however, are not well understood. In a two-year study we quantified the effects of early season drainage events utilized in DS rice on global warming potential (GWP) (CH4 + N2O in CO2 eq.), nitrate (NO3-) accumulation, and N fertilizer losses as measured by the difference in crop N-uptake compared to a WS control. Despite 1.06 kg ha-1 more N2O emissions in the DS system the GWP was 4,610 CO2 eq. kg ha-1, a 42% reduction compared to 7,983 CO2 eq. kg ha-1 in the WS system. This was due to a 46% reduction in CH4 in the DS (126 CH4 kg ha-1) relative to the WS (235 CH4 kg ha-1) system. Nitrate accumulation in the DS system amounted to 25.9 kg N ha-1, and subsequent N losses via denitrification likely contributed to the 22.4 kg N ha-1 less crop N-uptake in the DS system. These results suggest that DS rice has potential for improved environmental impact via GWP reductions. Future research should consider the effects of increased pre-plant N application rates and timing for improved N management, a quantification of annual GWP including CO2 emissions, and changes in soil organic carbon stocks in DS rice.

Many California rice growers are now using foliar-active herbicides that require fields to be drained before application. Current regulations limit aerial herbicides and they must be applied by ground, requiring a soil surface dry enough to support application equipment. Our research showed that draining rice fields for a prolonged period early in the season led to a buildup of nitrate in the soil. About 60% of this nitrogen was lost when the field was reflooded, reducing nitrogen-use efficiency and uptake, and lowering grain yields. Nitrate-nitrogen accumulated at a rate of about 1.8 pounds per acre daily, and accumulation began about 4 days after the field was drained. During a typical drain of 10 to 14 days, about 20 pounds of nitrate-nitrogen per acre can be lost. Field experiments showed that incorporating fertilizer nitrogen into the subsurface soil increases nitrogen-use efficiency. Based on this research, we recommend that growers incorporate as much of their preplant nitrogen as possible below the soil surface and limit the drain period as much as possible.